U.S. patent application number 16/028743 was filed with the patent office on 2020-01-09 for method and system for monitoring a point system of a railway network, and point system of a railway network.
This patent application is currently assigned to ALSTOM TRANSPORT TECHNOLOGIES. The applicant listed for this patent is ALSTOM TRANSPORT TECHNOLOGIES, UNIVERSITY OF CINCINNATI. Invention is credited to Allegra ALESSI, Hossein DAVARI ARDAKANI, Pierre DERSIN, Wenjing JIN, Piero LA-CASCIA, Benjamin LAMOUREUX, Jay LEE, Michele PUGNALONI, Zhe SHI.
Application Number | 20200010101 16/028743 |
Document ID | / |
Family ID | 67180587 |
Filed Date | 2020-01-09 |
United States Patent
Application |
20200010101 |
Kind Code |
A1 |
ALESSI; Allegra ; et
al. |
January 9, 2020 |
METHOD AND SYSTEM FOR MONITORING A POINT SYSTEM OF A RAILWAY
NETWORK, AND POINT SYSTEM OF A RAILWAY NETWORK
Abstract
Method for monitoring at least one point system of a railway
network comprising the steps of placing several sensors in
correspondence of said at least one point system; acquiring from
said sensors current and voltage signals of a point machine during
a maneuver; and segmenting the signals of the maneuver according to
different predetermined phases of movement. Then extracting
predetermined features from each segment; comparing the extracted
features with a set of predetermined values which represent a
"healthy" maneuver, thus obtaining a global indicator
representative of the conditions of the maneuver at the point
system; and comparing said global indicator with a failure
threshold, and if it exceeds said failure threshold, detecting a
failure in the point system.
Inventors: |
ALESSI; Allegra;
(NOTTINGHAM, GB) ; LAMOUREUX; Benjamin; (PARIS,
FR) ; DERSIN; Pierre; (LOUVECIENNES, FR) ;
LEE; Jay; (MASON, OH) ; DAVARI ARDAKANI; Hossein;
(CINCINNATI, OH) ; SHI; Zhe; (COLOMBUS, IN)
; JIN; Wenjing; (HEBEI, CN) ; LA-CASCIA;
Piero; (BUDRIO, IT) ; PUGNALONI; Michele;
(JIESI, IT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ALSTOM TRANSPORT TECHNOLOGIES
UNIVERSITY OF CINCINNATI |
Saint-Ouen
CINCINNATI |
OH |
FR
US |
|
|
Assignee: |
ALSTOM TRANSPORT
TECHNOLOGIES
Saint-Ouen
OH
UNIVERSITY OF CINCINNATI
CINCINNATI
|
Family ID: |
67180587 |
Appl. No.: |
16/028743 |
Filed: |
July 6, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B61L 23/04 20130101;
B61L 7/08 20130101; B61L 27/0088 20130101; B61L 25/06 20130101;
B61L 5/107 20130101 |
International
Class: |
B61L 25/06 20060101
B61L025/06; B61L 23/04 20060101 B61L023/04 |
Claims
1. Method for monitoring at least one point system of a railway
network comprising the steps of: placing several sensors in
correspondence of said at least one point system; acquiring from
said sensors current and voltage signals of a point machine during
a maneuver; segmenting the signals of the maneuver according to
different predetermined phases of movement; extracting
predetermined features from each segment; comparing the extracted
features with a set of predetermined values which represent a
"healthy" maneuver, thus obtaining a global indicator
representative of the conditions of the maneuver at the point
system; and comparing said global indicator with a failure
threshold, and if it exceeds said failure threshold, detecting a
failure in the point system.
2. The method according to claim 1, further comprising: determining
library of vectors relative to degradation mechanisms, replicating
multiple degradation patterns of the point system; concatenating
the values of the extracted features into a vector, said values
representing the effect of a degradation of the point system; and
comparing the vector with the library of vectors to identify the
degradation mechanism.
3. The method according to claim 1, wherein the global indicator
corresponds to an aggregation of values obtaining through the
comparison of the extracted features with the set of predetermined
values.
4. The method according to claim 1, further comprising: evaluating
the global indicator to determine different degradation patterns
with different levels, comprising but not limited to misalignment,
obstacle, excessive force.
5. The method according to claim 1, further comprising the step of
concatenating the values of the extracted features into a vector,
said values representing the effect of a degradation of the point
system.
6. The method according to claim 1, wherein the step of comparing
comprises the steps of: normalizing the extracted features with
respect to a set of reference features, thus obtaining
corresponding indicator values; and combining said indicators to
obtain the global indicator.
