U.S. patent application number 14/684011 was filed with the patent office on 2015-10-08 for system and method for detecting rock fall.
The applicant listed for this patent is Weir-Jones Engineering Consultants Ltd.. Invention is credited to Bohdan NEDILKO, Iain WEIR-JONES.
Application Number | 20150285927 14/684011 |
Document ID | / |
Family ID | 41506615 |
Filed Date | 2015-10-08 |
United States Patent
Application |
20150285927 |
Kind Code |
A1 |
NEDILKO; Bohdan ; et
al. |
October 8, 2015 |
SYSTEM AND METHOD FOR DETECTING ROCK FALL
Abstract
Aspects of the invention provide systems and methods for using
ballast sensors to detect rock fall events in a vicinity of railway
tracks or similar roadways or tracks. The ballast sensors are
spaced apart from the tracks. Particular embodiments permit the use
of signals from the ballast sensors to discriminate rock fall
events from other types of events and to detect the hypocenter of a
rock fall event.
Inventors: |
NEDILKO; Bohdan; (Burnaby,
CA) ; WEIR-JONES; Iain; (Vancouver, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Weir-Jones Engineering Consultants Ltd. |
Vancouver |
|
CA |
|
|
Family ID: |
41506615 |
Appl. No.: |
14/684011 |
Filed: |
April 10, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12972334 |
Dec 17, 2010 |
9031791 |
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14684011 |
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PCT/CA2009/000837 |
Jun 17, 2009 |
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12972334 |
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61073358 |
Jun 17, 2008 |
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Current U.S.
Class: |
702/2 ;
73/645 |
Current CPC
Class: |
B61L 23/041 20130101;
B61K 9/08 20130101; G01N 29/44 20130101; G01V 1/001 20130101 |
International
Class: |
G01V 1/00 20060101
G01V001/00; B61L 23/04 20060101 B61L023/04; G01N 29/44 20060101
G01N029/44 |
Claims
1. A system for detection of rock fall in a vicinity of a section
of railway track, the system comprising: a plurality of ballast
sensors spaced apart along the track section, each ballast sensor
located in a ballast proximate to the track section but spaced
apart from rails and ties associated with the track section and
each ballast sensor sensitive to acoustic energy and configured to
generate a corresponding ballast sensor signal in response to
detecting acoustic energy; a signal processing unit operatively
connected to receive the ballast sensor signals from the plurality
of ballast sensors, the signal processing unit configured to detect
rock fall events in a vicinity of the track section based, at least
in part, on the ballast sensor signals.
2. A system according to claim 1 wherein the signal processing unit
is configured to detect a plurality of different types of events
comprising rock fall events, train events wherein a train travels
over the track section and highrail vehicle events wherein a
highrail vehicle travels over the track section and wherein the
signal processing unit is configured to discriminate rock fall
events from train events or highrail vehicle events.
3. A system according to claim 2 wherein the signal processing unit
is configured to detect an event for a particular one of the
ballast sensors based, at least in part, on its corresponding
ballast sensor signal.
4. A system according to claim 3 wherein the signal processing unit
is configured to detect a start of the event and an associated time
t.sub.start, for the particular one of the ballast sensors, when a
STA/LTA parameter associated with the corresponding ballast sensor
signal is greater than a start trigger threshold
(thresh_start).
5. A system according to claim wherein the signal processing unit
is configured to detect an end of the event and an associated time
t.sub.end, for the particular one of the ballast sensors, when the
STA/LTA parameter associated with the corresponding ballast sensor
signal is less than an end trigger threshold (thresh_end).
6. A system according to claim 4 wherein the signal processing unit
is configured to determine the STA/LTA parameter for the
corresponding ballast sensor signal according to one of: ( i ) (
STA LTA ) n = i = ( n - ( a - 1 ) ) i = n x i a i = ( n - ( b - 1 )
) i = n x i b where b > a > 0 and n .gtoreq. a , b
##EQU00019## where: x.sub.i represents a value of an i.sup.th
sample of the corresponding ballast sensor signal, n is an index of
a current sample x.sub.n, a is an STA duration constant, b is an
LTA duration constant and ( STA LTA ) n ##EQU00020## is the STA/LTA
parameter; and ( ii ) ( STA LTA ) mod , n = i = ( n - ( a - 1 ) ) i
= n x i a c where n > a > 0 ##EQU00021## where: x.sub.i
represents a value of an i.sup.th sample of the corresponding
ballast sensor signal, n is an index of a current sample x.sub.n, a
is an STA duration constant, c is an experimentally determined
constant that is representative of an LTA during event free times
and ( STA LTA ) mod , n ##EQU00022## is the STA/LTA parameter.
7. A system according to claim 3 wherein the signal processing unit
is configured to detect a start of the event and an associated time
t.sub.start, for the particular one of the ballast sensors, when an
energy parameter associated with the corresponding ballast sensor
signal is greater than a start trigger threshold (E_thresh_start),
the energy parameter comprising a windowed average of a squared
amplitude of the corresponding ballast sensor signal.
8. A system according to claim 7 wherein the signal processing unit
is configured to detect an end of the event and an associated time
t.sub.end, for the particular one of the ballast sensors, when the
energy parameter associated with the corresponding ballast sensor
signal is less than an end trigger threshold (E_thresh_end).
9. A system according to claim 7 wherein the signal processing unit
is configured to determine the energy parameter for the
corresponding ballast sensor signal according to: E n = i = n - ( d
- 1 ) n ( x i ) 2 d ##EQU00023## where: x.sub.i represents a value
of an i.sup.th sample of the corresponding ballast sensor signal, n
is an index of a current sample x.sub.n, d is a window duration
constant and E.sub.n is the energy parameter.
10. A system according to claim 3 wherein the signal processing
unit is configured to determine a duration t.sub.dur associated
with the event for the particular one of the ballast sensors based,
at least in part, on its corresponding ballast sensor signal.
11. A system according to claim 10 wherein the signal processing
unit is configured to compare the event duration t.sub.dur with one
or more duration criteria and to determine on the basis of this
comparison that the event is not a rock fall event.
12. A system according to claim 11 wherein the signal processing
unit is configured to determine that the event is a train event or
a highrail vehicle event based at least in part on the comparison
of the event duration t.sub.dur with the one or more duration
criteria.
13. A system according to claim 12 wherein the signal processing
unit is configured to determine, for the particular one of the
ballast sensors, a PPV parameter which represents a magnitude of a
sample of the corresponding ballast sensor signal with the largest
absolute value during the event and wherein the signal processing
unit is configured to compare the PPV parameter with one or more
magnitude criteria and to determine on the basis of this comparison
whether the event is a train event or a highrail vehicle event.
14. A system according to claim 3 wherein the signal processing
unit is configured to determine a spectral power distribution
associated with the event for the particular one of the ballast
sensors based, at least in part, on its corresponding ballast
sensor signal.
15. A system according to claim 14 wherein the signal processing
unit is configured to compare the spectral power distribution with
one or more spectral criteria and to determine on the basis of this
comparison that the event is not a rock fall event.
16. A system according to claim 15 wherein the one or more spectral
criteria comprise a frequency threshold (thresh_freq) and the
signal processing unit is configured to determine that the event is
a train event or a highrail vehicle event when the spectral power
distribution comprises more than a particular percentage of its
power at frequencies above the frequency threshold
(thresh_freq).
17. A system according to claim 16 wherein the signal processing
unit is configured to determine, for the particular one of the
ballast sensors, a PPV parameter which represents a magnitude of a
sample of the corresponding ballast sensor signal with the largest
absolute value during the event and the signal processing unit is
configured to compare the PPV parameter with one or more magnitude
criteria and to determine on the basis of this comparison whether
the event is a train event or a highrail vehicle event.
18. A system according to claim 3 comprising one or more rail
sensors, each rail sensor operatively contacting the rails or the
ties associated with the track section and each rail sensor
sensitive to acoustic energy and configured to generate a
corresponding rail sensor signal in response to detecting acoustic
energy and wherein the signal processing unit is operatively
connected to receive the rail sensor signal.
19. A system according to claim 18 wherein the signal processing
unit is configured to determine a rail sensor spectral power
distribution associated with the event for a particular one of the
rail sensors based, at least in part, on its corresponding rail
sensor signal and configured to compare the rail sensor spectral
power distribution with one or more spectral criteria and to
determine on the basis of this comparison that the event is not a
rock fall event.
20. A system according to claim 3 wherein the signal processing
unit is configured to determine a PPV parameter associated with the
event for the particular one of the ballast sensors and its
corresponding ballast sensor signal, the PPV parameter representing
a magnitude of a sample of the corresponding ballast sensor signal
with the largest absolute value during the event.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 12/972,334 filed 17 Dec. 2010 entitled SYSTEM AND METHOD FOR
DETECTING ROCK FALL which is a continuation in part of Patent
Cooperation Treaty application No. PCT/CA2009/000837 filed 17 Jun.
2009, published under WO2010/003220 and entitled SYSTEM AND METHOD
FOR DETECTING ROCK FALL. This application also claims the benefit
of the priority of U.S. application No. 61/073,358 filed on 17 Jun.
2008 and entitled SEISMIC ROCK FALL DETECTION SYSTEM.
TECHNICAL FIELD
[0002] This invention relates to detection of rock fall events.
Particular embodiments provide systems and methods for rock fall
detection.
BACKGROUND
[0003] Rock fall events and other similar events (e.g. avalanches
and washouts) which take place in a vicinity of railway tracks can
damage the track, can damage passing trains and, in some cases, can
derail passing trains which can in turn cause significant damage to
the train and to people and/or property being transported by the
train. Damaged trains can cause corresponding damage to the
environment. Similar events which take place in a vicinity of other
transport-ways (e.g. roadways, bridges, subway tracks and the like)
can cause similar damage.
[0004] Prior art technology for detecting rock fall in a vicinity
of railway tracks involves so called "slide fences." Slide fences
incorporate current carrying wires which extend between fence posts
alongside the railway track. Falling rock may strike and break one
or more of these wires, opening the corresponding circuits and
preventing current flow therethrough. This change of current flow
may be detected to generate a rock fall indicator. Slide fences are
unreliable, because falling rock may not strike or break a wire,
but may still represent a danger to a passing train. Slide fences
also tend to generate false positive results, for example, when the
wire are broken by animals or the like. Additionally, if a slide
fence triggers (i.e. a wire is broken), then the slide fence must
be repaired (i.e. the broken wire must be replaced) and rail
traffic may be delayed until the slide fence is repaired.
[0005] There is a general desire for systems and methods of rock
fall detection that overcome or ameliorate these and/or other
deficiencies with the prior art.
SUMMARY OF THE INVENTION
[0006] One aspect of the invention provides a system for detection
of rock fall in a vicinity of a section of railway track. The
system comprises: a plurality of ballast sensors spaced apart along
the track section, each ballast sensor located in a ballast
proximate to the track section but spaced apart from rails and ties
associated with the track section and each ballast sensor sensitive
to acoustic energy and configured to generate a corresponding
ballast sensor signal in response to detecting acoustic energy; and
a signal processing unit operatively connected to receive the
ballast sensor signals from the plurality of ballast sensors, the
signal processing unit configured to detect rock fall events in a
vicinity of the track section based, at least in part, on the
ballast sensor signals.
[0007] Another aspect of the invention provides a method for
detection of rock fall in a vicinity of a section of railway track.
The method involves: providing a plurality of ballast sensors
spaced apart along the track section and locating each ballast
sensor in a ballast proximate to the track section but spaced apart
from rails and ties associated with the track section, each ballast
sensor sensitive to acoustic energy and configured to generate a
corresponding ballast sensor signal in response to detecting
acoustic energy; receiving the ballast sensor signals from the
plurality of ballast sensors; and processing the ballast sensor
signals to detect rock fall events in a vicinity of the track
section based, at least in part, on the ballast sensor signals.
[0008] Other aspects of the invention provide computer program
products comprising computer instructions which, when executed by a
processor, cause the processor to carry out the methods of the
invention.
[0009] Other features and aspects of specific embodiments of the
invention are described below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] In drawings which depict non-limiting embodiments of the
invention:
[0011] FIG. 1 is a schematic depiction of a rock fall detection
system according to a particular embodiment configured to detect
rock fall in a vicinity of a section of railway track;
[0012] FIG. 2A shows a sensor array according to a particular
embodiment which is suitable for use with the FIG. 1 rock fall
detection system and which incorporates a ballast sensor;
[0013] FIG. 2B shows a rail sensor which may be incorporated into
any one or more of sensor arrays of the FIG. 1 rock fall detection
system;
[0014] FIG. 3 is a schematic illustration of a signal processing
unit according to a particular embodiment which is suitable for use
with the FIG. 1 rock fall detection system;
[0015] FIG. 4A is a plot showing digitized and time stamped sensor
data typical for a small rock fall event obtained at the FIG. 3
data processor for a number of sensors;
[0016] FIG. 4B is a plot showing digitized and time-stamped sensor
data typical for a passing train obtained at the FIG. 3 data
processor for a number of sensors;
[0017] FIG. 4C is a plot showing digitized and time-stamped sensor
data typical for a passing highrail vehicle obtained at the FIG. 3
data processor for a number of sensors;
[0018] FIG. 4D is a plot showing digitized and time-stamped sensor
data typical for the activation of an on-sited power generator
obtained at the FIG. 3 data processor for a number of sensors;
[0019] FIG. 4E is a plot showing digitized and time-stamped sensor
data typical for a significant rock fall event obtained at the FIG.
3 data processor for a number of sensors;
[0020] FIG. 5 schematically illustrates a number of processing
parameters which may be determined from the sensor data;
[0021] FIGS. 6A and 6B respectively depict typical digitized,
time-stamped sensor data for a rock fall event and the
corresponding STA/LTA ratio;
[0022] FIGS. 6C and 6D respectively show a 0.4 second segment of
time stamped, digital sensor data and its corresponding FFT
associated with a typical rock fall event;
[0023] FIGS. 6E and 6F respectively show a 0.4 second segment of
time stamped, digital sensor data and its corresponding FFT
associated with a typical train event;
[0024] FIGS. 6G and 6H respectively show a 1.0 second segment of
time stamped, digital sensor data and its corresponding FFT
associated with a typical highrail vehicle event;
[0025] FIGS. 6I and 6J respectively show an 11 second segment of
time stamped, digitized sensor data and its corresponding FFT
associated with the operation of a generator in a vicinity of the
FIG. 1 track section;
[0026] FIG. 7A schematically depicts a method for event detection
method according to a particular embodiment;
[0027] FIG. 7B schematically depicts a method for post event
processing which may be performed as a part of the FIG. 7A event
detection method according to a particular embodiment;
[0028] FIG. 7C schematically depicts a method for estimating a
location of a rock fall event which may be performed as a part of
the FIG. 7A event detection method according to a particular
embodiment;
[0029] FIG. 7D schematically depicts a method for taking
appropriate action in respect of a rock fall event which may be
performed as a part of the FIG. 7A event detection method according
to a particular embodiment;
[0030] FIG. 8 is a schematic depiction of the triggered state of a
number of sensors in the FIG. 1 rock fall detection system in
response to a passing train;
[0031] FIG. 9A shows a typical response of a number of sensors of
the FIG. 1 rock fall detection system to a rock fall event;
[0032] FIG. 9B is a schematic depiction of the triggered state of
the FIG. 9A sensors;
[0033] FIG. 10 schematically illustrates a method for deployment of
the FIG. 1 system according to an example embodiment;
[0034] FIG. 11 schematically illustrates a method for
discriminating a series of events that may be caused by a human or
other animal according to a particular embodiment;
[0035] FIG. 12A schematically depicts a typical signal associated
with the passage of a typical train car over a magnetic wheel
detector;
[0036] FIG. 12B schematically depicts a typical signal associated
with the passage of a typical multi-car train over a magnetic wheel
detector;
[0037] FIG. 12C schematically depicts the extraction of temporal
differences between corresponding features of the signals
associated with a pair of wheel detectors; and
[0038] FIG. 13 exhibits a typical cross-correlation waveform
associated with signals from a pair of spaced apart ballast sensors
when a train is moving (or has moved) over the FIG. 1 track section
at a relatively constant speed.
