U.S. patent application number 15/197699 was filed with the patent office on 2017-04-20 for system and method for characterizing ferromagnetic material.
The applicant listed for this patent is The Charles Stark Draper Laboratory, Inc.. Invention is credited to Rami S. Mangoubi, Brian P. Timmons.
Application Number | 20170108469 15/197699 |
Document ID | / |
Family ID | 58387048 |
Filed Date | 2017-04-20 |
United States Patent
Application |
20170108469 |
Kind Code |
A1 |
Timmons; Brian P. ; et
al. |
April 20, 2017 |
SYSTEM AND METHOD FOR CHARACTERIZING FERROMAGNETIC MATERIAL
Abstract
A system and method using magnetic sensing to non-intrusively
and non-destructively characterize ferromagnetic material within
infrastructure. The system includes sensors for measuring magnetic
field gradients from a standoff distance adjacent to ferromagnetic
material. The method includes using the system to measure magnetic
fields, determining magnetic field gradients measured by a sensor
array, and comparing measured and modeled or historical magnetic
field gradients at the same or similar positions to identify
differences caused by a phenomenon in the ferromagnetic material,
and, in a particular embodiment, to recognize defects and
developing defects.
Inventors: |
Timmons; Brian P.; (Milford,
MA) ; Mangoubi; Rami S.; (Newton, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Charles Stark Draper Laboratory, Inc. |
Cambridge |
MA |
US |
|
|
Family ID: |
58387048 |
Appl. No.: |
15/197699 |
Filed: |
June 29, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62265851 |
Dec 10, 2015 |
|
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62185888 |
Jun 29, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/2025 20190101;
G01N 27/83 20130101; G01N 33/20 20130101; G01N 17/006 20130101 |
International
Class: |
G01N 27/83 20060101
G01N027/83; G01N 33/20 20060101 G01N033/20 |
Claims
1. A method for characterizing a ferromagnetic material,
comprising: receiving measured magnetic field data from a plurality
of sensors adjacent the ferromagnetic material at a plurality of
locations along the ferromagnetic material; deriving measured
magnetic field features from the measured magnetic field data; and
comparing the derived magnetic field features with modeled magnetic
field features to identify occurrence of a phenomenon in the
ferromagnetic material.
2. The method of claim 1, further comprising measuring a magnetic
field, using the sensors, to generate the magnetic field data.
3. The method of claim 1, the measuring magnetic field data
including obtaining measurements from a plurality of magnetometers
arranged in a known pattern.
4. The method of claim 1, the deriving magnetic field features
including determining differences between measured magnetic fields
for pairs of the sensors.
5. The method of claim 1, the deriving magnetic field features
including deriving magnetic field gradients between measured
magnetic fields for pairs of the sensors.
6. The method of claim 1, the magnetic field features being one or
more numerics, that are derived from the measured magnetic field
data, chosen from the group of numerics including: Fourier, Wavelet
or any other transform, magnetic field gradients; gradient Fourier
transform, wavelet transform; 2.sup.nd derivative matrices,
Hessians, and fractal dimension.
7. The method of claim 1, further comprising determining a nearest
sensor to the ferromagnetic material based on magnetic fields
measured from the plurality of sensors.
8. The method of claim 1, further comprising characterizing the
phenomenon of the ferromagnetic material to distinguish between a
defect and a non-defect.
9. The method of claim 8, the step of characterizing including
applying a a pairwise comparison between the measured magnetic
field features and the modeled magnetic field features to
characterize a type of phenomenon.
10. The method of claim 9, the step of applying utilizing a
pairwise statistical comparison plot.
11. The method of claim 8, the step of characterizing including
determining a signature from the measured magnetic field features
associated with a non-defect of the ferromagnetic material.
12. The method of claim 11, the step of characterizing further
including determining a magnetization direction and a magnetization
amplitude based on the signature of the non-defect.
13. The method of claim 12, the step of characterizing further
including using the magnetization amplitude of the non-defect to
scale the measured magnetic field data derived features to identify
a phenomenon as a defect.
14. The method of claim 12, further comprising determining modeled
magnetic field features is based on the magnetization direction and
the magnetization amplitude in the ferromagnetic material.
15. The method of claim 14, the step of determining modeled
magnetic field gradients being based on at least one physics
model.
16. The method of claim 14, determining modeled magnetic field
gradients being based on prior measurements of the ferromagnetic
material.
17. The method of claim 1, the step of characterizing the
phenomenon incorporating data from non-magnetic sensors with the
measured magnetic field data.
18. The method of claim 17, said data from non-magnetic sensors
including location information corresponding to scan positions.
19. A system for characterizing a ferromagnetic material,
comprising: memory capable of storing magnetic field data from at
least one sensor configured to measure magnetic field data at a
plurality of scan positions along the ferromagnetic material, and
software including machine readable instructions, a processor
coupled with the memory, the processor configured to, in response
to execution of the software, perform the steps of: derive magnetic
field feature data from the magnetic field data at the plurality of
scan positions, and compare the measured magnetic field feature
data with modeled magnetic field feature data to identify a
phenomenon in the ferromagnetic material.
20. The system of claim 19, further comprising the at least one
sensor, the at least one sensor being hardwired to the memory.
21. The system of claim 19, the at least one sensor selected from
the group consisting of a one-axis magnetometer, a two-axis
magnetometer, or a three-axis magnetometer.
22. The system of claim 19, the at least one sensor being a
plurality of sensors arranged in a one-, two-, or three-dimensional
array positionable at a standoff distance from the ferromagnetic
material.
23. The system of claim 19, the plurality of sensors having
adjustable positions to adjust spacing distances therebetween.
24. The system of claim 19, the ferromagnetic material comprising a
pipe and the phenomenon comprising a welded junction connecting a
first segment of the pipe to a second segment of the pipe.
25. The system of claim 24, the welded junction between the first
segment and the second segment producing a magnetic flux leakage,
the processor further configured to determine magnetization
direction and magnetization amplitude of the first segment and the
second segment in response to the magnetic flux leakage.
26. The system of claim 19, the step of comparing including
comparing a likelihood based on the magnetic field feature to a
threshold, the phenomenon being a known non-defect if the
likelihood is below the threshold, the pheonomenon being a defect
if the likelihood is above the threshold.
27. The system of claim 19, the memory further storing a pairwise
statistical plot, the processor further configured to characterize
the phenomenon based on the pairwise statistical plot.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application No. 62/185,888, filed Jun. 29, 2015, entitled
"Detection of Defects in Ferromagnetic Materials using Large
Standoff Magnetization (LSM) Sensors." This application also claims
priority to U.S. Provisional Application No. 62/265,851, filed Dec.
10, 2015, entitled "System and Method for Characterizing
Ferromagnetic Material." Each of the aforementioned applications is
incorporated by reference herewith in its entirety.
BACKGROUND
[0002] Metal components of structures are susceptible to defects,
such as due to imperfect manufacture, corrosion, fatigue, wear,
damage, etc. To prevent catastrophic failure of such structures,
metal components may be visually inspected to identify defects
before a failure occurs. However, many structures are not easily
inspected due to being buried underground or beneath the sea, or
due to being embedded within other materials such as concrete. For
large infrastructure that contains metal components, visual
inspection may be impractical or too costly to perform
routinely.