7. The method of claim 6, wherein normalizing the extracted
features comprises: subtracting an original value taken from the
signal in the segment from a predetermined mean value and dividing
the result by a predetermined standard deviation value.
8. The method of claim 6, wherein combining said indicators
comprises calculating the Mahalanobis distance of said
indicators.
9. The method of claim 6, wherein combining said indicators
comprises calculating a distance such as a Mahalanobis distance
between subgroup of said indicators or applying a principal
component analysis algorithm to subgroup of said indicators or
calculating minimum quantization error from subgroup of said
indicators.
10. The method according to claim 1, wherein the extracted features
include the peak value of the signal, differentials of the signal,
average of the signal, mean of the signal, maximum of the signal,
the slope of the signal curve.
11. The method according to claim 1, wherein the predetermined
values which represent a "healthy" maneuver are values determined
through a machine learning process based on data relative to
previous maneuvers, from a point system of nominal status.
12. The method according to claim 4, wherein the step of evaluating
the global indicator comprising different levels of misalignment,
obstacles, excessive force.
13. A monitoring system for monitoring at least one point system of
a railway network comprising a point system of a railway network, a
plurality of sensors placed in correspondence with said point
system and an elaboration unit connected to said sensors and to the
point system and arranged to carry out the method according to
claim 1.
14. A point system of a railway network including a plurality of
sensors arranged to be connected to an elaboration unit arranged to
carry out the method according to claim 1.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to a method and a system for
monitoring a point system of a railway network, and to a point
system of a railway network
BACKGROUND
[0002] A point system comprises a point machine, some moving
appliances, two stock rails and two switch rails.
[0003] Point machines, also known as switch machines, are used to
operate track switches, also known as turnouts, that enable routing
of trains from one track to another and that comprise stock rails
and switch rails. These point systems tend to be relatively heavy
mechanical or hydraulic machines moving heavy steel appliances in
sometimes extreme conditions. Failure of a point system can cause
total blockage of a railway, since a safe route over a switch may
not be established for a train due to the failure.
[0004] These major events can cause delays to freight and passenger
trains, failure to meet schedules that often result in financial
penalties, additional costs of train crews and locomotive
operations, blockage of highway crossings and reputation loss to
customers.
[0005] Point systems are lubricated, adjusted, and otherwise
maintained on a periodic basis, but given the often remote location
of these systems, the maintainers may be unaware of the impact on
the track layout of the number of operations, weather, and changing
ground surface conditions, or of the maintenance operation
itself.
[0006] It is known to monitor the conditions of a point system, as
shown for example in document US 2015/0158511, wherein a waveform
of the operating characteristics is examined to identify or predict
a problem with the operations of the point system.
[0007] One problem of the method disclosed in the above indicated
document is that turnouts are generally inspected on a scheduled
basis, therefore, failures which severely affect the railway
traffic may happen in the time period between two inspections
without possibility to foresee them.
[0008] Also, the method needs a monitor and a processor device so
that the operator must be physically on the operating site and
manually trigger the different tasks to perform the point system
monitoring.
[0009] Another problem of the method disclosed in the above
indicated document is that the healthy reference is learnt from a
single movement (the first movement), therefore, the natural
variations of the waveforms are not included in this reference and
there is a significant risk to confuse environmental change with
real degradations, and thus to make false detection and
diagnosis.
SUMMARY
[0010] The technical problem to solve is therefore how to perform a
remote and automated detection of anomalies in a point system, a
remote and automated identification of a degradation in the
conditions of the point system and a remote and automated prognosis
of the remaining time before failure of a turnout.
[0011] An object of the present invention is therefore to provide a
method for monitoring a point system of a railway network which
allows to perform a remote and automated analysis of the conditions
of the point system in order to foresee anomalies and plan
maintenance interventions, which allows to make diagnosis of
degradation conditions of the point system itself and to identify
the time remaining before a next failure of the point system, thus
overcoming the limitations of the prior art systems.
[0012] These and other objects are achieved by a method for
monitoring a point system of a railway network having the
characteristics defined in claim 1.
[0013] Further subjects of the present invention are a system for
monitoring a point system of a railway network, and a point system
of a railway network as claimed.
[0014] Particular embodiments of the invention are the subject of
the dependent claims, whose content is to be understood as an
integral or integrating part of the present description.