DETAILED DESCRIPTION
[0039] Throughout the following description, specific details are
set forth in order to provide a more thorough understanding of the
invention. However, the invention may be practiced without these
particulars. In other instances, well known elements have not been
shown or described in detail to avoid unnecessarily obscuring the
invention. Accordingly, the specification and drawings are to be
regarded in an illustrative, rather than a restrictive, sense.
[0040] Aspects of the invention provide systems and methods for
using ballast sensors to detect rock fall events in a vicinity of
railway tracks or similar tracks. The ballast sensors are spaced
apart from the tracks. Particular embodiments permit the use of
signals from the ballast sensors to discriminate rock fall events
from other types of events and to detect the hypocenter of a rock
fall event.
[0041] FIG. 1 is a schematic depiction of a rock fall detection
system 10 according to a particular embodiment configured to detect
rock fall in a vicinity of a section of railway track 12. Track
section 12 may typically be located in a sloped region which may
present a risk of rock fall from the up slope 14 toward the
downslope 16. This is not necessary. Track section 12 may be
located in a valley and may have upward slopes on both sides
thereof. In some embodiments, the length of track section 12 may be
in a range of 100 m-5 km. In other embodiments, track section 12
may have other lengths. To facilitate this description, a number of
direction conventions are used. As shown by the schematic axes
shown in FIG. 1, the z direction refers to the vertical direction
(i.e. the direction of gravity), the y direction is oriented along
track section 12 and the x direction refers to the direction that
crosses track section 12.
[0042] Rock fall system 10 comprises a plurality of sensor arrays
18 disposed along track section 12. As discussed in more detail
below, sensor arrays 18 comprise one or more sensors for detecting
acoustic and/or vibrational energy. In the illustrated embodiment,
there are n sensor arrays 18 corresponding to track section 12. In
general, the number n may be any suitable number that provides the
functionality described below and may depend on the geotechnical
characteristics of the substrate in a vicinity of track section
12.
[0043] Sensor arrays 18 are spaced apart from one another by
distances 20 in y-direction. In some embodiments, distances 20 are
in a range of 5-100 m. In other embodiments, this range is 10-50 m.
In still other embodiments, this range is 10-30 m. Distances 20 may
be based on a number of factors, including, by way of non-limiting
example: characteristics of sensors used in sensor arrays 18 (e.g.
types of sensors, signal to noise ratio, etc.), geotechnical
characteristics (e.g. quality factor of geologic substrate),
performance requirements (e.g. magnitude of rock fall which it is
desired for system 10 to detect) and/or other factors (e.g. local
weather patterns, local natural and/or man-made sources of noise).
Distances 20 may be uniform within system 10, but this is not
necessary. In general, distances 20 may differ between each
adjacent pair of sensor arrays 18.
[0044] Each of sensor arrays 18 generates one or more corresponding
sensor signals 22. In the illustrated embodiment sensor signals 22
are analog signals, but this is not necessary. In some embodiments,
sensor arrays 18 may output digital sensor signals. Sensor signals
22 are transmitted along transmission lines 24 to central signal
processing unit 26. Transmission lines, 24 may run through
protective conduits (not shown in FIG. 1), such as pipes made of
suitable metals, plastics, fiber or the like. Transmission lines 24
may be electrically shielded to prevent electrical interference
from external sources and/or to prevent cross-talk between signals
22. The schematic illustration of FIG. 1 shows a single signal 22
and a single transmission line 24 for each sensor array 18. This is
not necessary. In general, sensor arrays 18 may comprise multiple
sensors that generate a corresponding plurality of signals 22 which
in turn may be transmitted to signal processing unit 26 on a
corresponding plurality of transmission lines 24. It will be
appreciated by those skilled in the art that signals 22 from sensor
arrays 18 may be multiplexed on transmission lines 24 if
desired.
[0045] In the illustrated embodiment, system 10 comprises one or
more optional image capturing devices 34. Image capturing devices
34 may comprise closed circuit television cameras, for example. In
some embodiments, image capturing devices 34 capture digital images
and/or digital video. Image capturing devices 34 may be controlled
by signal processing unit 26 using signals 38 which are delivered
to image capturing devices along transmission lines 40. Image data
36 captured by image capturing devices 34 may be transmitted to
signal processing unit 26 along the same transmission lines 40.
Transmission lines 40 may represent more than one actual line. In
some embodiments, transmission lines 40 are not required and camera
control signals 38 may be wirelessly transmitted from signal
processor unit 26 to image capturing devices 34 and image data 36
may be wirelessly transmitted from image capturing devices 34 back
to signal processing unit 26.
[0046] Signal processing unit 26 may be housed in a suitably
protective enclosure (not shown)--e.g. a small building or the
like. At signal processing unit 26, sensor signals 22 are digitized
and processed to detect rock fall events. Processing signals 22 to
detect rock fall events, which is described in more detail below,
may involve discriminating rock fall events from other events. By
way of non-limiting example, such other events may include passing
trains, passing highrail vehicles (e.g. trucks that travel on track
section 12), other natural noise sources (e.g. waterfalls, falling
trees or animals) and/or other man-made noise sources (e.g. power
generators or pedestrians).
[0047] System 10 may optionally include a network connection 28 to
a remote workstation 30. Network connection 28 may be a wire
network connection, a wireless network connection and/or a fiber
optic network connection, for example. In some embodiments, remote
workstation 30 may be connected to system 10 via network connection
28 to perform a number of functions, which may include (by way of
non-limiting example): monitoring the status of system 10, logging
or storing data captured by system 10, recalibrating or
reconfiguring system 10, updating software used by system 10 or the
like. In some embodiments, some or all of the data captured by
sensor arrays 18 may be transmitted via network connection 28 to
remote workstation 30 and such data may be processed at the remote
workstation 30 to detect rock fall events in a similar manner that
rock fall events are detected by signal processing unit 26, as
described in more detail below.
[0048] System 10 may be a modular part of a greater system (not
shown) which incorporates other systems 32 similar to system 10.
For example, signal processing unit 26 may be optionally linked
(via network connection 28 or via some other network connection) to
similar signal processing units for other systems 32 similar to
system 10.
[0049] In the illustrated embodiment of FIG. 1, sensor arrays 18
are located on uphill side 14 of track section 12. This is not
necessary. In some embodiments, sensor arrays 18 may be
additionally or alternatively located on downhill side 16 of track
section 12. In some embodiments, a single sensor array 18 may
comprise a plurality of acoustic or vibrational energy sensors,
some of which may be located on uphill side 14 and some of which
may be located on downhill side 16.
[0050] Sensor arrays 18 may each comprise one or more acoustic
energy sensors. By way of non-limiting example, suitable acoustic
energy sensors may include: electromagnetic induction based sensors
(which may be referred to as geophones), accelerometers,
piezoelectric sensors, electroactive polymer based sensors, optical
sensors, capacitive sensors, micromachined sensors or the like. As
is known in the art, some acoustic energy sensors may be
directional--e.g. some acoustic sensors may have one or more axes
on which they are more sensitive to acoustic energy. In some
embodiments, the output of these acoustic energy sensors may be
generally correlated with (e.g. proportional to) the sensed
acoustic energy. In other embodiments, the output of these acoustic
energy sensors may be generally correlated with (e.g. proportional
to) other parameters, such as displacement, velocity or
acceleration of a sensor component.
[0051] FIG. 2A illustrates a sensor array 18 according to a
particular embodiment which is suitable for use with rock fall
detection system 10. In the FIG. 2A embodiment, sensor array 18
comprises a single, uni-axial, electromagnetic induction-type
sensor 50 which is located on uphill side 14 of track section 12.
Sensor 50 is located in the ballast 52 which supports track section
12 and is spaced apart from track section 12--i.e. sensor 50 is not
in direct contact with tracks 54 or ties 56. In this description,
this type of sensor 50 (which is located at least in part in
ballast 52 of track section 12 and is spaced apart from track
section 12) may be referred to as a ballast sensor. Sensor 50 may
be encased in a protective housing 58, which (in the illustrated
embodiment) comprises a grout-filled enclosure which may be made
from a suitable material such as suitable plastic, fiber, steel or
the like.
[0052] Protective housing 58 (and sensor 50) may be located in a
trench 60 which is excavated in ballast 52 alongside track section
12. In the illustrated embodiment, a region 62 surrounding housing
58 is filled with compacted sand, which may improve acoustic
conduction and/or protect sensor 50 and transmission line 24 from
sharp rocks which may be present in ballast 52, and a remaining
region 64 of trench 60 is back-filled with ballast 52. In the
illustrated embodiment sensor 50 is coupled to an anchoring stake
66 which may be driven into the substrate below ballast 52 and/or
below sand-filled region 62. Stake 66 may be situated, shaped
and/or otherwise configured to provide good acoustic coupling to
the geologic substrate in a region of track section 12.
[0053] As mentioned above, sensor 50 of the FIG. 2A embodiment is a
uni-axial sensor. The sensitivity axis of sensor 50 is the z axis
and sensor 50 generates a single corresponding signal 22. In one
particular embodiment, signal 22 is generally correlated with (e.g.
proportional to) a sensed velocity of a component of sensor 50.
However, as discussed above, in other embodiments, signal 22 may be
generally correlated with (e.g. proportional to) other parameters,
such sensed displacement, acceleration or energy of corresponding
sensor components. The inventors have determined that uni-axial (z
axis) sensors are sufficient for the purposes of detecting rock
fall on suitably steep slopes. It will be appreciated that
uni-axial sensors are less costly than multi-axial sensors. In some
environments or in some applications, however, it may be desirable
to incorporate multi-axial sensors. Accordingly, in some
embodiments, sensor 50 may be multi-axial or sensor array 18 may
comprise a plurality of uni-axial sensors oriented in different
directions. In such embodiments, the number of signals 22 generated
by a multi-axial sensor may correspond to its number of axes or the
number of signals 22 generated by a plurality of uni-axial sensors
may correspond to the number of uni-axial sensors.
[0054] As discussed above, sensor 50 is electronically connected to
transmission line 24 for transmission of a corresponding sensor
signal 22 to signal processing unit 26. As shown in FIG. 2A,
transmission line 24 may run through a suitable protective conduit
68, which may be made from a suitable material such as suitable
plastic, fiber, steel or the like. In some embodiments, conduit 68
may also house cables 70 (e.g. electrical and/or optical cables)
which form part of network connection 28 between system 10 and
remote workstation 30 and/or other systems 32 (see FIG. 1) and/or
transmission lines 40 associated with optional image capturing
devices 34.
[0055] FIG. 2B illustrates a sensor 80 which may be incorporated
into any one or more of sensor arrays 18. In FIG. 2B embodiment,
sensor 80 is similar in many respects to sensor 50 (FIG. 2A) in
that sensor 80 is a uni-axial, electromagnetic induction-type
sensor. Sensor 80 differs from sensor 50 in that sensor 80 is
mounted (via suitable mounting hardware 82) to track 54 as opposed
to being a ballast sensor which is spaced apart from track 54.
Sensors which are mounted to track section 12 (including track(s)
54 and/or ties 56) may be referred to in this description as rail
sensors. In other respects, sensor 80 may be similar to sensor 50
described above.
[0056] Experiments have determined that rail sensors may be more
sensitive to direct contact between falling rocks and track section
12 (e.g. track(s) 54 and/or ties 56) and may be more sensitive to
passing trains or highrail vehicles. In some embodiments,
therefore, it is desirable to include one or more rail sensors.
However, in some embodiments, it is desirable to include ballast
sensors rather than rail sensors or only ballast sensors, because:
ballast sensors may be less prone to damage by trains passing along
track section 12, ballast sensors may be more robust to maintenance
of track section 12 which may involve physical manipulation of
track section 12 (e.g. lifting track section 12 away from ballast
52), ballast sensors may produce more uniform signals, ballast
sensors may exhibit greater differences in spatial attenuation and
may therefore lead to more accurate location of the hypocenter of
rock fall events and ballast sensors may be less sensitive to high
frequency vibrations which may permit lower sampling rates and
correspondingly higher bit resolution for the same data acquisition
hardware.
[0057] FIG. 3 is a schematic illustration of a signal processing
unit 26 according to a particular embodiment which is suitable for
use with rock fall detection system 10. In the illustrated
embodiment, signal processing unit 26 comprises a plurality m of
inputs 100 corresponding to transmission lines 24 and signals 22
from sensor arrays 18. Each input signal 100 is provided to
corresponding signal conditioning circuitry 102. Suitable signal
conditioning circuitry 102 is well known to those skilled in the
art and, by way of non-limiting example, may comprise anti-aliasing
filter(s) and amplifier(s). Conditioned sensor signals 104 are then
provided to analog-to-digital converters (ADCs) 106. ADCs 106
sample conditioned sensor signals 104 and provide corresponding
digital sensor signals 108. In one particular embodiment, ADCs 106
provide 24 bits of digital resolution (i.e. digital sensor signals
108 comprise a sequence of 24 bit samples), but this is not
necessary. In other embodiments, ADCs 106 may output digital sensor
signals 108 having other suitable bit depths. The sampling rate of
ADCs 106 may be selected to be sufficiently fast to accommodate the
frequencies of interest, as described in more detail below.
[0058] Digital sensor signals 108 output from ADCs 106 are provided
to data logger 110. In addition to receiving digital sensor signals
108, data logger 110 also receives timing synchronization signal
112 from timing synchronization source 114. In one particular
embodiment, timing synchronization source 114 comprises a global
positioning satellite (GPS) receiver which receives timing
information from one or more satellite sources. A GPS-based timing
synchronization source 114 is particularly useful in embodiments,
where system 10 is a modular component system of a larger system
that includes other component system(s) 32 (FIG. 1), which other
component systems 32 may have their own signal processing units 26
and their own timing synchronization sources 114. In such systems,
GPS-based timing synchronization sources 114 could provide
synchronous timing signals 112 across modular component system 10
and other component systems 32. In other embodiments, where there
is only one signal processing unit 26, timing synchronization
source 114 may comprise one or more other sources of timing
information. By way of non-limiting example, timing synchronization
source 114 may access timing information from an internal or
external quartz piezo-electric oscillator, timing synchronization
source 114 may comprise a real time clock or a suitable hardware
timing chip or the like.