[0003] Many ferromagnetic objects, including steel pipe, act as
weak permanent magnets even when not intentionally magnetized; for
example, magnetic dipoles in steel may partially orient to the
Earth's magnetic field after cooling below the Curie temperature
when cast or hot-rolled in the foundry. Magnetic fields present in
ferromagnetic objects as stray byproducts of their manufacture are
known herein as parasitic fields. The Earth's magnetic field also
induces magnetic fields in ferromagnetic objects. These magnetic
fields permit detection of ferromagnetic objects from a distance.
Magnetic exploders for naval mines and torpedoes have been designed
to detect magnetic fields from large ferrous objects, such as
warships, since 1917, although both German and American magnetic
exploders were problematic when used in combat on torpedoes in
1939-1943. Magnetic exploders, however, are merely intended to
detect the object from a distance, not to detect or analyze defects
in that object.
[0004] Magnetic particle inspection is well known as a method for
detecting cracks in objects. In this technique, a ferromagnetic
object is placed in a magnetic field, and magnetic particles, such
as iron filings, are applied to the object. The magnetic field may
be provided by passing an electric current through the object, or
by placing the object in a field provided by an electromagnet. If a
crack is present, the magnetic particles cluster near the crack.
Field strengths used for magnetic particle inspection are typically
much greater than the Earth's magnetic field, or those parasitic
fields that may be present in ferromagnetic materials.
SUMMARY
[0005] According to an embodiment, a method for characterizing a
ferromagnetic material includes: receiving measured magnetic field
data from a plurality of sensors adjacent the ferromagnetic
material at a plurality of locations along the ferromagnetic
material; deriving measured magnetic field features from the
measured magnetic field data; comparing the derived magnetic field
features with modeled or previously collected, verified magnetic
field features to identify differences caused by a phenomenon in
the ferromagnetic material.
[0006] According to another embodiment, a system for characterizing
a ferromagnetic material includes: memory capable of storing
magnetic field data from at least one sensor configured to measure
magnetic field data at a plurality of scan positions along the
ferromagnetic material, and software including machine readable
instructions. The system may further include a processor coupled
with the memory, the processor configured to, in response to
execution of the software, perform the steps of: derive magnetic
field feature data from the magnetic field data at the plurality of
scan positions, and compare the measured magnetic field features
data with modeled magnetic field feature data to identify a
phenomenon in the ferromagnetic material.
BRIEF DESCRIPTION OF THE FIGURES
[0007] FIG. 1 is a block diagram of one system for characterizing
ferromagnetic material, in an embodiment.
[0008] FIG. 2 is a block diagram of another system for
characterizing ferromagnetic material, in an embodiment.
[0009] FIG. 3 illustrates one magnetic sensor array used in a
system for characterizing ferromagnetic material, in an
embodiment.
[0010] FIG. 4 illustrates a system for characterizing ferromagnetic
material, in an embodiment.
[0011] FIG. 5 illustrates a pipe made of ferromagnetic
material.
[0012] FIG. 6 is a flowchart including steps of a method to
characterize ferromagnetic material, in an embodiment.
[0013] FIG. 7 shows a plot of magnetic field versus scan position
from one sensor in a system that characterizes ferromagnetic
material.
[0014] FIG. 8 illustrates a plot of measured magnetic field
strength versus scan position for a system that characterizes
ferromagnetic material.
[0015] FIG. 9 illustrates a plot of dipole model magnetic field
strength versus scan position for a system that characterizes
ferromagnetic material.
[0016] FIG. 9A and FIG. 9B represent plots of magnetic field
gradients versus scan position in presence of a weld.
[0017] FIG. 9C and FIG. 9D represent plots of magnetic field
gradients versus scan position in presence of a defect.
[0018] FIG. 10 shows a plot of magnetic field strength versus scan
position for an axial dipole model, in an embodiment.
[0019] FIG. 11 shows a plot of magnetic field strength versus scan
position for a lateral dipole model, in an embodiment.
[0020] FIG. 12 shows a plot of magnetic field strength versus scan
position for a vertical dipole model, in an embodiment.
[0021] FIG. 13 shows a plot of magnetic field strength versus scan
position for a combination dipole model, in an embodiment.
[0022] FIG. 14 is a flowchart illustrating steps of a method to
characterize a ferromagnetic material, in an embodiment.
[0023] FIG. 15 shows a plot of measured magnetic field gradients
versus scan position, in an embodiment.
[0024] FIG. 16 shows a plot of dipole model magnetic field
gradients versus scan position, in an embodiment.
[0025] FIG. 17 shows steps for determining magnetic field gradients
in FIG. 14, in an embodiment.
[0026] FIG. 18 is a flow chart for a method to identify a
phenomenon within ferromagnetic material, in an embodiment.
[0027] FIG. 19 shows a pairwise statistical comparison plot for
characterizing ferromagnetic material, in an embodiment.
[0028] FIG. 20A, FIG. 20B, and FIG. 20C show diagrams of schemes
for combining magnetic field data with data from other sensing
modalities, in an embodiment.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0029] FIG. 1 schematically illustrates one system 100 for
characterizing a ferromagnetic material 130, in embodiments. System
100 non-intrusively and non-destructively detects local phenomena
in an infrastructure, including defects and non-defects, based on
ferromagnetic material 130. System 100 includes a plurality of
magnetic sensors 101. Although FIG. 1 shows four magnetic sensors
101, system 100 may have more or fewer sensors 101 without
departing from the scope hereof. Sensors 101 couple to a data
processing module 150 via communication paths 115, which may
include one or both of a wired and/or a wireless communication
media. Data processing module 150 processes magnetic field
measurements received from sensors 101 via communication paths 115
to characterize ferromagnetic material 130 as described below. Data
processing module 150 has at least one processor 152 coupled with a
memory 154, and may in some embodiments have a global positioning
system (GPS) receiver 156 and/or a digital-radio uplink 158.
Digital-radio uplink 158 may operate through a cell phone network,
or other wireless network such as WiFi, for example, to transmit or
receive information to a server 160. Server 160 may include, in
embodiments, a database 162 of anomalies.
[0030] Ferromagnetic material 130 exhibits magnetization based on
its structure, composition, and fabrication history. At the same
time, ferromagnetic material 130 may have a phenomenon 135 that
perturbs the magnetic field from ferromagnetic material 130, as
illustrated by magnetic field lines 140 in FIG. 1, wherein
phenomenon 135 "disrupts" an otherwise spatially regular magnetic
field of ferromagnetic material 130. Phenomenon 135 is for example
(a) a weld or junction between segments of ferromagnetic material
130, (b) an unintentional irregularity of cracked, missing or
otherwise faulty ferromagnetic material (hereinafter called a
"defect" and typically due to corrosion, fatigue, wear, damage or
imperfect manufacture; some defects may lead to infrastructure
failure), or (c) an intentionally-designed gap or opening. Sensors
101 may be magnetometers arranged in an array to measure magnetic
field 140 related to phenomenon 135.