[0015] Thanks to the fact that when a degradation appears in a
point system its kinematic behavior is different and the energy
consumption profile is affected, the method of the present
invention performs an anomaly detection, a degradation diagnosis
and a prognosis of a remaining useful life of the point system
itself by monitoring current and voltage signals supplied to the
point system during its maneuvers, and by following their evolution
in time.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Further characteristics and advantages of the present
invention will become apparent from the following description,
provided merely by way of a non-limiting example, with reference to
the enclosed drawings, in which:
[0017] FIG. 1 shows a schematic block diagram of the steps
performed by a method according to the present invention;
[0018] FIG. 2 is an example of a signal segmented according to
different phases of movement, for a single pushing point
electro-mechanical point machine with open-loop control;
[0019] FIG. 3 is an example of a signal segmented according to
different phases of movement, for a single pushing point
electro-mechanical point machine with closed-loop control;
[0020] FIG. 4 shows an interface for expressing a reference index
calculated according to a method of the present invention; and
[0021] FIG. 5 shows an alternative embodiment of the interface of
FIG. 4.
DETAILED DESCRIPTION
[0022] The method according to the present invention is based on
the acquisition of current and voltage signals supplied to a point
machine.
[0023] The signals are firstly segmented according to different
phases of a maneuver, then they are compressed into a set of
features that are physically relevant for assessing the condition
of the point system.
[0024] The analysis is performed for each point system of a railway
network and for each direction (Normal/Reverse) independently, this
being precise and robust to variability.
[0025] The core of the method of the present invention relies on
the processing done on the signals and on the way the degradations
are recognized.
[0026] The method of the present invention aims at assessing the
conditions of a railway turnout remotely, automatically and in
real-time, in order to provide the maintainers with useful and
precise information regarding the actual operation of the
corresponding point system. The method allows therefore the
maintenance to properly plan actions on the various point systems,
in particular only when necessary, to avoid service affecting
failures.
[0027] FIG. 1 shows a block diagram of the steps of a method
according to the present invention.
[0028] In a first step 2, several sensors per se known are placed
in correspondence with respective point systems. For example, the
sensors are placed in a manner known per se in an interlocking
equipment of an interlocking system, for example in the single
pushing point electro-mechanical point machine with open-loop
control configuration, or in the point system itself, for example
in the single pushing point electro-mechanical point machine with
closed-loop control configuration.
[0029] In a next step 4, for each point system, the sensors acquire
current and voltage signals of a turnout during a maneuver. These
signals are transferred, along with any relevant information
disclosed here below, to a processing unit placed in a remote cabin
along the railway track which segments, in a step 6, the signals of
the maneuver according to different predetermined phases of
movement, preferably unlocking of a locking system of the point
machine (or unlocking of an external locking system associated with
the point system), movement of the blades of the point system,
closing of the locking system, locking of the locking system.
[0030] Examples of relevant information transferred to the
processing unit include:
[0031] current supplied at the point system and used during the
maneuver;
[0032] voltage applied to the point system during the maneuver;
[0033] direction of the maneuver;
[0034] time taken to complete the maneuver;
[0035] detection of the position of the point system at the end of
the maneuver (confirmation that the maneuver has been completed
successfully);
[0036] point system actually doing the maneuver under analysis.
[0037] FIG. 2 and FIG. 3 show two examples of a current signal
segmented according to the different phases of movement,
respectively for a single pushing point electro-mechanical point
machine with open-loop control and for a single pushing point
electro-mechanical point machine with closed-loop control.
[0038] All the steps of the method of the present invention are
performed simultaneously on both the current and the voltage
signal, and the features extracted from these signals herein below
disclosed are combined for the final analysis, as below
detailed.
[0039] With regard to the segmentation step 6, it is worth pointing
out that the stroke of a slider has always a predetermined width,
for example 214 mm, hence whatever are the signal shape and the
time span for a maneuver, the slider will cover a stroke of such
width.
[0040] Starting from this stroke and considering that the internal
components of the point system do not move all at the same time,
the analysis has aimed to identify a correlation between parts of
the slider stroke and movement of internal components. At the end
of the analysis, the expected result was a stroke division into
segments associated to the movements of specific subset of
components, as herein below disclosed.
[0041] In FIG. 2, a first zone 100 corresponds to the moment when a
locking system of a point machine is unlocked, before a maneuver
starts. A second zone 102 corresponds to the movement of a first
switch rail. A third zone 104 corresponds to the movement of both
switch rails. A fourth zone 106 corresponds to the closing of the
first switch in a final position while the second switch rail
continues to move, and a fifth zone 108 corresponds to the locking
of the point system.
[0042] In FIG. 3, in a first zone 200 a maneuver starts at a
predetermined point system. In a second zone 202 there is a
movement of internal components of a locking system of the point
system to unlock the locking system itself. In a further zone 204
the locking system is unblocked and in a further zone 206 there is
the rails movement. In a subsequent zone 208 the locking system is
blocked, in a further zone 210 the locking system is locked and in
a final zone 212 the maneuver is terminated.