[0059] Using timing synchronization signal 112 and digital sensors
signals 108, data logger 110 time stamps, collects and logs the
data generated by sensor arrays 118 (FIG. 1). Data logger 110 may
have access to memory (not expressly shown) and may use any
suitable data structure(s) or database protocol(s) for logging
digital sensor signals 108 and corresponding time stamp information
from synchronization signal 112. Data logger 110 may store
information in a manner that is indexed, or otherwise accessible,
by time stamp indicia, by corresponding sensor, and/or by
occurrence of an event (as explained in more detail below). In some
embodiments, data logger 110 may be operatively connected (via
network interface 122 and network connection 28) to remote
workstation 30 and/or to other systems 32 (see FIG. 1). Processor
120 and/or data logger 110 may perform data compression to save
local storage space and/or network bandwidth. In the illustrated
embodiment, data logger 110 is also operatively connected (via
interface 118) to embedded data processor 120.
[0060] In some embodiments, signal conditioning circuitry 102, ADCs
106, and/or data logger 110 may be implemented by a data
acquisition unit (DAU) 116. Various DAUs are known to those skilled
in the art and are commercially available from a number of sources.
In some embodiments, DAU 116 may also incorporate its own timing
synchronization source 114. In some embodiments, DAU 116 may
include other components which are not expressly shown in the FIG.
3 illustration. By way of non-limiting example, such components may
include digital processing components (e.g. digital filters) or the
like. Suitable DAUs include, by way of non-limiting example, the
TMA-24 Microseismic Acquisition Unit available from Terrascience
Systems Ltd. of Vancouver, Canada and other suitable DAUs. In some
embodiments, it is desirable that the DAU sample at a rate greater
than or equal to 500 Hz with a bid resolution of 16 or more
bits.
[0061] Commercially available DAUs 116 may have a limited number of
inputs 100 or a limited data storage capacity. In some embodiments,
therefore, signal processing unit 26 may comprise a plurality of
DAUs 16, each of which may be configured in a manner similar to
that described herein.
[0062] Data logger 110 is operatively connected (via interface 118)
to data processor 120. Data processor 120 may be part of a suitably
configured computer system (not shown) or may be part of an
embedded system. Processor 120 shown schematically in FIG. 3 may
comprise more than one individual data processor which may be
centrally located and/or distributed. Processor 120 may comprise
internal memory (not shown) and/or have access to external memory
128. Processor 120 may be programmed with, or otherwise have access
to, software 124. As explained in more detail below, processor 120
may execute software 124 which may in turn cause processor 120 to
process data obtained from data logger 110 and to generate one or
more outputs 126. Processor 120 may also control the operation of
DAU 116, data logger 110 and/or system 10 via interface 118. In
some embodiments, processor 120 may be operatively connected (via
network interface 122 and network connection 28) to remote
workstation 30 and/or to other systems 32 (see FIG. 1). Processor
120 may output some or all of outputs 126 to remote workstation 30
and/or to other systems 32 via network interface 122 and network
connection 28.
[0063] In embodiments where system 10 includes optional image
capturing devices 34, signal processing unit 26 may also comprise
image data memory 130 for storing image data 36 captured by image
capturing devices 34. Image data 36 may be delivered to image data
memory 130 along transmission lines 40 as shown in the illustrated
embodiment or may be wirelessly delivered to image data memory 130
using a wireless transceiver (not shown). Data processor 120 may
also control image capturing devices 34 using camera control
signals 38 which may be transmitted to image capturing devices 34
along transmission lines 40 and/or wirelessly. Camera control
signals 38 may permit image capturing devices 34 to move (e.g.
pan), zoom, focus or the like and may control when and how image
capturing devices 34 capture image data 36.
[0064] FIG. 4A is a plot showing digitized and time stamped sensor
data obtained at processor 120 for a number of sensors (e.g.
sensors 50) within sensor arrays 18. The vertical axis of the FIG.
4A plot is measured in binary counts (e.g. digital values output by
ADCs 106 and stored in data logger 110) and the horizontal axis of
the FIG. 4A plot is measured in milliseconds (ms). As discussed
above, acoustic energy sensors within sensor arrays 18 may output
signals 22 that are generally correlated with sensed velocity of a
sensor element. In such embodiments, the binary counts on the
vertical axis of the FIG. 4A plot may also be correlated with this
velocity. Where the acoustic energy sensors within sensor arrays 18
represent other parameters (e.g. displacement, acceleration,
energy), the binary counts on the vertical axis of the FIG. 4A plot
may be correlated with such other parameters. It should be noted
that the scales of the vertical axis for the individual sensor
plots within FIG. 4A are different for each sensor--i.e. the plots
corresponding to sensors #1, #(m-1) and #m have ranges of
approximately (-100,100) binary counts, the plots corresponding to
sensors #2 and #4 have ranges of approximately (-2500,2500) and the
plot corresponding to sensor #3 has a range of approximately
(-5000,5000).
[0065] FIG. 4A shows typical digitized and time-stamped sensor data
obtained at processor 120 for a small event that is detected in a
region of sensors #2, #3 and #4 at a time around 3,000-7,000 ms. It
can be seen that, in the 3,000-7,000 ms time period, the magnitude
of the sensed signals of sensors #2, #3 and #4 (on the order of
thousands of binary counts) is significantly greater than the
background noise (on the order of hundreds of binary counts). This
event is typical of a small scale rock fall, but may also be
typical of other small scale events, such as (by way of
non-limiting example): raveling of a rock face, a surge in adjacent
waterfall activity, one or more animals, vegetation or fencing
shaken by wind or the like. For typical applications alongside
railways tracks, the scale of the FIG. 4A event may be interpreted
to be sufficiently small that it is not of significant concern.
[0066] FIG. 4B shows typical digitized and time-stamped sensor data
obtained at processor 120 for a passing train. Like FIG. 4A, the
vertical axis of the FIG. 4B plot is measured in binary counts and
the horizontal axis is measured in milliseconds. However in the
FIG. 4B plot, the vertical axes for each sensor are on the same
scale (-2.times.10.sup.6,2.times.10.sup.6). It can be seen that the
duration of the train event is significantly longer than the event
of FIG. 4A.
[0067] FIG. 4C shows typical digitized and time-stamped sensor data
obtained at processor 120 for a passing highrail vehicle. The
vertical axis of the FIG. 4C plot is measured in binary counts and
the horizontal axis is measured in milliseconds. While the vertical
scales vary between the individual FIG. 4C plots for the individual
sensors, it can be seen that the scales of the individual FIG. 4C
plots for the highrail vehicle have scales that are lower than
those of FIG. 4B for the train.
[0068] In some environments, there may be additional sources of
events which may be particular of the environment in which system
10 is deployed. One example of a such an event is the activation of
a power generator in a vicinity of system 10. Where it is desired
for system 10 to operate at a remote location, such a generator may
be used to power system 10 itself. Such a generator is not required
however and other sources of power (e.g. batteries, solar power or
wind power) may be used to power system 10. FIG. 4D shows typical
digitized and time-stamped sensor data obtained at processor 120
for the activation of a power generator in the vicinity of system
10. The vertical axis of the FIG. 4D plot is measured in binary
counts and the horizontal axis is measured in milliseconds. The
vertical axes for each sensor in the FIG. 4D plots is on the same
scale (-4000,4000).
[0069] FIG. 4E shows typical digitized and time-stamped sensor data
obtained at processor 120 for a rock fall event that is of
sufficient size to be of concern for typical railway applications.
The vertical axis of the FIG. 4E plot is measured in binary counts
and the horizontal axis is measured in milliseconds. It will be
noted that the vertical scales vary between the individual FIG. 4E
plots for the individual sensors. FIG. 4E indicates that the rock
fall event took place between approximately 19,000-21,000 ms.
Comparing the vertical scales of the various sensors and the
corresponding magnitudes of the sensed signals, it would appear
that the rock fall event occurred relatively closer to sensor #m
than to any of the other illustrated sensors. Comparing the
vertical scales and the corresponding magnitudes of the sensed
signals between the FIG. 4E rock fall event and the FIG. 4A small
scale event, it can be seen that the FIG. 4E rock fall event has
significantly greater magnitude.
[0070] In some embodiments, rock fall detection by system 10 may be
performed by signal processing unit 26 based on signals 22 received
from sensor arrays 18 (see FIG. 1). In particular embodiments, rock
fall detection by system 10 may involve processor 120 processing
data from data logger 110 (or DAU 116) to detect rock fall events
(see FIG. 3). In other embodiments, rock fall detection may be
performed at remote workstation 30 and/or at other systems 32
having access to data from data logger 110 (or DAU 116) via network
connection 28. For the remainder of this description, it is
assumed, without loss of generality, that rock fall detection is
performed by embedded processor 120 processing data received from
data logger 110.
[0071] A part of rock fall detection performed by system 10
involves discriminating between rock fall events and other types of
events which may be of less concern and/or between significant rock
fall events and relatively small rock fall events which may be of
less concern. An event that is determined by system 10 to be a
significant rock fall event, but which in fact is a different event
(e.g. a moving train, a train that has come to a stop in a vicinity
of track section 12, a moving highrail vehicle, a highrail vehicle
that has come to a stop in a vicinity of track section 12 (e.g. to
perform maintenance on track section 12), an animal in a vicinity
of track section 12 and/or a power generator) or is an
insignificant rock fall event may be referred to in this
description as a false positive detection result. In general, there
is a desire to minimize false positive detection results.
[0072] To detect rock fall events while minimizing false positive
detection results, processor 120 may process data received from
data logger 110 to determine a plurality of processing parameters.
Some or all of these processing parameters may be used in turn to
discriminate rock fall events from other events. FIG. 5
schematically illustrates a number of processing parameters 150
which may be determined by processor 120 using data accessed from
data logger 110. Each of these processing parameters 150 is
explained in more detail below. In some embodiments, processor 120
may output one or more of processing parameters 150 as outputs 126.
As discussed above, some or all of outputs 126 may be available to
remote workstation 30 and/or other systems 32 via network
connection 28.
[0073] Processor 120 may process the data corresponding to one or
more sensors to determine a ratio of a short term average (STA) to
a long term average (LTA), which may be one of the processing
parameters 150. This ratio may be referred to as an STA/LTA average
and may be computed according to:
( STA LTA ) n = i = ( n - ( a - 1 ) ) i = n x i a i = ( n - ( b - 1
) ) i = n x i b where b > a > 0 and b .gtoreq. a , b ( 1 )
##EQU00001##
where: x.sub.i represents the value of the sample, n is the index
of the current sample x.sub.n, a is the STA duration (number of
samples) and b is the LTA duration (number of samples). Examining
equation (1), it will be appreciated that the STA and LTA durations
a and b may be expressed as numbers of samples or equivalently as
temporal durations.
[0074] FIGS. 6A and 6B respectively depict typical digitized,
time-stamped sensor data obtained at processor 120 for a rock fall
event and the corresponding STA/LTA ratio. For the FIG. 6B plots,
the STA duration a is 20 ms and the LTA duration b is 1000 ms. It
can be seen from the FIGS. 6A and 6B plots that the rock fall event
occurs around the 1250-1750 ms time period.
[0075] The STA/LTA ratio is useful for detecting when a signal
changes to stand out from background noise and may therefore be
compared against a suitable threshold to trigger the start and end
of an event. For example, when the LTA/STA ratio is greater than an
event start threshold (thresh_start), then processor 120 may
determine that an event has started and the associated time
t.sub.start. Similarly, when an event has started and the LTA/STA
ratio falls below an event end threshold (thresh_end), then
processor 120 may determine that an event has ended and the
associated time t.sub.end. The STA/LTA threshold parameters
thresh_start, thresh_end may be experimentally determined as a part
of the calibration of system 10 and may depend, by way of
non-limiting example, on the STA averaging duration a, the LTA
averaging duration b, the spectral characteristics (e.g. amplitude
and dominant frequencies) of the background noise in a vicinity of
track section 12 and/or the expected spectral characteristics (e.g.
amplitude and dominant frequencies) of an event that system 10 is
designed to detect. These threshold parameters may additionally or
alternatively be user adjustable.
[0076] The start and end times t.sub.start, t.sub.end of an event
can also be used to determine the event duration t.sub.dur as one
of the FIG. 5 processing parameters 150 according to:
t.sub.dur=t.sub.end-t.sub.start (2)
[0077] An issue which may arise with the STA/LTA ratio is the so
called "memory" associated with the LTA. The LTA value computed by
processor 120 carries with it information about the last b samples
(where b is the LTA duration used in equation (1)). In some cases,
the last b samples will be influenced by an event. For example,
when a train passes track section 12, it typically takes a period
of time for the train to pass. In such cases, the last b samples
used to compute the LTA may be influenced by the signal associated
with the passing train--e.g. the LTA may be relatively large
during, or even after, a passing train. In such circumstances, the
relatively high LTA may cause the STA/LTA ratio to lose
sensitivity, even if the STA is relatively high.
[0078] In some embodiments, therefore, processor 120 processes the
data from data logger 110 to determine a modified STA/LTA ratio as
one of processing parameters 150. This modified STA/LTA ratio may
involve replacing the actual LTA with a constant c according
to:
( STA LTA ) mod , n = i = ( n - ( a - 1 ) ) i = n x i where n >
a > 0 ( 3 ) ##EQU00002##
The constant c may be representative of the LTA during event free
times (e.g. times without rock fall or passing trains or the like)
and, in some embodiments, may be determined during calibration of
system 10. For example, the constant c may be determined in a
relatively noise free period in the environment where system 10 is
deployed prior to the actual deployment of system 10. In one
particular embodiment, the constant c may be determined to be an
actual LTA during such a noise free period (e.g. determined
according to the denominator of equation (1)). The constant c may
be user adjustable.
[0079] The modified STA/LTA ratio (equation (3)) may be used in
substantially the same manner as the actual STA/LTA ratio (equation
(1)) to determine the start and end of an event and the associated
times t.sub.start, t.sub.end and to determine the associated event
duration t.sub.dur. In some embodiments, the modified STA/LTA ratio
may be used in addition to or as an alternative to the actual
STA/LTA ratio. In some embodiments, the thresholding decision
associated with the start and/or end of an event may involve a
compound decision wherein both the modified and actual STA/LTA
ratios are subject to threshold conditions. In some embodiments,
the decision as to whether to use the actual STA/LTA ratio, the
modified STA/LTA ratio or both (i.e. to determine the start and end
of an event and the associated times t.sub.start, t.sub.end and to
determine the associated event duration t.sub.dur) may be a
user-selectable parameter.