[0031] Identifying a defect in material 130 prior to failure in
components such as reinforcing steel, pipelines, oil platform legs,
ship hulls, etcetera buried underground or located underwater often
requires inspecting beneath a visible surface. The embodiments
disclosed herein may be suitable in evaluating ferromagnetic
material of such infrastructure including, but not limited to:
industrial vessels and pipes of plants and equipment, including
power plants, refineries and heat exchangers; pipelines, such as
oil and gas pipelines; railways, including rails and bridges of
railroads, light-rail and subways; structures, such as buildings
and bridges made with ferrous beams or rebar-reinforced concrete;
and partially or fully submerged drilling rigs, ships and
submarines.
[0032] During use of system 100 to inspect infrastructure, system
100 is positioned near, and moved along ferromagnetic material 130
while system 100 measures material-associated magnetic field 140.
Sensors 101 are arranged in a spatially distributed array that
provides a spatial map of magnetic field 140, at each traveled
location along material 130, with each sensor 101 measuring both
magnetic direction and magnitude. Data processing module 150 in
turn processes magnetic field measurements received from the array
of sensors 101 via communication paths 115 to characterize magnetic
field 140, thereby providing a current scan of magnetic field along
ferromagnetic material 130.
[0033] In data processing system 150, processor 152 may execute
software (for example software 263 discussed in further detail
below with respect to FIG. 2), realized as machine readable
instructions stored in memory 154, to implement (a) scan routines
to store the current scan of the magnetic field in memory 152, and
(b) analysis routines to analyze the scan of the magnetic field for
anomalies such as phenomenon 135. If an anomaly is located,
processor 152 may further execute additional software (for example
software 263 discussed in further detail below), also realized as
machine readable instructions stored in memory 154, to implement
further analysis routines on the stored scan to identify the
anomaly as a non-defect, such as a weld, flange, or
intentionally-designed gap/opening, or identify the anomaly as a
defect, such as a missing metal defect or other unintentional fault
within the material 130. It should be appreciated that various
aspects of data processing system 150 may be performed remotely,
such as in server 160, without departing from the scope hereof. For
example, the analysis routines, including analyzing the scan of
magnetic field for anomalies such as phenomenon 135, may be
performed on a scan that is previously implemented by data
processing module 150 (via scan routines) and then transmitted from
data processing module 150 to server 160.
[0034] In embodiments, the analysis routines operate by determining
signature phenomena, of the observed magnetic field (such as
phenomena in, or functions of, the magnetic field gradients and
derivatives thereof) as recorded from multiple locations in a
sliding window of the scan. In an embodiment, the software (for
example software 263 discussed in further detail below)
implementing such analysis routines determines signature phenomena
by fitting a superposition of predefined signature phenomena. The
predefined signature phenomena may be derived from (a) computer
models of magnetic dipoles to the observed magnetic field from the
locations in the sliding window, (b) a non-dipole based model, (c)
measurements, or (d) a combination thereof.
[0035] Information about anomaly types, including classifications
of the anomaly types and pattern phenomena corresponding to each
anomaly type, may be stored in memory 154 and/or database 162. In
an embodiment optimized for analysis of pipelines, the anomaly
types include exemplary good welds and exemplary defective welds,
as well as cracks, breaks, valves, taps, and corroded locations.
The analysis routines may be configured to provide the
classification that most closely matches each anomaly found during
a scan.
[0036] A location read from GPS 156, and/or other location sensors
such as an odometer, may in some embodiments be associated with a
portion of the scan associated with a defect, or in some
embodiments portions of the scan associated with a non-defect, such
as a weld or flange, and these locations and associated scan
windows are reported through uplink 158 to server 160 and stored in
database 162. Since weld locations in a pipeline, or bolted joints
in railroad track, are unlikely to change with time in
infrastructure 130, new phenomena, or phenomena that have
significantly changed character since any prior scan, can indicate
incipient failure such as cracks in a pipe or breaks in rail.
Either processor 152 or server 160, may correlate the current and
prior scan to align phenomena, and then compare phenomena of
anomalies detected in the current scan to observations made during
a prior scan at the same location, as may have been previously
recorded in database 162, to determine whether the phenomenon is
new, and identify it as new. New phenomena, as well as phenomena
classified as defects, may warrant further investigation, such as
by excavating a pipeline.
[0037] In particular embodiments, system 100 does not include a
bias magnet for magnetizing the ferromagnetic material 130. In
these embodiments, the magnetic fields sensed by system 100 are
parasitic magnetic fields and fields induced in the ferromagnetic
material by the Earth's magnetic field.
[0038] FIG. 2 schematically illustrates a system 200 that
characterizes ferromagnetic material. System 200 is a an embodiment
of system 100 In system 200, sensors 101, of FIG. 1, are
implemented in a sensor array 250 that communicatively couples to
data processing module 150. System 200 implements data processing
module 150 with at least one processor 264 in communication with
memory 262. Processor 264 is an embodiment of processor 152. Memory
262 is an embodiment of memory 154 and may be transitory and/or
non-transitory and in some embodiments includes one or both of (a)
volatile memory such as RAM and (b) non-volatile memory such as,
ROM, EEPROM, Flash-EEPROM, magnetic media including disk drives,
optical media. Memory 262 stores software 263 and firmware 261 as
machine readable instructions executable by processor 264 to
process data from sensor array 250 and identify and/or characterize
one or more phenomena 135 of ferromagnetic material 130. It should
be appreciated that various aspects of software 263 and firmware
261 may be implemented by server 160 shown in FIG. 1, instead of,
or in addition to, data processing module 150. In embodiments,
measurements from sensor array 250 are received by a receiver 255
that communicates measurements to data processing module 150. In
other embodiments, measurements are communicated directly from
sensor array 250 to data processing module 150. Receiver 255 is for
example a data acquisition device. In embodiments, data from
non-magnetic sensors 252 (e.g., accelerometers) are also received
by receiver 255, as more fully described below. Illustratively,
data processing module 150 includes an interface 265 for
communicating with other devices, including server 270 that
processes and stores data. Server 270 is similar to server 160, and
therefore the discussion of server 160 applies equally to server
270. Although data processing module 150 is shown as a single
device, it should be appreciated that data processing module 150
may incorporate one or more devices such as computers, processors,
memories, etc.
[0039] FIG. 3 schematically illustrates an exemplary magnetic
sensor array 300 for characterizing ferromagnetic material 330 in
the form of a pipe. Sensor array 300 includes ten magnetic sensors,
including a first magnetic sensor 301, a magnetic second sensor
302, and so on up to a tenth magnetic sensor 310 arranged in a
three-dimensional (3D) array. More or fewer magnetic sensors may be
utilized without departing from the scope hereof. Sensor array 300
is an embodiment of sensor array 250, FIG. 2, and each sensor
301-310 is for example an embodiment of sensor 101 of FIGS. 1-2.
FIG. 3 illustrates an exemplary "T" arrangement of sensors 301-310
positioned along three orthogonally oriented axes.