[0043] The segmentation is advantageously realized based on a known
stroke of a slider of the point machine. The slider stroke is
subdivided in segments based on identified movements of specific
subset of the point system.
[0044] Returning now to FIG. 1, at step 8, the processing unit
extracts, for each segment disclosed with reference to FIGS. 2 and
3, and for each current and voltage signal, relevant features such
as the peak value of the signal, differentials, average, etc. The
set of extracted values are different for different point systems.
For example, for a first type point system known per se, the
average and the standard deviation of the first three segments are
extracted, whereas for a second type point system known per se, the
duration and the slope of the first segment and the kurtosis on the
second segment are extracted. The selection of features is based on
the physical behavior of the point system itself, therefore, if the
behavior is different between the first and the second type point
system, the features also vary.
[0045] The extracted features, which represent statistical and
physical characteristics of the point system, belong to different
groups:
[0046] context features: necessary to contextualize the maneuver,
for example if a maneuver is complete, if it is a maintenance
maneuver, if the machine is being operated at a particular
temperature, etc.;
[0047] usage features: to contextualize the frequency of use of the
point system;
[0048] turnout "health" features: characteristics necessary to
contextualize the global turnout behavior;
[0049] point machine "health" features:
[0050] a) global: characteristics of the signal that relate to the
point machine;
[0051] b) segments: characteristics of the signal that relate to
each segment (or phase of movement) of the point machine;
[0052] In the following of the description, reference will be made
in general only to "extracted features".
[0053] Through this segmentation step 6, the maneuver is
represented by a vector of features which highlight the physical
behavior of the point system. The vector comprises the features of
both the current and voltage signal. This reduces significantly the
data to be treated, and subsequently the volume of data to be
communicated and processed at the following steps.
[0054] The feature extraction represents the final step in a
pre-processing phase.
[0055] Advantageously, therefore, a vector representing the
maneuver is determined, the vector comprising said extracted
features, e.g. peak values, differential values, average values,
mean values measured during each segment of the maneuver.
[0056] Advantageously the vector is compared with a library of
vectors relative to degradation mechanisms, and the degradation
mechanism is determined in function of the comparison.
[0057] At step 10 the processing unit compares the set of extracted
features with a set of predetermined reference features or values
which represent a "healthy" maneuver, thus producing a "health"
index or indicator as disclosed herein below.
[0058] The "health" index is a numerical value which indicates the
level of degradation of the target equipment with respect to a
failure threshold and varies monotonically with the condition of
the equipment. The "health" index is an aggregation or selection of
health indicators as herebelow disclosed, and is used to define an
overall global "health" of the point system. The "health" index is
used in prognostics for an estimation of an expected evolution in
time and, thus, of an estimated time before an expected failure.
The "health" index is calculated based on a comparison between the
vector above disclosed and several references values. In
particular, the health index is compared with a threshold value
representative of the health index of N previous maneuvers, in
order to determine whether the current maneuver is healthy or
not.
[0059] In the present description the expressions "health" or
"healthy" refer to a maneuver which takes place at a point system
which is in good operating conditions, which do not need
maintenance, etc.
[0060] To perform such comparison, for each segment, as indicated
above, different features such as the mean of the signal, the
maximum of the signal, the derivative of the signal, the slope of
the curve, are extracted in a manner known per se. These values are
then normalized with respect to a set of predetermined reference
"healthy" values of features.
[0061] The predetermined reference "healthy" values are values
automatically learnt with a machine learning process from data
relative to previous maneuvers. In particular, in order to detect a
deviation from what is expected to be a healthy behavior, and
therefore to detect an anomaly, a priori predetermined maneuvers,
in particular forty maneuvers, are identified as "healthy". A
corresponding area in a feature-space, which corresponds to a
healthy region, can be therefore determined. In other words, in an
n-dimensional space, where n is the number of features taken into
consideration, an area can be defined as "healthy". These forty
maneuvers, twenty per each direction of movement along the railway
track, are referred to as "baseline", as they define a base value
against which current new values have to be compared.
[0062] The degree of health or abnormality of a new maneuver is
therefore represented by the distance between the "healthy" area in
the feature-space and the new maneuver itself. The "health" index
is defined as the distance between an healthy maneuver and a
current maneuver in the feature-space.
[0063] In the following of the description, the term
"predetermined" will refer to a value(s) learnt each time thanks to
a machine learning process known per se, based on the concepts
above disclosed.