[0080] Another processing parameter 150 that may be determined by
processor 120 based on data from data logger 110 may be referred to
as a peak particle velocity (PPV). The PPV may represent the
magnitude of the sample with the largest absolute value during an
event and may be determined according to:
PPV=MAX{|x.sub.i.parallel.i.epsilon.t.sub.start . . . t.sub.end}
(4)
where MAX{.cndot.} is an operator that returns the maximum value of
the operand and x.sub.i represents the value of the i.sup.th
sample.
[0081] As discussed above, in the illustrated embodiment, sensor
arrays 18 comprise one or more acoustic energy sensors (e.g.
sensors 50) which output signals 22 correlated with velocity of a
sensor component. As such, the PPV corresponds to the maximum or
peak velocity measured by such sensors--hence the term peak
particle velocity. In general, however, sensor arrays 18 may
comprise acoustic energy sensors which output signals 22 correlated
with other parameters (e.g. energy, displacement and/or
acceleration). In such embodiments, PPV should be understood to
represent the magnitude of the sample with the largest absolute
value during an event in accordance with equation (4) and need not
represent velocity in strict sense. In some embodiments, processor
120 may also determine the time t.sub.PPV associated with the PPV.
In some embodiments, processor 120 may also determine a global PPV
value PPV.sub.global which represents the magnitude of the sample
with the largest absolute value over all of the recorded
samples--i.e. a PPV which is not limited to the times between
t.sub.start and t.sub.end during an event.
[0082] System 10 may use PPV to help discriminate between
significant rock falls and other types of events. In one particular
embodiment, PPV is subjected to a thresholding process which may
filter out small rock fall events, other low magnitude events (e.g.
animals) and/or background noise events (e.g. the operation of a
power generator). For example, if the PPV of an event is less than
a PPV threshold (thresh_PPV), then processor 120 may determine that
the event has insufficient magnitude to be a significant rock fall.
The PPV threshold parameter thresh_PPV may be experimentally
determined as a part of the calibration of system 10 and may
depend, by way of non-limiting example, on particular minimum
magnitude rock fall detection required of system 10, the expected
magnitude of low magnitude events (e.g. animals or humans), the
expected magnitude of background events (e.g. power stations,
waterfalls, wind) and/or the like. The PPV threshold parameter
thresh_PPV may additionally or alternatively be user
adjustable.
[0083] Another processing parameter 150 that may be determined by
processor 120 based on data from data logger 110 may be referred to
as a the signal energy E. In some embodiments, the signal energy E
used by system 10 may represent a windowed average of the sample
amplitude squared and may be determined according to:
E n = i = n - ( d - 1 ) n ( x i ) 2 d ( 5 ) ##EQU00003##
where: x.sub.i represents the value of the i.sup.th sample, n is
the index of the current sample x.sub.n and d is the window
duration (number of samples). Examining equation (5), it will be
appreciated that the duration d may be expressed as numbers of
samples or equivalently as temporal durations.
[0084] Like the STA/LTA ratio discussed above, the signal energy E
is useful for detecting when a signal changes to stand out from
background noise and may therefore be compared against suitable
thresholds to trigger the start and end of an event. For example,
when the signal energy E is greater than an event start threshold
(E_thresh_start), then processor 120 may determine that an event
has started and the associated time t.sub.start. Similarly, when an
event has started and the signal energy E falls below an event end
threshold (E_thresh_end), then processor 120 may determine that an
event has ended and the associated time t.sub.end. The threshold
parameters E_thresh_start, E_thresh_end may be experimentally
determined as a part of the calibration of system 10 and may
depend, by way of non-limiting example, on the duration d of the
energy window, the spectral characteristics (e.g. amplitude and
dominant frequencies) of the background noise in a vicinity of
track section 12 and/or the expected spectral characteristics (e.g.
amplitude and dominant frequencies) of an event that system 10 is
designed to detect. These threshold parameters may additionally or
alternatively be user adjustable. The start and end times
t.sub.start, t.sub.end of an event can also be used to determine
the event duration t.sub.dur as described above (equation (2)).
[0085] The signal energy E may also be used in addition to or in
the alternative to the STA/LTA ratio in other circumstances where
it might be appropriate to use the STA/LTA ratio. The maximum
signal energy E.sub.max=MAX{E.sub.i|i.epsilon.t.sub.start . . .
t.sub.end} also exhibits a correlation with the PPV discussed
above. In some embodiments, the maximum signal energy E.sub.max may
be used in addition to or in the alternative to the PPV value in
circumstances where it might be appropriate to use the PPV
value.
[0086] Another processing parameter 150 that may be determined by
processor 120 based on data from data logger 110 is the spectral
power distribution (e.g. frequency content) of a signal
corresponding to an event. In one particular embodiment, processor
employs a Fast Fourier Transform (FFT) technique to the sampled
data during an event (i.e. between t.sub.start and t.sub.end). The
spectral power may therefore be referred to in this description as
the FFT. As is known in the art, however, there are a number of FFT
techniques and other techniques for determining the time-frequency
content of a digitally sampled signal and any such techniques may
be used to determine the time-frequency content of a signal.
[0087] FIGS. 6C and 6D respectively show a 0.4 second segment of a
time stamped, digital sensor signal received at processor 120 and a
corresponding FFT associated with a typical rock fall event. FIG.
6D show that most of the spectral power of the digital sensor
signals associated with a typical rock fall event is concentrated
in the frequency band less than 125 Hz. FIGS. 6E and 6F
respectively show a 0.4 second segment of a time stamped, digital
sensor signal received at processor 120 and a corresponding FFT
associated with a typical train event. FIG. 6F shows that the
spectral power of the digital sensor signals associated with a
typical train event is spread over 0-400 Hz and has significant
power at frequencies over 200 Hz. FIGS. 6G and 6H respectively show
a 1.0 second segment of a time stamped, digital sensor signal
received at processor 120 and a corresponding FFT associated with a
typical highrail vehicle event. FIG. 6H shows that the spectral
power of the digital sensor signals associated with a typical
highrail vehicle event (like a train event) is spread over 0-400 Hz
and has significant power at frequencies over 200 Hz. The sensor
data shown in FIGS. 6C-6H is obtained from representative rail
sensors, but the inventors have concluded that similar results are
achievable with suitably configured ballast sensors.
[0088] FIGS. 6I and 6J respectively show an 11 second segment of a
time stamped, digital sensor signal received at processor 120 and a
corresponding FFT associated with the operation of a generator in a
vicinity of track section 12. FIG. 6J shows that the spectral power
of the digital sensor signals associated with the generator has a
unique frequency signature with harmonics at 30.66 Hz, 45.95 Hz and
60 Hz.
[0089] FIG. 7A schematically depicts a method 200 for event
detection according to a particular embodiment. Method 200 may be
performed in whole or in part by embedded processor 120. Method 200
may make use of data obtained from data logger 110 and/or DAU 116
and may also make use of processing parameters 150. As discussed
above, in other embodiments, rock fall detection (including method
200 in whole or in part) may be performed by other processors, such
as by processors associated with remote workstation 30 and/or other
systems 32.
[0090] Method 200 starts at block 201. Method 200 may involve a
number of procedures which are similar for the data associated with
each sensor--e.g. to each particular digital sensor signal 108
(FIG. 3). In the illustrated embodiment, these similar procedures
are shown by the representative procedures of block 202 (associated
with sensor #1) and block 204 (associated with sensor #m). It will
be appreciated that, depending on the number of sensors and the
corresponding number of digital sensor signals 108, method 200 may
generally comprise any suitable number of procedures similar to
those of blocks 202, 204. The procedure of block 202 is now
described in more detail, it being understood that the procedure
associated with block 204 and other similar blocks may be
substantially similar to that of block 202.
[0091] The block 202 procedure starts in block 210 which involves
initializing a number of parameters. For example, block 210 may
involve obtaining sufficient number of data samples (a) to
calculate the STA (the numerator of equation (1) and/or equation
(3)) and/or a sufficient number of data samples (b) to calculate
the LTA (the denominator of equation (1)). Such data samples may be
taken from the digital signal sensor signal 108 associated with
block 202. Block 210 may involve resetting a number of the
processing parameters 150 which may have been used during previous
post event processing (described in more detail below). Block 210
may also involve initializing one or more calibration parameters
and/or user-configurable parameters. The procedure of block 202
then proceeds to block 215, which involves obtaining the next data
sample--e.g. the next data sample from the digital sensor signal
108 associated with block 202.
[0092] In block 220, block 202 may involve updating one or more
processing parameters 150 based on the newly acquired block 215
data and, in some instances, the historical data obtained prior to
the current iteration of block 215. In particular embodiments, the
particular processing parameters 150 which are updated in block 215
include those associated with event-start triggering criteria. As
explained above, processing parameters 150 associated with
triggering the start of an event may include: the STA/LTA ratio
(equation (1)), the modified STA/LTA ratio (equation (3)) and/or
the energy (equation (5)).
[0093] Block 225 involves evaluating event-start criteria. The
block 225 event-start criteria may involve an evaluation of whether
one or more processing parameters (e.g. the STA/LTA ratio (equation
(1)), the modified STA/LTA ratio (equation (3)) and/or the energy
(equation (5)) are greater than one or more corresponding
event-start thresholds (e.g. thresh_start.sub.(STA/LTA),
thresh_start.sub.(STA/LTA)mod, thresh_start.sub.(E)). If the block
225 evaluation of the event-start criteria is negative (block 225
NO output), then the procedure of block 202 loops back to block 215
to obtain another data sample. If on the other hand the block 225
evaluation of the event-start criteria is positive (block 225 YES
output), then the procedure of block 202 proceeds to block 230.
[0094] Block 230 involves setting a value for t.sub.start. In
particular embodiments, the block 230 t.sub.start value may be
based on the time associated with the current block 215 data
sample. The procedure of block 202 then proceeds to blocks 235 and
240 which involve obtaining the next data sample and updating one
or more processing parameters in a manner similar to that of blocks
215 and 220 described above.
[0095] Block 245 then involves evaluating event-end criteria. The
block 245 event-end criteria may involve an evaluation of whether
one or more processing parameters (e.g. the STA/LTA ratio (equation
(1)), the modified STA/LTA ratio (equation (3)) and/or the energy
(equation (5)) are less than one or more corresponding event-end
thresholds (e.g. thresh_end.sub.(STA/LTA),
thresh_end.sub.(STA/LTA)mod, thresh_end.sub.(E)). If the block 245
evaluation of the event-end criteria is negative, then the block
202 procedure loops back to block 235 to obtain another data
sample. If on the other hand the block 245 evaluation of the
event-end criteria is positive, then block 202 procedure determines
that the event has ended and proceeds to block 250, which involves
setting a value for the event end time t.sub.end. In particular
embodiments, the block 250 t.sub.end value may be based on the time
associated with the current block 235 data sample.
[0096] In the illustrated embodiment, the block 202 procedure then
proceeds to block 255, which involves post event processing. The
post event processing of block 255 may involve discriminating
between types of events or otherwise determining whether a
particular event is a significant rock fall event. In the
illustrated embodiment of method 200, the block 255 post event
processing is shown within the block 202 procedure--i.e. the block
255 post event processing may be performed for each sensor whose
digital signal 108 triggers the detection of an event. This is not
necessary. In some embodiments, the block 255 post event processing
may be performed outside of the block 202 procedure--e.g. the block
255 post event processing may be performed on a global basis and/or
for a subset of the sensors whose digital signals 108 trigger the
detection of an event.
[0097] FIG. 7B schematically depicts a method 300 for post event
processing which may be performed in block 255 according to a
particular embodiment. In the illustrated embodiment, the post
event processing of method 300 involves discriminating between a
number (e.g. six) of different types of events. In other
embodiments, the post event processing may involve discriminating
between two types of events--i.e. significant rock fall events and
any other kind of event. In some embodiments, method 300 may be
performed for each sensor whose digital sensor signal 108 triggers
the detection of an event. In other embodiments, method 300 may be
performed for a subset of the sensors whose digital sensor signals
108 trigger the detection of an event.
[0098] In the illustrated embodiment, method 300 starts in block
305 which involves determining one or more event specific
processing parameters 150. Once t.sub.start and t.sub.end are
determined (eg. in blocks 230, 250) for a particular sensor in
system 10, processor 120 may obtain a subset of the associated
digital sensor signal 108 which occurs between t.sub.start and
t.sub.end. This data subset may in turn be processed to obtain the
block 305 event specific processing parameters 150. Examples of
event specific processing parameters that may be determined in
block 305 include: the duration t.sub.dur of the event which may be
determined according to equation (2); the PPV which may be
determined according to equation (4); the time (t.sub.PPV)
associated with the PPV; the spectral power (FFT) of the discrete
signal between t.sub.start and t.sub.end. To the extent that the
STA/LTA ratio, the modified STA/LTA ratio or the energy are not
determined in the block 202 procedure, then any one or more of
these quantities (and/or their associated maxima, STA/LTA.sub.max,
STA/LTA.sub.mod.sub.--.sub.max, E.sub.max and the times of their
associated maxima) may also be determined in block 305.
[0099] Method 300 then proceeds to block 310 which involves
evaluating event duration criteria. The block 310 evaluation may
involve comparing the event duration t.sub.dur to a threshold
(thresh_dur) to determine whether the event duration t.sub.dur is
less than the threshold (thresh_dur). In some embodiments, the
event duration threshold (thresh_dur) may be in a range of 1-3
seconds. In other embodiments, this range may be 2-10 seconds. The
magnitude of the event duration threshold (thresh_dur) may depend
on the typical length of the trains that pass through track section
12.
[0100] If t.sub.dur is greater than the event duration threshold
(thresh_dur), then method 300 may proceed along the block 310 NO
output to block 315. In the illustrated embodiment, block 315
involves concluding that the event is either a passing train or a
passing highrail vehicle. From block 315, method 300 proceeds to
block 320 which involves an evaluation of a magnitude criteria to
determine whether the event was triggered by a passing train (block
320 YES output and the conclusion of block 325) or the event was
triggered by a highrail vehicle (block 320 NO output and the
conclusion of block 330). The block 320 magnitude evaluation may
involve comparing the PPV of the associated digital sensor signal
108 to a suitable threshold. If the PPV is greater than the block
320 threshold, then the event is determined to be a train (block
320 YES output and block 325 conclusion), whereas if the PPV is
less than the block 320 threshold, then the event is determined to
be a highrail vehicle (block 320 NO output and block 330
conclusion). Depending on the geological site conditions, in some
embodiments block 320 may additionally or alternatively involve an
evaluation of spectral criteria (e.g. comparing the FFT of an event
to one or more thresholds). Such spectral criteria may be used as
an alternative to or in addition to the block 320 magnitude
criteria to discriminate a train event from a highrail event.