[0040] Although in FIG. 3, sensor array 300 is shown in a "T"
arrangement, the sensor array 300 may be configured in other
patterns without departing from the scope hereof. For example,
sensor array 300 may also be implemented in non-orthogonal
arrangements, instead of the orthogonal arrangement shown in FIG. 3
without departing from the scope hereof. Moreover, sensor array
300, in either a non-orthogonal or an orthogonal arrangement may be
configured with more or fewer magnetic sensors, and could be
deployed in positions and arranged in a pattern, such as in a cone-
or sphere-shaped pattern. Furthermore, in embodiments, the sensor
array may be synthesized with just a single magnetic sensor moved
between known positions to make multiple measurements as a data
array. Likewise, the locations of sensors 301-310 need not be
restricted to locations along the axes of a 3D coordinate system.
One- or two-dimensional arrays may also be beneficially employed as
array 300.
[0041] Magnetic sensor array 300 is positioned with a standoff
distance 312 above ferromagnetic material 330 having a defect 350.
Ferromagnetic material 330 is an example of ferromagnetic material
130, FIG. 1, while defect 350 is an example of phenomenon 135.
Defect 350 is for example a missing metal defect, a
corrosion-induced defect, or any other type of irregularity that is
substantially different from an expected shape and structure of
ferromagnetic material 330. Defect 350 thus causes a magnetic field
phenomenon with an exemplary magnetization direction indicated by
arrow 340. Standoff distance 312 may be known, estimated or
measured, for example using ground penetrating radar.
[0042] The ability to sense magnetic fields with sensor arrays,
such as sensor array 300, depends on standoff distance 312, the
strength of magnetic field 340 from ferromagnetic material 330, the
sensitivity of magnetic sensors 301-310, and spacing distances 321,
322, 323, 324, 325 between sensors 301-310 in sensor array 300. In
an embodiment, magnetic sensors 301-310 are magnetometers that
measure magnetic fields. Magnetic sensors 301-310 may be one-axis
magnetometers that measure magnetic fields along one axis, two-axis
magnetometers that measure magnetic fields along two axes, or
three-axis magnetometers that measure magnetic fields along three
axes. The three axes are for example x, y, and z axes depicted in
FIG. 3. Note that sensor array 300 includes variable spacing
distances between magnetic sensors 301-310; for example, a first
distance 321 between magnetic sensors 301 and 302 is greater than a
second distance 322 between magnetic sensors 302 and 303. Similarly
along the z-axis, a fourth distance 324 may be greater than a fifth
distance 325. In an embodiment, first, second, third, fourth, and
fifth distances 321, 322, 323, 324, 325 are optimized to measure
dipole magnetic fields and determine magnetic field gradient peak
signatures of defect 350 for a given standoff distance 312. In an
operational example, which the embodiments herein are not limited
to, a third distance 323 between magnetic sensors 303 and 304 is
about 15 cm for a standoff distance 312 of 25 cm. In another
operational example, magnetic sensors 301-310 have adjustable
positions within sensor array 300 such that sensor spacing
distances 321, 322, 323, 324, 325 are adjusted to optimize
measurement of magnetic fields having different field strengths for
different standoff distances 312.
[0043] FIG. 4 illustrates yet another system 400 for characterizing
ferromagnetic material 430. System 400 is an embodiment of system
100. System 400 shows four sensor arms 411, 412, 413, 414 each of
which contains one or more sensors 410 (e.g., magnetometers) that
measure magnetic field strength. Sensors 410 may be arranged in an
array, such as the sensor array 300 of FIG. 3, and attached to a
frame 420 by sensor arms 411, 412, 413, 414 or by other structure
when moving the array of sensors 410 along ferromagnetic material
430. Sensors 410 are an embodiment of sensors 101 and are arranged
in an example of sensor array 250. Ferromagnetic material 430 is an
example of ferromagnetic material 130. By way of example, frame 420
may be equipped with straps 405 or other means for a user to carry
system 400. In another embodiment, system 400 is mechanically
coupled to a vehicle, such as an automobile, train, aerial vehicle,
or underwater vehicle. Sensor arms 411, 412, 413, 414 may be
moveable up and down along frame 420 to account for variation in
standoff distance 312.
[0044] A power supply 440 electrically couples to sensors 410 to
provide direct current (DC) electrical power. Power supply 440 may
be wired to an electrical grid or have a battery pack that enables
remote, off-grid use of system 400. A receiver 455 couples to
sensors 410 via communication path 415, which is similar to
communication path 115 of FIG. 1, to receive data therefrom.
Receiver 455 is for example an embodiment of receiver 255, FIG. 2.
A computer 460 connects to receiver 455 via communication path 425
to process received sensor data. Computer 460 is for example an
embodiment of data processing module 150 implementing processor(s)
264, memory 262, and optional interface 265. Communication paths
415, 425 may include one or both of a wired and/or a wireless
communication media.
[0045] FIG. 5 shows an exemplary pipe 530 made of ferromagnetic
material. Pipe 530 is an example of ferromagnetic material 130 and
430 and may be characterized using any of systems 100, 200, and
400. Pipe 530 includes a weld 535, which is a welded junction that
joins a first segment 531 to a second segment 532 of pipe 530. Weld
535 is an example of an intentional non-defect phenomenon that
produces a characteristic magnetic field phenomenon providing a
magnetic field signature that may resemble a magnetic dipole. For
example, magnetic flux leakage may occur at weld 535 producing the
magnetic field signature. In an embodiment, magnetic field
signatures are determined in real-time and used for calibration and
compensation of magnetic field measurements caused by variability
such as platform motion or standoff distance 312. Data processing
module 150 compares magnetic field measurements obtained by sensors
101 to known magnetic field signatures to detect a defect, such as
defect 450, FIG. 4. In FIG. 2, memory 262 may thus include at least
one magnetic field signature for this purpose.
[0046] FIG. 6 is a flowchart illustrating steps of an exemplary
method 600 for measuring magnetic field 140 from infrastructure
containing ferromagnetic material 130. Method 600 is an example of
a "scan routine" as discussed above with respect to FIGS. 1, 2, and
4. As such, method 600 may be performed by system 100 of FIG. 1,
system 200 of FIG. 2, and system 400 of FIG. 4, for example using
data processing module 150 executing software 263.
[0047] In an optional step 610, the system for characterizing
ferromagnetic material moves to a first scan position, such as an
arbitrary location adjacent to infrastructure containing
ferromagnetic material. In an example of step 610, system 400 of
FIG. 4 is moved to a position adjacent to first segment 531 of pipe
530 of FIG. 5. In other examples, system 100 or 200, of FIGS. 1 and
2, is moved to a position adjacent to a first segment of
ferromagnetic material 130.
[0048] In a step 620, the system measures magnetic fields. In an
example of step 620, sensors 410 measure a magnetic field (e.g.
magnetic field 140) from first segment 531. In other examples of
step 620, sensors 110 of FIGS. 1-2, possibly in the arrangement of
array 300 of FIG. 3, measure a magnetic field from ferromagnetic
material 130.