[0064] Preferably, a Gaussian normalization is done as here
disclosed: a predetermined mean is subtracted from the original
value of each feature taken from the signal and divided by a
predetermined standard deviation. In particular, the predetermined
mean is a value calculated as the mean of the set of reference
"healthy" values (the baseline above indicated), and the
predetermined deviation standard is one value of the set of
reference "healthy" values (the baseline above disclosed).
[0065] The result of such normalization is called "indicator". The
indicators of all the features, relating to both the current signal
and the voltage signal, are then combined, to give a single value
called global "health" indicator. In particular, the global
"health" indicator is the Mahalanobis distance calculated from the
set of indicators. A failure threshold and a set of AND rules are
then applied in a manner known per se to the value of the global
"health" indicator to assess the state of the point system:
healthy, abnormal or degraded. In particular, if the global
"health" indicator exceeds the failure threshold, this means that
the point system is about to have a failure.
[0066] Examples of AND rules include the following: if the time
between the current maneuver and the previous one is less than a
predetermined time, for example one minute, and the energy of the
maneuver is above a threshold, then some maintenance is
occurring
[0067] Several shape parameters of the acquired signals/the
extracted features are therefore normalized with respect to learnt
baselines and then combined (the Mahalanobis distance) to calculate
the global "health" indicator which represents how similar the
actual shape of the signals is to the shape of a set of "healthy"
reference curves.
[0068] At this point, at step 12, the values of the indicators are
concatenated into a vector called signature, said values
representing the effect of a given degradation on the point system,
which are an immediate, direct and easy to understand
interpretation of the characteristics of the turnout.
[0069] The "health" state is expressed in a user-friendly web
interface which allows an immediate comprehension of the state of
the point system together with a list of possible failure
mechanisms. The interface helps the maintainers to understand the
type of maintenance intervention necessary to restore the asset of
the point system as well as the urgency of the action.
[0070] FIG. 4 shows an example of such interface. This interface
offers an overview of a railway network being monitored, and is
divided into three main areas, namely a global vision area 300, a
field layout area 302 and an overall summary area 304.
[0071] In the global vision area 300 there are two types of
information for all the point systems being monitored: reference
information and availability information. This information is
summarized by two rings, respectively an internal and an external
ring, wherein each point system is a square in these rings.
[0072] The "health" of each point system is indicated by a color.
The availability of the machines is indicated by various hues in
the internal ring, where a dark zone indicates that there is a
maintenance action being performed on the point system, a light
zone indicates that the point system is offline because the sensors
are not sending data and a medium colored zone indicates that the
switch point system is online. It is possible to select any machine
from the ring to see the corresponding reference index.
[0073] In the field layout area 302, the monitored point systems
are presented in their real on-field configuration, colored by
their "health" state to immediately identify which machines are in
need of maintenance. If the machines are offline or under
maintenance, this is reported as shown in FIG. 5.
[0074] In FIG. 5 it is possible to see that turnout 2, indicated by
reference 500, is undergoing maintenance. This can be seen from the
dark frame in field layout area 302 and from the "zebra" section
502 in the global vision area 300.
[0075] Returning now to FIG. 4, in the overall summary area 304 it
is possible to see an overview of the characteristics of the
railway network being monitored. A first zone 304a indicates the
availability of the monitored machines, in other words the
percentage of machines which are online over the total. A second
zone 304b is the global health of the machines, in other words the
percentage of machines which are "healthy" over the total. A third
zone 304c indicates the total number of maneuvers registered since
the installation date. A fourth zone 304d is the date and time of
the last maneuver monitored, to give an indication of how updated
the page is.
[0076] The method of the present invention has been able to detect
faults which the maintainers had not noticed, as well as power
supply interruptions, obstacles and rail deformation. Therefore,
the detection capabilities of the method of the present invention
have proven to be more effective than visual inspections.
[0077] The present invention further comprises a monitoring system
for monitoring a point system of a railway network which
includes:
[0078] a point system of a railway network as above disclosed;
[0079] a plurality of sensors per se known placed in correspondence
with said point system; and
[0080] an elaboration unit connected to said sensors and to the
point system and arranged to carry out the method of the present
invention.
[0081] Further subject of the present invention is a point system
of a railway network including a plurality of sensors arranged to
be connected to an elaboration unit arranged to carry out the
method of the present invention.
[0082] Clearly, the principle of the invention remaining the same,
the embodiments and the details of production can be varied
considerably from what has been described and illustrated purely by
way of non-limiting example, without departing from the scope of
protection of the present invention as defined by the attached
claims.
* * * * *