[0101] Returning to the block 310 evaluation, if t.sub.dur is less
than the event duration threshold (thresh_dur), then method 300 may
proceed along the block 310 YES output to optional block 335. Block
335 involves the optional evaluation of spectral criteria to
determine whether an event was triggered by a passing train or
highrail vehicle. As discussed above, depending on the geological
conditions in a vicinity of track section 12, suitably configured
sensors (e.g. ballast sensor 50 of FIG. 2A and/or rail sensor 80 of
FIG. 2B) may generate distinctive frequency characteristics in
response to trains and/or highrail vehicles traveling on track
section 12. These distinctive frequency characteristics may be used
to discriminate trains or highrail vehicles from other types of
events. In one particular embodiment, the block 335 spectral
criteria involves determining whether the FFT associated with a
digital sensor signal 108 has a significant amount (e.g. x % or
more) of its power at frequencies greater than a frequency
threshold (thresh_freq). In one particular embodiment, this
threshold may be in a range of 100 Hz-300 Hz. In another
embodiment, this threshold may be in a range of 125 Hz-200 Hz. In
one particular embodiment, the significant amount (e.g. x % or
more) may be in a range of 0%-25%. In other embodiments, the
significant amount (e.g. x % or more) may be in a range of
5%-15%.
[0102] If the FFT of the rail sensor has a significant amount (e.g.
x % or more) of its power at frequencies greater than a frequency
threshold (thresh_freq), then the block 335 evaluation is positive
(YES output) and method 300 proceeds to block 340 which involves
concluding that the event is either a passing train or a passing
highrail vehicle. Blocks 340, 345, 350 and 355 may be substantially
similar to blocks 315, 320, 325 and 330 discussed above and may
involve discriminating between a train (block 350 conclusion) and a
highrail vehicle (block 355 conclusion).
[0103] If, on the other hand, the FFT of the rail sensor does not
have a significant amount (e.g. x % or more) of its power at
frequencies greater than a frequency threshold (thresh_freq), then
the block 335 evaluation is negative (NO output) and method 300
proceeds to block 360 which involves evaluation of magnitude
criteria to determine whether the event in question is a
significant rock fall event--i.e. a rock fall event worthy of
concern. The block 360 magnitude evaluation may involve comparing
the PPV of the associated digital sensor signal to a suitable
threshold (thresh_PPV). In some embodiments, this magnitude
threshold (thresh_PPV) may be in a range of 500-5000 bits. In other
embodiments, this range may be 750-2,500 bits. If the PPV is less
than the block 360 threshold (thresh_PPV), then the event is
determined to be an insignificant event (block 360 NO output and
block 365 conclusion).
[0104] In the illustrated embodiment, however, method 300 goes
beyond the block 365 conclusion of classifying the event as an
insignificant event. As discussed above, it may be desirable to
discriminate other types of natural or human-made noise that may
trigger events in a vicinity of track section 12. In one particular
embodiment, a generator (not shown) is located in a vicinity of
track section 12. When the generator turns on, it may trigger an
event on one or more sensors of system 10. In the illustrated
embodiment, method 300 proceeds from block 365 to block 370 which
involves evaluation of spectral criteria. As explained above, the
spectral power associated with the start up and operation of the
generator has a particular spectral pattern. Accordingly, spectral
criteria can be designed for the block 370 inquiry to determine
whether the event was triggered by the generator. Such spectral
criteria may involve evaluation of whether the FFT of the
associated digital sensor signal 108 has a significant amount (e.g.
y % or more) of its power at frequencies within particular
frequency bands associated with the start up and operation of the
generator. If the block 370 evaluation is positive (block 370 YES
output), then method 300 concludes that the event was triggered by
the generator in block 375.
[0105] It should be noted that the block 370 spectral evaluation
and the block 375 conclusion that the event was triggered by a
generator represent one non-limiting example of the type of
criteria which may be used to discriminate other types of natural
or human-made surface noise that may trigger events in a vicinity
of track section 12. In other embodiments, it might be desirable to
use additional or alternative criteria (e.g. in block 370 or in
other similar inquiries) to discriminate additional or alternative
surface noise events. Such surface noise events may include (by way
of non-limiting example): noise created by moving water (e.g.
waterfalls, rivers or the like); noise created by animals; noise
created by nearby traffic; noise created by falling trees; noise
created by trains or highrail vehicles that have come to a stop in
a vicinity of track section 12; and/or the like. The types of
criteria used to discriminate these events may include (by way of
non-limiting example): magnitude criteria, spectral criteria,
duration criteria, correlation criteria and/or the like. It is not
necessary that the evaluation of these additional or alternative
criteria occur in any particular order relative to the other method
300 criteria evaluations. In general, the method 300 criteria
evaluations can occur in any desirable order. For example, if it is
known that the generator is likely to start every 10 minutes and
run for 2 minutes, then it may be desirable to locate the block 370
spectral criteria evaluation at an earlier point within method 300
to quickly conclude generator events and to thereby conserve
processing resources.
[0106] If the block 370 evaluation is negative (block 370 NO
output), then method 300 proceeds to block 380 which involves an
evaluation of accumulation criteria to determine whether there has
been a sufficient amount of low magnitude rock fall within a
sufficiently short period to time to conclude that there has been
rock fall accumulation that may be of concern. In one particular
embodiment, the block 380 accumulation criteria involves
consideration of whether method 300 has reached block 380 (i.e.
small rock fall event) more than a threshold number of times
(thresh_#) within a recent time period .DELTA.T. By way of
non-limiting example, block 380 may involve evaluating whether
method 300 has reached block 380 more than 5 times within the last
hour. In some embodiments, the block 380 threshold number of times
(thresh_#) is in a range of 3-50. In other embodiments, this range
is 10-20. In some embodiments, the block 380 time period .DELTA.T
is in a range of 30-900 minutes. In other embodiments, this range
is 60-480 minutes.
[0107] If the block 380 accumulation criteria evaluation is
positive (block 380 YES output), then method 300 proceeds to block
385 which concludes that there has been sufficient rock fall
accumulation to be of concern. If the block 390 accumulation
criteria evaluation is negative (block 380 NO output), then method
300 proceeds to block 390 which concludes that the event was an
insignificant rock fall event.
[0108] Returning to the block 360 magnitude evaluation, if the PPV
is greater than the block 360 threshold (thresh_PPV), then method
300 proceeds (via block 360 YES output) to block 392 which involves
an inquiry into whether the current event is followed by a train
event. The inventors have determined that when a train passes over
track section 12, the passing train can trigger a number of events
(e.g. the train can satisfy the block 225 event start criteria)
prior to triggering the principal train event. These events which
are triggered prior to the principal train event may be referred to
as train precursor events.
[0109] FIG. 8 is a schematic depiction of the triggered state of a
number of sensors in response to a passing train. It can be seen
from FIG. 8, that a number of train precursor events 303 occur for
each sensor in the time leading up to the persistent principal
train events 304. The inventors have determined that the time
during which train precursor events are likely to occur is within a
time window .DELTA.t.sub.pre-train prior to the onset of principal
train events. In some embodiments, this time window
.DELTA.t.sub.pre-train is in a range of 5-30 seconds. In other
embodiments, this time window .DELTA.t.sub.pre-train is in a range
of 10-20 seconds.
[0110] In some embodiments, the block 392 inquiry as to whether the
event is followed by a train event may involve an inquiry into
whether the current event being processed in method 300 is followed
within a time window .DELTA.t.sub.pre-train by a persistent train
event. As discussed herein, a persistent train event can be
discriminated on the basis of duration criteria (e.g. block 310),
spectral criteria (e.g. block 335), magnitude criteria (e.g. block
320, block 330), cross-correlation criteria, or any suitable
combination thereof. If the block 392 inquiry is positive (e.g. the
current event is followed by a train event within the time window
.DELTA.t.sub.pre-train--block 392 YES output), then method 300
proceeds to block 394 which involves concluding that the current
event is a train precursor event. If, on the other hand, the block
392 inquiry is negative (e.g. the current event is not followed by
a train event within the time window .DELTA.t.sub.pre-train--block
392 NO output), then method 300 proceeds to block 395, where the
current event is determined to be a significant rock fall
event.
[0111] As discussed above, method 300 (FIG. 7B) represents one
possible embodiment of block 255 of method 200 (FIG. 7A). Returning
to FIG. 7A, at the conclusion of block 255 (e.g. method 300),
method 200 proceeds to block 260 which involves an inquiry into
whether the block 255 post event processing associated with any of
the sensors reached a conclusion that the event was a rock fall
event (e.g. either the block 390 conclusion of method 300 that the
event is an insignificant rock fall event and/or the block 395
conclusion of method 300 that the event is a significant rock fall
event). If the event was not a rock fall event (block 260 NO
output), then method 200 proceeds to block 265 which involves
taking appropriate action for a non-rock fall event.
[0112] The nature of the block 265 action may depend on whether any
of the non-rock fall events are considered to be important for some
reason. The block 265 action may comprise logging the non-rock fall
event or doing nothing. In one particular embodiment, the block 265
action may involve generating an event record associated with the
non-rock fall event. The record of the non-rock fall event may
include recordal of a number of parameters associated with the
event. In particular embodiments, the block 265 record may include
one or more of: the event type (e.g. a block 325, 350 train event,
a block 330, 355 highrail vehicle event or a block 375 surface
noise event); a number of triggered sensors; start and end times of
the event which may include the start time (t.sub.start) for the
first triggered sensor and the end time (t.sub.end) for the last
sensor to remain triggered; the PPV and the associated time
t.sub.PPV for each triggered sensor; the maxima (and associated
times) of one or more other block 305 event specific parameters
(e.g. STA/LTA.sub.max, E.sub.max or the like); and any other
parameter of which data processor 120 may be aware. In some
embodiments, the block 265 record may also include one or more of
the images of track section 12 which may be captured by cameras
34.
[0113] In some embodiments, block 265 may involve storing the event
record in local memory (e.g. in data logger 110, memory 128 and/or
image data memory 130) until such time as signal processing unit 26
is polled for events (e.g. by remote workstation 30 over network
connection 28). Depending on the availability of local memory, in
other embodiments, block 265 may involve transmitting the event
record (e.g. to remote workstation 30 over network connection 28)
for remote storage.
[0114] If, on the other hand, the event was a rock fall event
(block 260 YES output), then method 200 proceeds to block 270 which
involves estimating a location of the rock fall event. Block 270
may involve estimating a location of the rock fall event with a
degree of accuracy which is finer than the minimum spacing 20
between sensor arrays 18 of system 10 (see FIG. 1). FIG. 7C
schematically depicts a method 400 for estimating a location of a
rock fall event which may be performed in block 270 according to a
particular embodiment. Method 400 commences in block 410 which
involves selecting a group of sensors to be considered for
estimating the rock fall location. In some embodiments, block 410
may involve determining the group of sensors to include all
triggered sensors whose start times t.sub.start are within a time
window .DELTA.T.sub.start of the start time t.sub.start of a first
sensor to trigger on a rock fall event.
[0115] This block 410 determination is shown schematically in FIGS.
9A and 9B. FIG. 9A shows a typical response of a number of sensors
to a rock fall event. Like FIG. 4E, the vertical axis of FIG. 9A
plot is measured in binary counts and the horizontal axis is
measured in milliseconds. It will be noted that the plots for the
individual sensor signals shown in FIG. 9A are on different
vertical scales, but that the particular scales for each sensors
are omitted for clarity. FIG. 9A also contrasts with FIG. 4E in
that FIG. 9A is shown on a much smaller time scale--i.e. FIG. 4E
spans a time period of 30 s, whereas FIG. 9A spans a time period of
2.2 s.
[0116] For each sensor of the FIG. 9A plot, FIG. 9B shows when the
sensor is triggered--i.e. when the sensor's trigger status is ON.
It can be seen from FIG. 9A, that the sensor #1 is the first to
trigger (at t=t.sub.start.sub.--.sub.sensor#1). Because acoustic
waves take time to travel through the substrate in the region of
track section 12, it may be assumed that sensor #1 is closest to
the hypocenter of the rock fall event. The block 410 process for
selecting the group of sensors to be considered for estimating the
rock fall location may involve selecting all of the sensors which
are triggered within a time window .DELTA.T.sub.start of
t.sub.start.sub.--.sub.sensor#1.
[0117] This time window .DELTA.T.sub.start is shown in FIG. 9B. It
can be seen from FIG. 9B that sensors #2-8 and #12-13 are also
triggered within this time window .DELTA.T.sub.start. Accordingly,
block 410 may involve selecting sensors #1-8 and #12-13 to be the
sensors used for estimating the rock fall location. FIG. 9B also
shows that sensors #9-11 and #14-15 are not triggered. As such,
sensors #9-11 and #14-15 are not selected for estimating the rock
fall location in accordance with the illustrated embodiment.
Although not explicitly shown in FIG. 9B, sensors which are
triggered after the time window .DELTA.t.sub.start may be assumed
to be indicative of a different event.
[0118] The block 410 time window .DELTA.T.sub.start may be related
to a prediction of the average speed of acoustic waves in the earth
near track section 12 and the length of track section 12 being
considered. For example, if the length of track section 12 being
monitored is 1 km and the average speed of acoustic waves in the
substrate near track 12 is determined to be 300 m/s, then the block
410 time window .DELTA.T.sub.start may be set to be 3.3
seconds.
[0119] Once the block 410 group of sensors is selected method 400
(FIG. 7C) proceeds to block 415 which involves selecting a group of
p potential locations for the hypocenter of the rock fall event.
The p potential hypocenter locations may be spaced apart from one
another by a suitable interval d which depends on the location
detection accuracy desired from system 10. The number p of
potential hypocenter locations may depend, for example, on the
processing resources associated with system 10 (e.g. associated
with embedded processor 120). As discussed above, it is logical to
assume that the hypocenter of the rock fall may be most proximate
to the sensor that triggers first (e.g. sensor #1 in the exemplary
circumstance of FIGS. 9A and 9B). In particular embodiments, the
group of p potential hypocenter locations may be provided in a grid
around the location of the sensor that is triggered first and the
grid may have a spacing of d between potential locations. In some
embodiments, the spacing d may be in a range of 1-20 m. In some
embodiments, this spacing d is in a range of 2-5 m. In some
embodiments, the number p of potential hypocenter locations is in a
range of 5-100. In some embodiments, this number p of potential
hypocenter locations is in a range of 10-25.
[0120] Method 400 then proceeds to loop 420. Loop 420 involves
carrying out a number of procedures for each of the p potential
hypocenter locations determined in block 415. In the illustrated
embodiment, loop 420 is indexed by the variable i, which may be
referred to as the loop counter. The loop counter i starts at i=1
on the first iteration of loop 420 and is incremented by one for
each iteration of loop 420 until i=p at which point, method 400
exits from loop 420. Loop 420 commences in block 425 which involves
selecting the i.sup.th potential hypocenter location and
determining the distances x.sub.i between the i.sup.th potential
hypocenter location and the locations of the block 410 sensors. It
will be appreciated that where the block 410 group of sensors
includes N sensors, then the quantity x.sub.i will be a 1.times.N
vector quantity having the form [x.sub.1.sub.--.sub.i,
x.sub.2.sub.--.sub.i . . . x.sub.N.sub.--.sub.i].sup.T where the
notation x.sub.j.sub.--.sub.i indicates the distance between the
j.sup.th sensor and the i.sup.th potential hypocenter location.