[0049] In a step 630, the system for characterizing ferromagnetic
material moves to a next scan position. In an example of step 630,
system 400 of FIG. 4 moves to a position adjacent weld 535 of pipe
530 of FIG. 5. In another example of step 630, system 100 or 200,
of FIG. 1-2, is moved to a next scan position along ferromagnetic
material 130.
[0050] Step 640 is a decision. If in step 640 the end of the
infrastructure is reached, or the end of a desired scan range is
reached, method 600 ends. Otherwise, method 600 returns to step
620. In this way, method 600 is carried out to scan an entire
infrastructure or a desired portion of an infrastructure. The rate
at which magnetic fields are measured between first scan position
and the next scan position may depend on bandwidth of data
acquisition such as receiver 455 of FIG. 4. In an embodiment,
system 400 is moved between locations at a rate of 0.25 meters per
second. In another embodiment, system 100 or 200, of FIG. 1-2, is
moved at a rate of 0.25 meters per second along ferromagnetic
material 130.
[0051] FIG. 7 shows a plot 700 of exemplary magnetic fields
measured by one sensor, such as sensor 304 in array 300 or any of
sensors 110 of FIGS. 1-2 and 410 of FIG. 4, versus scan position
along pipe 330. Specifically, plot 700 illustrates exemplary
magnetic field 140 measured by this sensor during method 600 over
multiple iterations of step 620. Plot 700 includes magnitude of
magnetic field, B, aligned in x, y, and z axes (B.sub.x, B.sub.y,
B.sub.z) versus scan position along pipe 330. A dataset 710 shows
magnetic field strength along the x-axis, B.sub.x, versus scan
position; a dataset 720 shows magnetic field strength along the
y-axis, B.sub.y, versus scan position; and a dataset 730 shows
magnetic field strength along the z-axis, B.sub.z, versus scan
position. The scan direction is oriented along the x-axis and
sensor 304 is centered above pipe 330 in the y-dimension. By way of
comparison, at a scan position of zero in FIG. 7, sensor 304 of
FIG. 3 is positioned directly above defect 350. As sensor 304 is
moved along ferromagnetic material 330, the scan position from
defect 350 varies, corresponding to an increasing (positive values)
or decreasing (negative values) scan position depending on the
direction of movement.
[0052] Referring again to FIG. 6, in an optional step 650, magnetic
field data measured from step 620 is processed to characterize
ferromagnetic material 130. In an example of step 650, measured
magnetic field data is compared, by data processing module 150
executing software 263 (or alternatively a remote server such as
server 160 executing software similar to software 263), with an
empirically determined or physics-based model of magnetic fields to
identify and characterize phenomena in the magnetic field data
caused by a phenomenon of ferromagnetic material 130. Measured data
and modeled data are compared using for example matched filters or
statistical-detection algorithms. One example of a physics-based
model is a magnetic dipole model. Missing metal from ferromagnetic
material produces predominantly magnetic dipole characteristics
that are detected and matched with a magnetic dipole model. Missing
metal defects, such as defect 350, FIG. 3, may have a dipole in
reverse orientation to magnetization in ferromagnetic material 330.
The reverse dipole orientation may be used to help identify defect
350. Similarly, welds forming junctions between segments of
ferromagnetic material, such as weld 535 of FIG. 5, produce
predominantly magnetic dipole characteristics. For example, at weld
535 between pipe segments 531, 532 dipoles may exist due to
differences in magnetization direction and amplitude between pipe
segments 531, 532 together with magnetic reorientation due to
heating when the weld was made.
[0053] In a particular embodiment, modeled data is determined from
a finite element model. In embodiments, model-based analysis, for
example performed by data processing module 150 executing software
263, of magnetic dipoles detected by the system includes one or
more of: applying interpolation on the magnetic field signature
sphere to obtain the magnetic field at planes above and parallel
and near-parallel to the pipe at different distances, and angles;
extracting magnetic field spatial phenomena from the magnetic
field, such as gradient, directional derivative, divergence or
Laplacian, curl, magnitude and neighborhood local statistical
moments of these phenomenon fields; obtaining daughter magnetic
field phenomena from the field, such as a Spatial Fast-Fourier
Transform (FFT) phase field, power spectral density (PSD), and
Wavelet coefficients; separately analyzing each phenomenon
statistically, for example using the t-test and the Wilcoxon Rank
test; and selecting phenomena by collectively satisfying, or
optimally satisfying, multiple criteria such as p-values,
correlation to size and height, and orthogonality (non-correlation
among phenomena). Nearby pairs and triplets of the above phenomena
are fused for FFT and Wavelet analysis. Extracted phenomena are
compared to a library of model-derived phenomena, such as welds and
defects.
[0054] FIGS. 8 and 9 show exemplary plots of measured and modeled
magnetic field strength, respectively, as a function of scan
position. FIG. 8 shows a plot 800 of exemplary magnetic fields
measured by a single sensor (e.g. one of sensors of array 300) for
a range of scan positions using method 600 of FIG. 6 implemented by
system 400 of FIG. 4. Plot 800 may thus illustrate magnetic fields
at a plurality of scan positions for weld 535 of FIG. 5 such as
measured with sensor 304 for example. A dataset 810 shows magnetic
field strength along the x-axis, B.sub.x, a dataset 820 shows
magnetic field strength along the y-axis, B.sub.y, and a dataset
830 shows magnetic field strength along the z-axis, B.sub.z, over a
range of scan positions along the x-axis at a position centered
over the pipe in the y-dimension. Magnetic field strength of pipe
segments as determined at weld 535, such as that illustrated in
plot 800, may be used for scaling magnetic field measurements from
pipe segments 531, 532 to normalize data for improved detection of
defects.
[0055] FIG. 9 shows a plot 900 of exemplary magnetic field strength
versus scan position from a dipole model used in characterizing a
ferromagnetic material phenomenon, such as weld 535 that joins
first and second pipe segments 531, 532 of FIG. 5. A dataset 910
shows magnetic field strength along the x-axis, B.sub.x, a dataset
920 shows magnetic field strength along the y-axis, B.sub.y, and a
dataset 930 shows magnetic field strength along the z-axis,
B.sub.z, versus scan position along the x-axis at a position
centered over the pipe in the y-dimension.
[0056] According to an embodiment, data processing module 150
compares measured magnetic field plots, such as plot 800 of FIG. 8,
with modeled magnetic field plots, such as plot 900 of FIG. 9 to
distinguish a weld signature from a defect signature in step 650 of
method 600, thereby detecting whether a defect has occurred. While
both weld and defect signatures have dipole characteristics,
magnetic field changes along the pipe may differ in magnitude from
those expected at a weld. Further, field gradients at a weld will
tend to taper from a field orientation in one segment of the pipe
to a potentially-different orientation in another segment of the
pipe, rather than returning to the same orientation beyond the
defect as to be expected in a single section of pipe. This may be
due to a broad transition zone between magnetic polarization of
pipe sections produced as the metal was heated and cooled during
welding, this transition zone being broader than typical missing
metal defects.