Once the location of the i.sup.th potential hypocenter location is
known, the distances x.sub.i may then be determined based on
pre-calibrated or otherwise known locations of the sensors of
system 10.
[0121] Method 300 then proceeds to block 430 which involves
determining model parameters. In one particular embodiment, the
spatial attenuation of the PPV of a rock fall event is modeled
according to an exponential decay model:
y(x)=Ae.sup.-Bx (6)
where: y(x) represents the PPV amplitude at a distance x from the
hypocenter of the rock fall event, A is a model parameter
representative of the PPV at the hypocenter and B is an absorption
coefficient model parameter which may be representative of a
quality factor of the substrate in the region of track section 12.
In accordance with the above described notation, model equation (6)
may be rewritten as:
y.sub.j_=A.sub.ie.sup.-B.sup.i.sup.x.sup.j_i (7)
where: y.sub.j.sub.--.sub.i is the expected PPV of the j.sup.th
sensor based on a rock fall at the i.sup.th potential hypocenter,
x.sub.j.sub.--.sub.i is the distance between the j.sup.th sensor
and the i.sup.th potential hypocenter location, A.sub.i is a model
parameter representative of the PPV at the i.sup.th potential
hypocenter and B.sub.i is an absorption coefficient model parameter
for the i.sup.th potential hypocenter which may be representative
of a quality factor of the substrate in the region of track section
12. In other embodiments, other models may be used for the spatial
attenuation of the PPV.
[0122] Block 430 then involves solving for the model parameters by
minimizing a cost function. In embodiments which make use of the
model of equations (6) and (7), the model parameters to be
determined in block 430 are the quantities A.sub.i and B.sub.i. It
will be appreciated that in embodiments which use other attenuation
models, the model parameters to be determined may be different. In
one particular example embodiment, the cost function used in block
430 is a least squares cost function which, for the i.sup.th
potential hypocenter location, may be given by:
F i = j = 1 N w j_i ( r j_i ) 2 = j = 1 N w j_i ( y j - A i - B i x
j_i ) 2 ( 8 ) ##EQU00004##
where: F.sub.i is the cost function for the i.sup.th potential
hypocenter location, N is the number of block 410 sensors,
w.sub.i.sub.--.sub.j is an optional weighting coefficient for the
j.sup.th sensor and the i.sup.th potential hypocenter location,
y.sub.j is the actual sensor PPV for the j.sup.th sensor and the
quantity
r.sub.j_i=y.sub.j-A.sub.ie.sup.-B.sup.i.sup.x.sub.j-i
is referred to as the residual for the j.sup.th sensor and the
i.sup.th potential hypocenter location.
[0123] The cost function of equation (8) can be minimized when:
.differential. F i .differential. A = 0 and ( 9 a ) .differential.
F i .differential. B = 0 ( 9 b ) ##EQU00005##
Equations (9a) and (9b) can be solved for the i.sup.th potential
hypocenter location to yield the parameters A.sub.i and
B.sub.i.
[0124] Solving equations (9a) and (9b) is a non-linear problem
which may be simplified (e.g. linearized) by taking the natural
logarithm of both sides of equation (7) to yield:
ln(y.sub.j.sub.--.sub.i)=ln(A.sub.i)-B.sub.ix.sub.j.sub.--.sub.i=y'.sub.-
j.sub.--.sub.i=A'.sub.i-B.sub.ix.sub.j.sub.--.sub.i (10)
where y'.sub.j.sub.--.sub.i=ln(y.sub.j.sub.--.sub.i) and
A'.sub.i=ln(A.sub.i). Equation (10) represents a linear regression
model (as opposed to the exponential regression model of equation
(7) and may be used to create a least squares cost function, which
in turn may be minimized to yield the quantities A'.sub.i and
B.sub.i. The parameter A.sub.i may then be obtained according to
A.sub.i=e.sup.A'.sup.i. Minimizing a least squares cost function
for a linear regression model has a closed form solution and is
well understood by those skilled in the art.
[0125] Examining the equation (6) model more closely, it can be
seen that the quantity A depends on the amplitude of an event
wavelet at the hypocenter and therefore varies from event to event.
In contrast, the quantity B represents a quality factor which may
depend on the geotechnical characteristics of the environment
around track section 12. It would be expected, therefore, that the
quantity B is relatively constant from event to event. The
inventors have experimentally determined (using the least squares
curve fitting techniques described above) that the parameter B for
a particular track section 12 typically stays within 5-10% of some
average value B.sub.o. Accordingly, in some embodiments, the
quantity B.sub.o may be determined during calibration and
thereafter the parameter B.sub.i may be taken as a constant
B.sub.i=B.sub.o. In such embodiments, equations (8) and (9a) may be
used to solve for A.sub.i, which is given by:
A i = j = 1 N w j_i y j - B 0 x j_i j = 1 N w j_i - 2 B 0 x j_i (
11 ) ##EQU00006##
[0126] For the equation (6) attenuation model, at the conclusion of
block 430, method 400 has determined the model parameters A.sub.i
and B.sub.i for the i.sup.th potential hypocenter location. For
other models, block 430 may yield different model parameters for
the i.sup.th potential hypocenter location. Method 400 then
proceeds to block 435 which involves determining an error metric
associated with the i.sup.th potential hypocenter location. In
general, the block 435 error metric may be any suitable quantity
that is representative of the error associated with the model
parameters determined in block 430 for the i.sup.th potential
hypocenter. The block 435 error metric may involve a summation of
constituent error metrics over the N block 410 sensors. Each
constituent error metric may involve a difference between the PPV
predicted by the loop 420 model and the PPV measured by the sensor.
In embodiments which make use of a least squares cost function
(e.g. equation (8)), the block 435 error metric (E.sub.i) may
comprise a sum of the squares of the residuals for the i.sup.th
potential hypocenter location over the N block 410 sensors which
may be given by:
E i = j = i N ( r j_i ) 2 ( 12 ) ##EQU00007##
in embodiments which make use of the regression model of equation
(7), equation (12) becomes:
E i = j = 1 N ( y j - y j_i ) 2 ( 13 ) ##EQU00008##
where y.sub.j is the PPV value measured at the j.sup.th sensor and
y.sub.j.sub.--.sub.i is the PPV value predicted by model equation
(7) for the j.sup.th sensor and the i.sup.th potential hypocenter
location.
[0127] Once the block 435 error metric (E.sub.i) is determined,
method 400 proceeds to block 440 which involves evaluation of a
loop exit condition. If there are other potential hypocenter
locations in the block 415 group of p potential hypocenter
locations which have yet to be examined, then the block 440 inquiry
is negative (NO output) and method loops back to block 450, where
the loop counter i is incremented to refer to the next potential
hypocenter location and then back to block 425. If the procedures
of blocks 425, 430 and 435 have been performed for all of the block
415 group of p potential hypocenter locations, then the block 440
loop exit condition is fulfilled (YES output) and method 400
proceeds to block 445.
[0128] Block 445 involves selecting one of the block 415 group of
potential hypocenters to be the estimated hypocenter of the rock
fall event. In the illustrated embodiment, the block 445 selection
is based on the hypocenter having the lowest error metric (as
determined in block 435). In embodiments which use the error metric
of equation (12) or (13), block 445 involves selecting the
estimated location of the hypocenter of the rock fall event to be
the potential hypocenter having the lowest value of E.sub.i.
[0129] In other embodiments, method 400 may be implemented with a
different attenuation model. For example, in one alternative
embodiment, the attenuation model of equation (6) may be replaced
by the following model:
y ( x ) = A - Bx x ( 14 ) ##EQU00009##
which represents a combination of absorption and geometric
spreading. For the equation (14) model, the equivalent to equation
(7) is:
y j_i = A i - B i x j_i x j_i ( 15 ) ##EQU00010##
the least squares cost function equivalent to equation (8) is:
F i = j = 1 N w j_i ( r j_i ) 2 = j = 1 N w j_i ( y j - A i - B i x
j_i x j_i ) 2 ( 16 ) ##EQU00011##
and the residual r.sub.j.sub.--.sub.i for the j.sup.th sensor and
the i.sup.th potential hypocenter location is given by:
r j_i = y j - A i - B i x j_i x j_i ( 17 ) ##EQU00012##
Equation (15) may be linearized by taking the natural logarithm of
both sides to obtain:
ln(y.sub.j.sub.--.sub.i=ln(A.sub.i)-B.sub.ix.sub.j.sub.--.sub.i-1/2
ln(x.sub.j.sub.--.sub.i)=y'.sub.j.sub.--.sub.i=A'.sub.i-B.sub.ix.sub.j.su-
b.--.sub.i-1/2 ln(x.sub.j.sub.--.sub.i) (18)
where y'.sub.j.sub.--.sub.i=ln(y.sub.j.sub.--.sub.i) and
A'.sub.i=ln(A.sub.1). When the assumption is made that
B.sub.i.apprxeq.B.sub.0, equations (9a) and (16) may be used to
solve for A.sub.i according to:
A i = j = 1 N w j_i y j - B 0 x j_i x j_i j = 1 N w j_i - 2 B 0 x
j_i x j_i ( 19 ) ##EQU00013##
[0130] The variables used in the model of equations (14)-(19) may
have substantially the same meaning as those used in the above
described model based on equations (6)-(11). Method 400 using the
model set out in equations (14)-(19) may be similar to method 400
described above using the model of equations (6)-(11). More
particularly, block 430 may involve determining the model
parameters A.sub.i and B.sub.i for the i.sup.th potential
hypocenter, block 435 may involve determining an error metric
associated with the i.sup.th potential hypocenter (e.g. using
equations (12) and (13), except that the equation (15) regression
model is used in the place of the equation (7) regression model)
and the remainder of method 400 may be substantially similar to
that described above.
[0131] As discussed above, method 400 (FIG. 7C) represents one
possible embodiment of block 270 of method 200 (FIG. 7A). The block
445 estimated hypocenter location may be the output of the block
270 event location estimation. In other embodiments, block 270 may
be implemented by other methods. Returning to FIG. 7A, at the
conclusion of block 270 (e.g. method 400), method 200 may proceed
to optional block 275 which involves estimating an event energy.
The optional block 275 energy estimation may also be based on the
spatial attenuation model used in block 270 to estimate the rock
fall hypocenter. In the particular exemplary embodiment described
in method 400 above, the spatial attenuation model is represented
by equations (6) and (7).
[0132] If it is assumed that the trajectory associated with a
falling rock is predominantly vertical, then the rock's kinetic
energy may be expressed as:
KE=mhg (14)
where: m is the mass of the rock, h is the height from which the
rock falls and g is the acceleration due to gravity. The block 275
energy estimation may also involve the assumption that the PPV of a
rock fall event at the hypocenter is proportional to the kinetic
energy KE:
KE=kA (15)
where: A is the value of the model parameter A.sub.i determined in
block 430 and associated with the hypocenter selected in block 445
and k is a constant of proportionality which may be determined
experimentally during calibration of system 10. The block
determination of the energy associated with a rock fall event may
be determined using equation (15).
[0133] Method 200 (FIG. 7A) proceeds to block 280 which involves
taking the appropriate action for a rock fall event. FIG. 7D
schematically illustrates a method 500 for taking appropriate
action in respect of a rock fall event which may be performed in
block 280 according to a particular embodiment. Method 500
commences in optional block 510 which involves obtaining one or
more images of the estimated event location. The estimated event
location may be the event location determined in block 270 (e.g.
the block 445 hypocenter). Optional block 510 may involve
controlling one or more image capturing devices 34 using camera
control signals 38. Image capturing device(s) 34 may be controlled,
so as to direct them toward the estimated event location and to
capture corresponding image data 36. As discussed above, image data
36 may be stored in image data memory 130.
[0134] Method 500 then proceeds to block 515 which involves
generating a record of the rock fall event. The record of the rock
fall event may include recordal of a number of parameters
associated with the rock fall event. In particular embodiments, the
block 515 record may include one or more of: the event type (e.g. a
block 395 or block 390 rock fall event); a number of triggered
sensors; a number N of block 410 sensors; start and end times of
the event which may include the start time (t.sub.start) for the
first triggered sensor and the end time (t.sub.end) for the last
sensor to remain triggered; the estimated location of the
hypocenter of the event (e.g. the block 445 hypocenter); the
estimated PPV of the event at the hypocenter (e.g. the value of the
model parameter A.sub.i determined in block 430 for the hypocenter
selected in block 445); the estimated event energy (e.g. the block
275 energy); the PPV and the associated time t.sub.PPV for each
triggered sensor; the maxima (and associated times) of one or more
other block 305 event specific parameters (e.g. STA/LTA.sub.max,
E.sub.max or the like); and any other parameter of which data
processor 120 may be aware. In some embodiments, the block 515
record may also include one or more of the block 510 images of the
estimated event location.
[0135] Method 500 then proceeds to block 520 which involves
evaluation of an alarm criteria. In one particular embodiment, the
block 520 alarm criteria may involve comparison to determine
whether the estimated PPV of the event at the hypocenter (e.g. the
value of the model parameter A.sub.i determined in block 430 for
the hypocenter selected in block 445) is greater than a PPV alarm
threshold (thresh_PPV_alarm). In other embodiments, the block 520
alarm criteria may involve comparison to determine whether the
estimated event energy (e.g. the block 275 energy) is greater than
an energy alarm threshold (thresh_KE_alarm). It will be
appreciated, based on equation (15) above, that in the
above-described embodiment, these two block 520 alarm criteria are
equivalent and related by the experimentally determined scaling
factor k. In some embodiments, the block 520 alarm criteria may be
additionally or alternatively based on inquiries into one or more
other parameter(s) measured or estimated by system 10.
[0136] If the block 520 inquiry is positive (e.g. the estimated PPV
of the event at the hypocenter is greater than a PPV alarm
threshold (thresh_PPV_alarm)), then method 500 proceeds along the
block 520 YES output to block 530 which may involve triggering an
alarm and/or transmitting the block 515 event record directly back
to an offsite location (e.g. via network connection 28 to remote
workstation 30). The block 530 alarm may involve triggering sensory
stimulus at remote workstation 30 and/or an email at remote
workstation 30 or the like. In some embodiments, when the block 530
alarm is received at remote workstation 30, the block 515 event
record (including any block 510 images) may be evaluated by human
personnel. If the event is determined by human personnel to be
worthy of service disruption, then vehicular traffic may be
prevented from traveling on track section 12 until the event is
investigated more thoroughly and/or cleared. In some embodiments,
human intervention may not be desired or required and the block 530
alarm may cause a communication to be directed to rail vehicle
operators to alert them to the event and to cause them to stop
traveling on or toward track section 12.
[0137] If the block 520 inquiry is negative (e.g. the estimated PPV
of the event at the hypocenter is less than the PPV alarm threshold
(thresh_PPV_alarm)), then method 500 proceeds along the block 520
NO output to block 540. In the illustrated embodiment, block 540
involves transmitting and/or logging the event in the normal course
(i.e. without triggering an alarm). Block 540 may involve storing
the block 515 event record in local memory (e.g. in data logger
110, memory 128 and/or image data memory 130) until such time as
signal processing unit 26 is polled for events (e.g. by remote
workstation 30 over network connection 28). Depending on the
availability of local memory, in other embodiments, block 540 may
involve transmitting the block 515 event record (e.g. to remote
workstation 30 over network connection 28) without triggering an
alarm.
[0138] FIG. 10 schematically illustrates a method 600 for
deployment of system 10 according to an example embodiment. Method
600 commences in block 610 which involves assessing geotechnical
characteristics of the environment in the vicinity of track section
12. Block 610 may involve simulating rock fall events using drops
of known weights from known heights at known locations (i.e. test
drops). Block 610 may involve using portable sensor arrays (similar
to sensor arrays 18) and portable signal processing units 26. Block
610 may involve assessing one or more of: [0139] ambient noise
characteristics (including, by way of non-limiting example,
characterizing noise from sources such as waterfalls, running water
sources, winds, nearby traffic and other sources of surface noise);
[0140] the surface wave velocity in the substrate in a vicinity of
track section 12; [0141] the soil quality factor (e.g. the
parameter B.sub.o described above); [0142] the accuracy range of
block 270 location estimation method (e.g. the inventors have
determined that the accuracy of method 400 may decrease with
distance between the sensors and the rock fall location); [0143]
assessing an amount of data scattering resulting from acoustic
energy crossing an obstacle (e.g. the track in circumstances where
sensors are installed on both sides of track section 12 or if track
section 12 has curvature) and, if significant energy loss takes
place, then determining that data from "shadowed" sensors should
not be mixed with the remaining sensors; [0144] verifying that gain
settings associated with signal conditioning circuitry 102 are
suitable to capture events in a range of interest; and/or [0145]
the like.
[0146] Method 600 then proceeds to block 620 which involves using
the block 610 information to determine a sensor density for system
10 and determining the associated system layout. Block 620 may
involve comparing PPVs associated with test drops at various
distances against background noise. In some embodiments, the PPV of
the smallest event necessary to be detected should be greater than
3 times the background noise level. In other embodiments, this
ratio is 4-5 times.
[0147] Method 600 then proceeds to block 630 which involves
installing system 10 in accordance with the block 620 layout. The
portable sensor arrays and portable signal processing units may be
replaced by permanent sensor arrays 18 and signal processing unit
26. Some of the geotechnical parameters determined in block 610 may
be reassessed using the permanent system components. Optional block
640 may involve determining a background noise level (and an
associated LTA constant c) which may be used, in some embodiments,
to compute the modified STA/LTA ratio in accordance with equation
(3) described above.
[0148] Method 600 then proceeds to block 650 which involves testing
system 10 by running system 10 for a period of time sufficient to
capture all types of detectable events. System 10 (and in
particular software 124 used by data processor 120) may be adjusted
as needed during this testing period to optimize performance.
Digital sensor signals associated with particular events may be
recorded so that they can be used again to evaluate changes to
software 124. In block 660, system 10 is commissioned to operate,
but is subject to regular routine testing and recalibration as
desired.
[0149] In some embodiments, it may be desirable to attempt to
discriminate events (and/or series of events) caused by human(s)
and/or other animal(s) from events caused by rock fall, rail
traffic and/or other source of surface noise. FIG. 11 illustrated a
method 700 which may be used to discriminate a series of events
that may be caused by a human or other animal according to a
particular embodiment. Method 700 may be performed in method 200
(FIG. 7A) after the detection of an event. For example, method 700
may be performed between blocks 260 and 265 and/or between blocks
275 and 280.
[0150] Method 700 commences in block 710 which involves determining
a temporal correlation between the current event and the previous M
events. The parameter M may be based on empirical evidence and may
depend on sensor sensitivity, the importance of detection of
animals in the vicinity of track section 12 or the like. The block
710 temporal correlation may be determined using a wide variety of
techniques known to those skilled in the art. One such technique
involves determining a mean time of the last M events (e.g. the
mean start time t.sub.start of the last M events) and comparing the
time that is furthest from the mean time with the mean time. In
accordance with this technique, a large difference indicates a
fairly weak temporal correlation and a small difference indicates a
fairly strong temporal correlation. Another technique involves
computing the statistical standard deviation .sigma. of the times
of the last M events (e.g. the mean start times t.sub.start of the
last M events). In accordance with this technique, a large
deviation indicates a relatively weak temporal correlation, whereas
a small deviation indicates a relatively strong temporal
correlation.
[0151] If the block 710 inquiry indicates that the temporal
correlation of the last M events is less than a threshold
(temp_corr_thresh), then method 700 may proceed along the block 710
NO output to block 720 where method 700 concludes that the series
of events were not produced by an animal. If, on the other hand,
the block 710 inquiry indicates that the temporal correlation of
the last M events is greater than a threshold (temp_corr_thresh),
then method 700 may proceed along the block 710 YES output to block
730. Block 730 involves evaluation of a spatial correlation of the
last N events. In some embodiments, the block 710 number of events
M is equal to the block 730 number of events N. The block 730
spatial correlation may be determined on the basis of the event
locations determined in block 270 (e.g. method 400), for example.
The block 730 spatial correlations may be determined using any of a
large variety of techniques known to those skilled in the art,
including those described above for block 710.
[0152] If the block 730 inquiry indicates that the spatial
correlation of the last N events is less than a threshold
(spat_corr_thresh), then method 700 may proceed along the block 730
NO output to block 740 where method 700 concludes that the series
of events were not produced by an animal. If, on the other hand,
the block 730 inquiry indicates that the spatial correlation of the
last N events is greater than a threshold (spat_corr_thresh), then
method 700 may proceed along the block 730 YES output to block 750,
which involves concluding that the series of events was most likely
caused by human(s) or other animal(s).
[0153] Where track section 12 is located in a region having
relatively large amounts of active train traffic and/or highrail
vehicular traffic, there is a relatively high likelihood of false
positive events related to such active train and/or highrail
vehicular traffic. In addition to trains and highrail vehicles that
are moving at regular speed through such active regions, such
active regions may be associated with relatively large amounts of
"cultural noise". Such cultural noise may include, by way of
non-limiting example, slow or stationary trains or highrail
vehicles, movement of track maintenance personnel and/or equipment,
site excavation and/or construction work and the associated
movement of personnel and/or equipment, right-of-way maintenance
and/or the like. In such active regions (or in any other regions),
it may be desirable to include additional sensors, additional
processing techniques and/or other additional techniques to
minimize (to the extent possible) the detection of false positive
events.
[0154] In some embodiments, system 10 may include particular
vehicle detection sensors for detecting slow moving and/or
stationary trains, slow moving and/or stationary highrail vehicles
and/or other vehicles operating in a vicinity of such active track
sections. Such vehicle detection sensors may include, for example,
magnetometers which may be mounted directly to track section 12
and/or at a distance (e.g. 0.5 m-2.5 m) away from track section 12
(e.g. in ballast 52), ultrasound vehicle sensors, optical (e.g.
infrared) vehicle sensors and/or the like. Magnetometers may sense
the presence of iron or other magnetic materials associated with
trains and/or highrail vehicles. Ultrasound vehicle sensors may
sense the presence of trains and/or highrail vehicles using
reflected acoustic energy. Optical vehicle sensors may sense the
presence of trains and/or highrail vehicles by sensing the
interruption of an optical (e.g. infrared) beam.
[0155] Vehicle detection sensors which may be incorporated into
system 10 to detect slow moving and/or stationary trains, slow
moving and/or stationary highrail vehicles and/or other vehicles
operating in a vicinity of track section 12 may additionally or
alternatively include magnetic wheel detectors of the type used in
the rail industry to trigger so-called "hot box" detectors. Such
wheel detectors may be mounted directly to track section 12 (e.g.
tracks 54 and/or ties 56). As their name implies, magnetic wheel
detectors may be used to detect the wheels of trains and highrail
vehicles.
[0156] A typical rail car may have a total of eight wheels (four on
each side) with four (two on each side) near the front of the car
and four (two on each side) near the rear of the car. FIG. 12A
schematically depicts a typical signal 800 emitted from a magnetic
wheel detector in response to the passage of a single car of a
train over the wheel detector (after suitable amplification and
optional temporal filtering (e.g. smoothing to reduce high
frequency noise)). It can be seen from FIG. 12A that signal 800
exhibits four spikes A, B, C, D. Spikes A, B, C, D correspond to
the detection of the four rail car wheels (on one side of the car)
by the wheel detector magnet. The first two spikes A, B of signal
800 are associated with the two wheels near the front of the car
and the second two spikes C, D are associated with the two wheels
near the rear of the car (assuming that the car is moving
forwardly).
[0157] The wheel detector signal for a highrail vehicle (having the
same wheel pattern as the rail car associated with the FIG. 12A
signal) may exhibit a shape similar to that of signal 800, except
that the time between the first two spikes and the second two
spikes may be less because of the relatively short distance between
the front wheels and rear wheels of a highrail vehicle. For train
cars or highrail vehicles with different wheel patterns, signal 800
may have a different signature. However, there will typically be an
identifiable signal feature (e.g. a spike or the like) associated
with each wheel (on one side) of the car/vehicle. FIG. 12B
schematically shows a typical signal 802 emitted from a magnetic
wheel detector in response to the passage of a train having
multiple cars (after suitable amplification and optional temporal
filtering (e.g. smoothing to reduce high frequency noise)). It can
be seen from signal 802 that a feature pattern or signature similar
to that of signal 800 (FIG. 12A) is repeated for each car in the
train.
[0158] In some embodiments, system 10 may comprise one or more
vehicle detection sensors (e.g. magnetometers, ultrasound vehicle
sensors, optical vehicle sensors, wheel detectors and/or the like)
which may be used to detect the presence of a train or a highrail
vehicle on track section 12 and/or to discriminate whether a
detected/triggered event is a train or highrail vehicle on track
section 12. Such vehicle detection sensors may be provided as part
of one or more corresponding sensor arrays 18 and may provide
corresponding vehicle detection information 22 to signal processing
unit 26 over transmission lines 24. Alternatively, such vehicle
detection sensors could be provided independently of sensor arrays
18 and may independently communicate with signal processing unit
26. Vehicle detection information received from vehicle detection
sensors may be handled by signal processing unit 26, DAU 116 and/or
data logger 110 in the same or similar manner as other sensor
information 22 discussed herein. Data from vehicle detection
sensors may be logged during a time period when an event is
triggered (e.g. between the times of a block 225 YES output and a
block 245 YES output (FIG. 7A)) or may be logged continually. Once
stored in data logger 110 or DAU 116, information from vehicle
detection sensors can be processed by data processor 120 to
generate other forms of processing parameters 150. Some or all of
these processing parameters 150 may be used in turn to detect the
presence of a train or a highrail vehicle on track section 12
and/or to discriminate whether a detected/triggered event is a
train or highrail vehicle on track section 12 (e.g. as a part of
block 255 and/or method 300).
[0159] A number of exemplary embodiments incorporating wheel
detector type vehicle sensors are now described. It will be
appreciated that in many instances, other types of vehicle
detection sensors could be used in addition to, or as alternatives
to, wheel detectors with suitably appropriate modifications of the
exemplary embodiments described herein.
[0160] Signals from one or more wheel detectors may be subjected to
an amplitude thresholding criteria to detect the presence of a
train or a high rail vehicle on track section 12. Referring to FIG.
12B, if it is determined that the absolute value of a wheel
detector signal is greater than a threshold value wheel_thresh,
then it may be concluded that there is a train or highrail vehicle
overtop of the wheel detector at that time or that a triggered
event was the result of a train or highrail vehicle. In some
embodiments, system 10 may record or otherwise observe a time
associated with each instance that the wheel detector signal
crosses the threshold wheel_thresh. In some embodiments, system 10
may record or otherwise observe only times associated with the
absolute value of the wheel detector signal crossing the threshold
wheel_thresh in a particular direction (e.g. from below the
threshold to above the threshold or vice versa). In some
embodiments, the value of wheel_thresh may be low or even zero,
since a typical wheel detector is not particularly noisy or
susceptible to false positive events.
[0161] Such a thresholding criteria may be incorporated into the
post event processing of block 255 and/or method 300 described
above. For example, if it is determined that the absolute value of
a wheel detector signal was greater than the threshold wheel_thresh
at any time during the event period (e.g. between t.sub.start and
t.sub.end), then block 255/method 300 may conclude that the event
was a train or high rail vehicle. As another example, if it is
determined that the absolute value of a wheel detector was greater
than the threshold wheel_thresh a number of times during the event
period (e.g. between t.sub.start and t.sub.end), but that this
number of times was less than a threshold number of times
wheel_num_thresh, then block 255/method 300 may conclude that the
event was a highrail vehicle as opposed to a train.
[0162] In addition to detecting the presence of a highrail vehicle
or train, system 10 may use a plurality of wheel detectors spaced
apart from one another by known distances to estimate the direction
and/or speed of a passing vehicle. For example, where two wheel
detectors are spaced apart from one another by a known distance D,
the direction of travel of the passing vehicle may be determined by
detecting which wheel detector signal leads the other and an
estimate of the speed of the train or the highrail vehicle may be
determined by dividing the wheel detector separation distance D by
the temporal difference between corresponding features of the wheel
detector signals at the two wheel detectors. FIG. 12C schematically
depicts wheel detector signals 804A, 804B associated with the
passage of a highrail vehicle over a pair of wheel detectors and
the manner in which temporal differences between corresponding
features of wheel detector signals 804A, 804B may be used to
determine the direction and speed of a vehicle. It can be seen from
FIG. 12C, that signal 804A leads signal 804B. System 10 may
therefore determine that the train or highrail vehicle that
generated signals 804A, 804B was moving from the direction of the
sensor associated with signal 804A toward the direction of the
sensor associated with signal 804B. FIG. 12C exhibits a temporal
difference .DELTA.t.sub.1 between corresponding spikes 806A, 806B
of wheel detector signals 804A, 804B. The speed of a train or
highrail vehicle may be estimated to be
v 1 = D .DELTA. t 1 = D ( t 1 B - t 1 A ) , ##EQU00014##
where t.sub.1A may be the time that signal 804A crosses the
threshold wheel_thresh at peak 806A and t.sub.1B may be the time
that signal 804B crosses the threshold wheel_thresh at peak
806B.
[0163] In addition to the first temporal difference .DELTA.t.sub.1
between corresponding first spikes 806A, 806B of wheel detector
signals 804A, 804B, FIG. 12C exhibits the determination of a
subsequent temporal difference .DELTA.t.sub.n between corresponding
subsequent spikes 808A, 808B of wheel detector signals 804A, 804B.
System 10 may estimate the speed of a train or highrail vehicle at
a subsequent time associated with the n.sup.th corresponding
features of wheel detector signals 804A, 804B to be
v n = D .DELTA. t n = D ( t nB - t nA ) , ##EQU00015##
where t.sub.nA may be the time that signal 804A crosses the
threshold wheel_thresh at peak 808A and t.sub.nB may be the time
that signal 804B crosses the threshold wheel_thresh at peak
808B.