[0057] In an embodiment, a scalar likelihood, L, indicates the
presence of a defect determined from gradients in all axes in a
scan position window near the phenomenon, and from other
statistical processing; if L is greater than a threshold, the
phenomenon or anomaly is reported as a defect. FIGS. 9A-9D
illustrate L plotted versus scan window position with a threshold
of one; FIGS. 9A and 9B are associated with weld signatures and
L<1 indicating non-defect, for FIGS. 9C and 9D, L>1
indicating a defect. The window size may be varied and the gradient
data rescanned repeatedly with different window sizes depending on
the sizes of ferromagnetic material, phenomenon (e.g. defects,
weld, or anomaly) as discussed further below with respect to FIG.
18.
[0058] Magnetic fields calculated from dipole models for x, y and
z-axes, such as those plotted versus scan position in FIG. 9,
depend on orientation of the magnetic dipole. For example, a dipole
may have an axial orientation along the scanning direction, for
example along the x-axis of FIG. 3, a lateral orientation sideways
from the scanning orientation, for example along they-axis of FIG.
3, or a vertical orientation that is up and down from the scanning
direction, for example along the z-axis of FIG. 3. A combination
dipole has magnetization components of all three orientations,
C.sub.x, C.sub.y, and C.sub.z. Three-axis magnetic fields are
calculated for a dipole model using Equation 1, below.
( B x B y B z ) = 1 r 5 ( C x ( 3 x 2 - r 2 ) + 3 C y xy + 3 C z xz
3 C x xy + C y ( 3 y 2 - r 2 ) + 3 C z yz 3 C x xz + 3 C y yz + C z
( 3 z 2 - r 2 ) ) Equation 1 ##EQU00001##
[0059] Equation 1 is the magnetic field equation for an arbitrary
dipole orientation where C.sub.x, C.sub.y, and C.sub.z are
combination magnetic fields proportional to magnetization along the
x, y, and z-axes, respectively, and r is the absolute distance that
includes standoff distance 312 from the sensor to the magnetic
field source. In order for a magnetic signature to resemble a
dipole, sensor distance from a magnetic source, r, is for example
about two to three times longer than the magnetic source itself,
although shorter sensor distances contain dipole characteristics
that may be matched to Equation 1 if r is known.
[0060] FIG. 10 shows a plot 1000 of exemplary modeled magnetic
fields versus scan position for an axial dipole model, aligned with
the x-axis, which may be used by data processing module 150 (or
server 160 implementing analysis functions) to identify phenomena
of ferromagnetic material 130. A dataset 1010 shows magnetic field
strength along the x-axis, B.sub.x, a dataset 1020 shows magnetic
field strength along the y-axis, B.sub.y, and a dataset 1030 shows
magnetic field strength along the z-axis, B.sub.z, for a magnetic
dipole source oriented axially. C.sub.x is a constant, C.sub.y and
C.sub.z are zero. Each of datasets 1010, 1020, and 1030 show the
magnetic field as a function of x at a position centered over the
magnetic dipole source in the y-direction.
[0061] FIG. 11 shows a plot 1100 of exemplary modeled magnetic
field strength versus scan position for a lateral dipole model,
aligned with the y-axis, which may be used by data processing
module 150 (or server 160 implementing analysis functions) to
identify phenomena of ferromagnetic material 130. A dataset 1110
shows magnetic field strength along the x-axis, B.sub.x, a dataset
1120 shows magnetic field strength along they-axis, B.sub.y, and a
dataset 1130 shows magnetic field strength along the z-axis,
B.sub.z for magnetic dipole source oriented laterally. C.sub.y is a
constant, C.sub.x and C.sub.z are zero. Each of datasets 1110,
1120, and 1130 show the magnetic field as a function of x at a
position centered over the magnetic dipole source in the
y-direction.
[0062] FIG. 12 shows a plot 1200 of exemplary modeled magnetic
field strength versus scan position for a vertical dipole model
which may be used by data processing module to identify phenomena
of ferromagnetic material 130. A dataset 1210 shows magnetic field
strength along the x-axis, B.sub.x, a dataset 1220 shows magnetic
field strength along the y-axis, B.sub.y, and a dataset 1230 shows
magnetic field strength along the z-axis, B.sub.z for a magnetic
dipole source oriented vertically. C.sub.z is a constant, C.sub.x
and C.sub.y are zero. Each of datasets 1210, 1220, and 1230 show
the magnetic field as a function of x at a position centered over
the magnetic dipole source in the y-direction.
[0063] FIG. 13 shows a plot 1300 of exemplary combination magnetic
field strength versus scan position, which combines axial, lateral,
and vertical dipole orientations of FIGS. 10-12. A dataset 1310
shows magnetic field strength along the x-axis, B.sub.x, a dataset
1320 shows magnetic field strength along the y-axis, B.sub.y, and a
dataset 1330 shows magnetic field strength along the z-axis,
B.sub.z. C.sub.x, C.sub.y and C.sub.z are constants adjusted for
model fitting, based on factors including the strength of measured
magnetic fields.
[0064] Other than comparing models and measurements of magnetic
fields over scan position, such as step 650 of method 600, magnetic
field gradients may be used to further identify phenomena of
ferromagnetic material 130. According to an embodiment, magnetic
field gradients are calculated from a plurality of sensors arranged
in an array, such as sensor array 300 of FIG. 3. Specifically, FIG.
14 is a flowchart illustrating steps of one method 1400 to detect a
phenomenon of a ferromagnetic material and characterize the
ferromagnetic material based upon magnetic field data obtained
using one or more sensors. Each sensor (e.g. sensors 110, 310, 410)
is configured to measure the magnitude and direction of the local
magnetic field. Method 1400 uses models and measurements of
magnetic fields over scan position to detect and characterize
phenomenon 135 of ferromagnetic material 130. Data processing
module 150 (or server 160 implementing analysis functions) may
perform method 1400 based upon magnetic field data obtained from
sensor array 250. Method 1400 may be implemented in data processing
module 150 (or server 160) as at least a portion of software 263
and/or firmware 261, FIG. 2. Accordingly, it should be appreciated
that method 1400 may also be implemented using system 400, of FIG.
4. Aspects of method 1400 are for example an embodiment of step 650
of method 600.
[0065] In step 1410, magnetic field data are received for a
plurality of scan positions. In an example of step 1410, processor
264 executes software 263 and/or firmware 261 stored in memory 262
to parse data from sensor array 250, which is received either
directly from sensor array 250 or optionally via receiver 255.
[0066] In step 1420, magnetic field derived features are derived
from the magnetic field data of step 1410. Exemplary magnetic field
derived features comprise numerics that are derived from the raw
sensor data, or a denoised version thereof, including but not
limited to: the field measurements, their Fourier, Wavelet or any
other transform, their magnetic field gradients; the gradient
Fourier transform, wavelet transform or any other transform;
2.sup.nd derivative matrices or Hessians, their Fourier transforms
or any of their transforms, fractal dimension of the field,
gradients, Hessians, or features recovered by data mining or
machine learning/deep learning methods.