[0164] In some embodiments, system 10, may estimate a speed for all
corresponding features of wheel detector signals 804A, 804B (e.g.
every time the absolute value of wheel detector signals 804A, 804B
both cross the threshold wheel_thresh), for all corresponding
features of wheel detector signals 804A, 804B when wheel detector
signals 804A, 804B cross the threshold wheel_thresh in a certain
direction (e.g. every time the absolute value of wheel detector
signals 804A, 804B both cross from below, to above, the threshold
wheel_thresh), or for every number of corresponding features of
wheel detection signals 804A, 804B (e.g. every k.sup.th
corresponding feature) of wheel detector signals 804A, 804B. The
number k of features between speed estimates can be a configurable
parameter of system 10 and may depend on available processor
resources. In some embodiments, data from wheel detectors may be
logged and corresponding speeds may be estimated only after an
event has been triggered (e.g. after the block 225 inquiry results
in a YES output (FIG. 7A) or after t.sub.start). The correspondence
between features of the signals 804A, 804B associated with a pair
of wheel detectors may be maintained by suitable indices which may
be incremented each time that a threshold crossing event (e.g. a
wheel detection signal 804A, 804B crosses wheel_thresh) is recorded
for that wheel detection signal 804A, 804B.
[0165] In some embodiments, the estimated speeds v.sub.n (or the
determined temporal differences .DELTA.t.sub.n) at different times
may be processed (e.g. integrated or differentiated) to determine
an estimated position and/or acceleration/deceleration of a train
or highrail vehicle. Such information may be used to estimate the
location that a train or highrail vehicle comes to rest on track
section 12. Some systems may incorporate more than two wheel
detectors. In such systems, velocity, position and/or acceleration
estimates based on different sensor pairs can be combined (e.g.
averaged) to determine a better estimate of the vehicle velocity,
position and/or acceleration. Also, velocity differences between
different sensor pairs could be used as another technique for
estimating acceleration. For example, if sensor A is spaced apart
from sensor B by a distance D and sensor B is spaced apart from
sensor C by a distance D, and it is determined that the temporal
difference .DELTA.t.sub.AB between the sensor A and B signals for a
particular feature is greater than the temporal difference
.DELTA.t.sub.BC between the sensor B and C signals for the same
particular feature, then it may be concluded that the vehicle is
accelerating as it moves from sensor A to B to C.
[0166] In some embodiments, system 10 may detect the presence of
trains and/or highrail vehicles on track section 12 using the
cross-correlation of signals from multiple sensors of the same type
which are spaced apart from one another by known distances D.
Sensors which may be used for this cross-correlation process may
include ballast sensors (e.g. acoustic ballast sensors 50 of the
type described above), rail sensors (e.g. rail sensors 80 of the
type described above), wheel detectors and/or the like.
Cross-correlation data may be generated for time periods when an
event is triggered (e.g. between the times of a block 225 YES
output and a block 245 YES output (FIG. 7A)) or may be logged
continually. Once obtained, cross-correlation data can be processed
by data processor 120 to generate other forms of processing
parameters 150. Some or all of these processing parameters 150 may
be used in turn to detect the presence of a train or a highrail
vehicle on track section 12 and/or to discriminate whether a
detected/triggered event is a train or highrail vehicle on track
section 12 (e.g. as a part of block 255 and/or method 300).
[0167] FIG. 13 exhibits a typical cross-correlation waveform 820
associated with the signals from a pair of spaced apart ballast
sensors 50 when a train is moving (or has moved) over track section
12 at a relatively constant speed (after suitable amplification and
optional temporal filtering (e.g. smoothing to reduce high
frequency noise) of the signals from sensors 50). It can be seen
from FIG. 13, that cross-correlation waveform 820 exhibits a
reasonably sharp peak 822 at a time t.sub.CCmax.
[0168] In some embodiments, system 10 may determine the presence of
a train or a highrail vehicle on track section 10 when peak 822 of
cross-correlation waveform 829 is greater than a threshold value
CC_thresh and when the absolute value of the time t.sub.CCmax of
peak 822 occurs within a window between the times t.sub.1 and
t.sub.2. This determination may be a part of block 255/method 300,
although this is not necessary. The threshold CC_thresh may be
experimentally determined and may be a configurable parameter of
system 10. In some embodiments, the times t.sub.1 and t.sub.2 can
be configured to correspond to maximum and minimum expected speeds
of a train or highrail vehicle. For example, if a train is expected
to move with a maximum speed v.sub.max and it is known that the
sensors associated with cross-correlation signal 820 as spaced from
one another by a distance D, then t.sub.1 may be set to
t 1 = D v max ##EQU00016##
and the train is expected to move with a minimum speed v.sub.min,
then t.sub.2 may be set to
t 2 = D v min . ##EQU00017##
The times t.sub.1, t.sub.2 can also be experimentally determined
and configured parameters of system 10. The actual speed of a train
or highrail vehicle may be estimated from the time t.sub.CCmax
associated with the peak 822 of cross-correlation signal 820
according to
v = D t CCmax . ##EQU00018##
This variation of this speed estimate over time may be processed
(e.g. integrated or differentiated) to determine an estimated
position and/or acceleration/deceleration of a train or highrail
vehicle. Such information may be used to estimate the location that
a train or highrail vehicle comes to rest on track section 12. The
direction of travel of a train or highrail vehicle can also be
determined from the cross-correlation between two sensors, since
the time t.sub.CCmax associated with the cross-correlation peak 822
will be in the positive or negative half axis depending on the
direction of motion of the train or highrail vehicle.
[0169] In some embodiments, cross-correlation analysis may be
performed on a plurality of different sensor combinations (e.g.
combinations involving more than two sensors) before concluding
that an event is associated with a train or highrail vehicle (i.e.
that an event is not a rock fall). For example, it may be required
that each (or some percentage) of the cross-correlations of a
plurality of different sensor combinations exhibits a peak that is
greater than a threshold CC_thresh and occurs within a specified
temporal window before concluding that an event is a train or a
highrail vehicle. The cross-correlation of two or more different
sensor combinations can also be used to estimate the acceleration
of a vehicle. Consider again the example where sensor A is spaced
apart from sensor B by a distance D and sensor B is spaced apart
from sensor C by a distance D. If the time of the cross-correlation
peak (t.sub.CCmax) is greater for the cross-correlation of the
signals from sensors A and B than the time of the cross-correlation
peak (t.sub.CCmax) for the cross-correlation of the signals from
sensors B and C, then it can be concluded that the train/vehicle is
accelerating as it moves from A to B to C. As another example, if
the time of the cross-correlation peak (t.sub.CCmax) for the
signals from sensors A and C is less than twice the time of the
cross-correlation peak (t.sub.CCmax) for the signals from sensors A
and B, then it can be concluded that the train/vehicle is
accelerating as it moves from A to B to C.
[0170] In some embodiments, system 10 may include the ability for
all or part of system 10 to be temporarily shut-off in some
circumstances. For example, a signal may be sent to signal
processing unit 26 via network connection 28 (or otherwise
communicated to system 10) which causes system 10 (e.g. signal
processing unit 26) to temporarily disable (e.g. disregard
information received from) one or more of sensor arrays 18. This
temporary shut-off signal may be generated in any of a variety of
manners. By way of non-limiting example, if track maintenance,
right-of-way, excavation or construction personnel and/or equipment
will be working in a vicinity of a particular group of sensor
arrays 18, then: [0171] a shut-off signal for that group of sensor
arrays 18 may be communicated from a communication device (not
shown) connected to network connection 28 by such personnel (or by
other suitable personnel); [0172] optional cameras 34 may be motion
activated and may communicate video signal(s) (e.g. via network
connection 28) to a person who may determine whether a group of
sensors arrays 18 should be temporarily shut-off; [0173] a signal
may be communicated to system 10 from a GPS-enabled device carried
by such personnel or coupled to such equipment which may indicate
the location of the personnel or equipment and may thereby enable
system 10 to determine which sensor arrays 18 should be temporarily
disabled; and/or [0174] a signal from some other sensor or group of
sensors (e.g. a light-activated IR sensor, a radio frequency
identification (RFID) sensor and/or the like) may be communicated
to system 10. Such sensors may be strategically located to
indicated to system 10 which sensor arrays 18 should be temporarily
disabled.
[0175] In cases where system 10 is shut-off in whole or in part by
way of a manually generated signal, then it may be desirable to
have some automated technique for re-activating system 10 to avoid
such personnel accidentally leaving system 10 in a shut-off state.
By way of non-limiting example, such automated technique may
include: a temporal re-activation (e.g. system 10 reactivates after
a period (e.g. a user configurable period) of time; a sensor based
reactivation (e.g. system 10 reactivates when a light sensor
determines that it is dark, where a GPS-enabled device determines
that it is outside of a vicinity of track section 12 or the like);
an automated reminder to a suitable person to reactivate system 10
(e.g. communicated over network 28); and/or the like.
[0176] As will be apparent to those skilled in the art in the light
of the foregoing disclosure, many alterations and modifications are
possible in the practice of this invention without departing from
the spirit or scope thereof. For example: [0177] The particular
embodiments of the methods described above are exemplary in nature.
In other embodiments, portions of these methods my be modified or
changed. In some embodiments, aspects of these methods may be
performed in suitable orders other than the orders described above.
By way of non-limiting example, in some embodiments of method 300
(FIG. 7B), the procedures of blocks 360-390 may be performed before
the procedures of blocks 310-330 and/or blocks 335-355 or the
procedures of blocks 335-355 may be performed prior to the
procedures of blocks 310-330. Those skilled in the art will
appreciate that there are other circumstances in which the order of
particular operations may be changed in circumstances where this is
desirable. [0178] Method 300 of the illustrated embodiment
described above involves discriminating a variety of different
types of events (i.e. train events, highrail vehicle events,
surface noise events, insignificant rock fall events and
significant rock fall events. This is not necessary. In some
embodiments, it is desirable to discriminate a smaller number of
events (e.g. the two categories of significant rock fall events and
other events). In such embodiments, method 300 may be suitable
modified such that the block 310 NO output, optional block 335 YES
output, block 360 NO output and block 392 YES output all lead to
the same conclusion (i.e. other type of event) and method 300 may
conclude a significant rock fall event when the block 392 inquiry
is negative (i.e. block 392 NO output). In other such embodiments,
some of blocks 365-390 may be maintained to discriminate small rock
fall events or rock fall accumulation. [0179] In some embodiments,
method 200 and/or method 300 may be modified to provide an inquiry
into a minimum delay between events (.DELTA.event). Events which
occur at within a time (and/or number of samples) separation less
than the minimum delay .DELTA.event from one another may be
determined to belong to the same event. In particular embodiments,
such closely spaced events may be merged into a single event or one
or more of such closely spaced events may be ignored. [0180] In
some embodiments, method 200 and/or method 300 may be modified to
provide an inquiry into a minimum number of triggered sensors
(#_sensor_min). If the number of sensors triggered by an even is
less than this minimum number of sensors (#_sensor_min), then the
event can be determined to be too small to be of concern.
[0181] FIG. 9B described above makes use of a parameter
.DELTA.T.sub.start to determine the sensors to be included in the
block 410 group of sensors. This parameter .DELTA.t.sub.start or a
similar (possibly larger) temporal parameter may be used to
determine a maximum arrival time difference. If a first sensor is
triggered at a time t.sub.start.sub.--.sub.sensor#1 and one or more
sensors become triggered after this maximum arrival time
difference, then the subsequently triggered sensors can be
determined to belong to a separate event. The maximum arrival time
difference can be determined based at least in part of the
experimentally determined surface wave velocity in the substrate in
a vicinity of track section 12. [0182] Some of the above described
embodiments describe using an experimentally determined average
B.sub.0 for the model parameter B.sub.i of equation (7) and/or
equation (15). In some embodiments, this parameter B.sub.0 may be
the same for a particular track section 12, but may differ as
between each of a plurality of modular track sections 12 which may
be incorporated into a overall system or this parameter B.sub.0 may
vary locally within a track section (12). [0183] The methods
described above involve the discrimination of a number of events.
These events represent non-limiting examples of events that may be
discriminated by system 10. In other embodiments, system 10 may be
configured to discriminate other types of events, such as, by way
of non-limiting example: switch points being moved, locomotive
bells/horns and thermal expansion and the accompanying rail creep
atop the ties caused by solar heating of the rail. [0184] In some
embodiments, system 10 can exhibit one of two states--rock fall and
clear-to-pass. The rock fall state can indicate that system 10 has
detected an event that may be a rock fall and consequently a train
should not pass through track section 12 without taking
precautionary measures (e.g. slowing to a speed at which braking
may be effective, stopping and waiting for a crew to arrive to
investigate the event, stopping and waiting for the state of system
10 to enter the clear-to-pass state and/or the like. To be as safe
as possible, system 10 may default to the rock fall state. System
10 can raise and alarm or take other suitable action when it
determines that its state should be changed to rock fall. In some
embodiments, system 10 can be reset from a rock fall state to a
clear-to-pass state if a train passes through track section 12
without incident. For example, if system 10 is in a rock fall
state, then a precautionary measure that could be taken is for a
train to slow to a speed where the train could be safely brought to
a stop after visually sighting a rock fall event. If, however, the
train is able to pass through the site of the predicted rock fall
event without incident, then the state of system 10 may be reset to
clear-to-pass. [0185] In some embodiments (e.g. applications where
the reliability of system 10 is considered to be crucial), system
10 may be made redundant through use of redundant components. For
example, referring to FIG. 1, system 10 may be modified to include
redundant sensors arrays 18 (e.g. each individual sensor array 18
shown in FIG. 1 would be replaced by a plurality of redundant
sensors arrays 18, if one sensor array 18 were to fail, system 10
could revert to its redundant backup sensor array). By way of
non-limiting example, system 10 could also comprise redundant image
capture devices 34, transmission lines 24, signal processing units
26 and network connections 28. [0186] Optional image capturing
devices (e.g. cameras) 34 may be remotely controlled by a user via
network connection 28. In some embodiments, upon detection of a
rock fall event (or any other event) by system 10, system 10 and/or
a remote user may control image capture devices 34 to capture one
or more images of the event location. Images captured by image
capture devices 34 may be communicated over network connection 28
to a control center, where they may be reviewed by an operator. The
operator may then decide manually whether the event is a legitimate
rock fall event or whether the event is some other type of event.
[0187] The term acoustic is used throughout this description and
the accompanying claims. It will be appreciated that in the context
of this description and the accompanying claims, the term acoustic
should be understood to refer generally to mechanical and/or
vibrational energy which may travel through any medium. Acoustic
waves and acoustic sensors should be understood to refer generally
to waves which transfer this mechanical and/or vibrational energy
through any medium and sensors which detect this mechanical and/or
vibrational energy.
[0188] Accordingly, the scope of the invention should be determined
in accordance with the following claims.
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