[0067] In an example of step 1420, the magnetic field derived
features that are calculated are magnetic field gradients. In such
example, the magnetic field gradients are calculated, by data
processing module 150 (or server 160), from differences in magnetic
fields between sensors 301-310 of sensor array 300, FIG. 3 for a
plurality of scan positions. In one embodiment, a single sensor
such as sensor 304 measures magnetic fields at a plurality of scan
positions, and one or more gradients are calculated, using for
example data processing module 150 (or server 160), from the
plurality of measurements. In another embodiment, magnetic field
gradients between different sensors are calculated for each scan
position. Equation 2, below, shows an exemplary calculation for
magnetic field gradients between fourth sensor 304 and eighth
sensor 308 along the x-axis of FIG. 3.
.DELTA. B xyz .DELTA. x = ( B x S 4 - B x S 8 B y S 4 - B y S 8 B z
S 4 - B z S 8 ) / x S 4 - S 8 Equation 2 ##EQU00002##
[0068] In Equation 2, .DELTA.B.sub.xyz/.DELTA.x is the difference
between three-axis magnetic fields between sensor 304 (abbreviated
S4) at position x.sub.S4 and sensor 308 (abbreviated S8) at
position x.sub.S8. B.sub.xS4 is the x-axis magnetic field at fourth
sensor 304, B.sub.xS8 is the x-axis magnetic field at eighth sensor
308, and so on for y-axis and z-axis magnetic fields, B.sub.y,
B.sub.z. X.sub.S4-S8 is the spacing distance between sensors 304
and 308.
[0069] Three-axis magnetic field gradients are calculated from
dipole models of magnetic fields for additional select pairs of
sensors in the same manner. For example, three-axis magnetic field
gradients (.DELTA.B.sub.xyz) are calculated using Equation 3,
below, between fourth sensor 304 and ninth sensor 309, between
fourth sensor 304 and tenth sensor 310, and between ninth sensor
309 and tenth sensor 310 along the z-axis, as depicted in FIG.
3.
.DELTA. B xyz .DELTA. z = ( B x S 4 - B x S 9 z S 4 - S 9 B x S 4 -
B x S 10 z S 4 - S 10 B x S 9 - B x S 10 z S 9 - S 10 B y S 4 - B y
S 9 Z S 4 - S 9 B y S 4 - B y S 10 z S 4 - S 10 B y S 9 - B y S 10
z S 9 - S 10 B z S 4 - B z S 9 z S 4 - S 9 B z S 4 - B z S 10 z S 4
- S 10 B z S 9 - B z S 10 z S 9 - S 10 ) Equation 3
##EQU00003##
[0070] In Equation 3, .DELTA.B.sub.xyz/.DELTA.z is the difference
between three-axis magnetic fields along the z-axis, z.sub.S4-S9 is
the spacing distance between fourth sensor 304 (abbreviated S4) and
ninth sensor 309 (abbreviated S9), B.sub.xS4 is the x-axis magnetic
field at fourth sensor 304, B.sub.xS9 is the x-axis magnetic field
at ninth sensor 309, and so on for other sensor pairs and for
y-axis and z-axis magnetic fields, B.sub.y, B.sub.z.
[0071] Similarly, select three-axis magnetic field gradients
(.DELTA.B.sub.xyz/.DELTA.y) are calculated along they-axis using
Equation 4, below.
.DELTA. B xyz .DELTA. y = ( B x S 1 - B x S 2 y S 1 - y S 2 B y S 1
- B y S 2 y S 1 - y S 2 B z S 1 - B z S 2 y S 1 - y S 2 B x S 1 - B
x S 3 y S 1 - y S 3 B y S 1 - B y S 3 y S 1 - y S 3 B z S 1 - B z S
3 y S 1 - y S 3 B x S 1 - B x S 4 y S 1 - y S 4 B y S 1 - B y S 4 y
S 1 - y S 4 B z S 1 - B z S 4 y S 1 - y S 4 B x S 2 - B x S 3 y S 2
- y S 3 B y S 2 - B y S 3 y S 2 - y S 3 B z S 2 - B z S 3 y S 2 - y
S 3 B x S 1 - B x S 5 y S 1 - y S 5 B y S 1 - B y S 5 y S 1 - y S 5
B z S 1 - B z S 5 y S 1 - y S 5 B x S 2 - B x S 4 y S 2 - y S 4 B y
S 2 - B y S 4 y S 2 - y S 4 B z S 2 - B z S 4 y S 2 - y S 4 B x S 1
- B x S 6 y S 1 - y S 6 B y S 1 - B y S 6 y S 1 - y S 6 B z S 1 - B
z S 6 y S 1 - y S 6 B x S 2 - B x S 5 y S 2 - y S 5 B y S 2 - B y S
5 y S 2 - y S 5 B z S 2 - B z S 5 y S 2 - y S 5 B x S 3 - B x S 4 y
S 3 - y S 4 B y S 3 - B y S 4 y S 3 - y S 4 B z S 3 - B z S 4 y S 3
- y S 4 B x S 1 - B x S 7 y S 1 - y S 7 B y S 1 - B y S 7 y S 1 - y
S 7 B z S 1 - B z S 7 y S 1 - y S 7 B x S 2 - B x S 6 y S 2 - y S 6
B y S 2 - B y S 6 y S 2 - y S 6 B z S 2 - B z S 6 y S 2 - y S 6 B x
S 3 - B x S 5 y S 3 - y S 5 B y S 3 - B y S 5 y S 3 - y S 5 B z S 3
- B z S 5 y S 3 - y S 5 B x S 3 - B x S 6 y S 3 - y S 6 B y S 3 - B
y S 6 y S 3 - y S 6 B z S 3 - B z S 6 y S 3 - y S 6 B x S 4 - B x S
5 y S 4 - y S 5 B y S 4 - B y S 5 y S 4 - y S 5 B z S 4 - B z S 5 y
S 4 - y S 5 B x S 2 - B x S 7 y S 2 - y S 7 B y S 2 - B y S 7 y S 2
- y S 7 B z S 2 - B z S 7 y S 2 - y S 7 B x S 4 - B x S 6 y S 4 - y
S 6 B y S 4 - B y S 6 y S 4 - y S 6 B z S 4 - B z S 6 y S 4 - y S 6
B x S 3 - B x S 7 y S 3 - y S 7 B y S 3 - B y S 7 y S 3 - y S 7 B z
S 3 - B z S 7 y S 3 - y S 7 B x S 5 - B x S 6 y S 5 - y S 6 B y S 5
- B y S 6 y S 5 - y S 6 B z S 5 - B z S 6 y S 5 - y S 6 B x S 4 - B
x S 7 y S 4 - y S 7 B y S 4 - B y S 7 y S 4 - y S 7 B z S 4 - B z S
7 y S 4 - y S 7 B x S 5 - B x S 7 y S 5 - y S 7 B y S 5 - B y S 7 y
S 5 - y S 7 B z S 5 - B z S 7 y S 5 - y S 7 B x S 6 - B x S 7 y S 6
- y S 7 B y S 6 - B y S 7 y S 6 - y S 7 B z S 6 - B z S 7 y S 6 - y
S 7 ) Equation 4 ##EQU00004##
[0072] In Equation 4, .DELTA.B.sub.xyz/.DELTA.y is the difference
between three-axis magnetic fields along the y-axis, y.sub.S1-S2 is
the spacing distance between first sensor 301 (abbreviated S1) and
second sensor 302 (abbreviated S2), B.sub.xS1 is the x-axis
magnetic field at first sensor 301, B.sub.xS2 is the x-axis
magnetic field at second sensor 302, and so on for other sensor
pairs and for y-axis and z-axis magnetic fields, B.sub.y and
B.sub.z.
[0073] In an example of step 1420, x-axis magnetic field gradients
(.DELTA.B.sub.xyz/.DELTA.x) are calculated using Equation 2 from
differences between three-axis magnetic fields (B.sub.x, B.sub.y,
B.sub.z) measured with fourth sensor 304 (S4) and eighth sensor 308
(S8) along the x-axis as depicted in FIG. 3. Similarly, select
z-axis magnetic field gradients (.DELTA.B.sub.xyz/.DELTA.z) are
calculated using Equation 3 for magnetic fields measured with
fourth sensor 304 (S4), ninth sensor 309 (S9), and tenth sensor 310
(S10), along the z-axis, as depicted in FIG. 3. Similarly, select
y-axis magnetic field gradients (.DELTA.B.sub.xyz/.DELTA.y) are
calculated using Equation 4 for magnetic fields measured with first
sensor 301 (S1), second sensor 302 (S2), third sensor 303 (S3),
fourth sensor 304 (S4), fifth sensor 305 (S5), sixth sensor 306
(S6), and seventh sensor 307 (S7), along the y-axis, as depicted in
FIG. 3. Exemplary measured magnetic field gradients are plotted in
FIG. 15.
[0074] FIG. 15 shows a plot 1500 of exemplary measured magnetic
field gradients in the x-axis versus scan position. Plot 1500 is
determined by data processing module 150 from magnetic field 140 of
defect 350 measured using sensor array 300 of FIG. 3 for example.
Dataset 1510 shows a first gradient .DELTA.B.sub.x between first
sensor 301 and second sensor 302. Dataset 1520 shows a second
gradient .DELTA.B.sub.x between first sensor 301 and third sensor
303. Dataset 1530 shows a third gradient .DELTA.B.sub.x between
first sensor 301 and fourth sensor 304. Dataset 1540 shows a fourth
gradient .DELTA.B.sub.x between third sensor 303 and fourth sensor
304. Dataset 1550 shows a fifth gradient .DELTA.B.sub.x between
third sensor 303 and fifth sensor 305.
[0075] Although step 1420 is described above including measured
magnetic field gradients, it should be appreciated that other
measured magnetic field derived features (other than gradients)
could be utilized in step 1420. For example, instead of gradients,
step 1420 may calculate measured magnetic field hessians, wavelets,
power spectral density, or fractal dimension without departing from
the scope hereof. As such, it should be appreciated that, although
equations 2-4 above show the formula for gradients, step 1420 may
be implemented based on similar formulas for many other magnetic
field derived features that are derived from the magnetic field
sensor data, such as those magnetic field derived features
discussed above.
[0076] In an embodiment, method 1400 includes optional step 1430,
wherein at least one model of magnetic field derived features is
calculated from modeled magnetic fields for a plurality of scan
positions. In an example of step 1430, modeled magnetic field
gradients shown in FIG. 16 are calculated by data processing module
150 (or server 160) using Equations 2-4 from model magnetic fields
calculated using Equation 1 for select pairs of sensors and a
plurality of scan positions. In an alternative embodiment, modeled
magnetic field features are calculated from historical data as
found in database 162 for the same location. For example, if the
same ferromagnetic material 130 was previously scanned using method
600, the measured magnetic field features are used as a model for
comparison with repeat measurements. This approach enables (a)
monitoring a small anomaly that may be a defect over time to
determine if it is growing in size; growth in size is more likely
associated with a developing defect than with a weld or flange.
[0077] FIG. 16 shows a plot 1600 of exemplary magnetic field
gradients in the x-axis versus scan position calculated by data
processing module 150 (or server 160) for a dipole model of a
defect, such as defect 350 of FIG. 3. Dataset 1610 shows a first
gradient .DELTA.B.sub.x between first sensor 301 and second sensor
302. Dataset 1620 shows a second gradient .DELTA.B.sub.x between
first sensor 301 and third sensor 303. Dataset 1630 shows a third
gradient .DELTA.B.sub.x between first sensor 301 and fourth sensor
304. Dataset 1640 shows a fourth gradient .DELTA.B.sub.x between
third sensor 303 and fourth sensor 304. Dataset 1650 shows a fifth
gradient .DELTA.B.sub.x between third sensor 303 and fifth sensor
305. Again, it should be appreciated that step 1430 is not limited
to magnetic field gradients, but can be implemented based on other
magnetic field derived features such as those discussed above.
[0078] In step 1440, measured magnetic field derived feature data
are compared to modeled magnetic field feature data for a plurality
of scan positions to identify one or more phenomena in magnetic
field features caused by welds, defects, or anomalies in the
ferromagnetic material. In an example of step 1440, multiple
measured magnetic field gradients from sensor array 300, such as
those shown in FIG. 15, are compared, using data processing module
150 (or server 160), to modeled magnetic field gradients, such as
those shown in FIG. 16, to identify a phenomenon in magnetic field
gradients caused by defect 350 of ferromagnetic material 330 of
FIG. 3. As part of step 1440, measured and modeled data may be
analyzed for correct dipole orientation based on dipole model
gradients.
[0079] According to an embodiment, select magnetic field phenomena
containing a defect signature are used to identify defect 350.
According to another embodiment, step 1440 includes an optional
step 1442 of incorporating data from non-magnetic sensors 252 of
FIG. 2 to further enhance characterization of ferromagnetic
material 130. In one example, non-magnetic sensors 252 provide
ground penetrating radar used to measure standoff distance 312. In
another example, data processing module 150 utilizes GPS location
information provided by GPS 156 for each magnetic field
measurement, which may be augmented by one or both of Wide Area
Augmentation System (WAAS) data and odometer data.
[0080] In an optional step 1450, one or more defects or
irregularities of a ferromagnetic material are characterized, and
their locations and classifications may be reported in step 1460.
In an example of step 1450, defect 350 of FIG. 3 is identified and
characterized. In an example of step 1460, location of defect 350
is reported to server 160 and stored in database 162, FIG. 1.
Reporting location of defects and irregularities includes
displaying two and three-dimensional plots on interface 265 of data
processing module for example. Depending on the type of phenomenon
identified, a more intrusive inspection, such as digging out an
underground pipe for visual inspection, may be performed in the
identified locations.
[0081] Characterization of a defect by data processing module 150
in step 1450 may include determining its size and orientation, and
may further include classifying a type of missing metal defect.
Characterization may include distinguishing between a defect and a
non-defect such as a weld, flange, coupled branch line, bend, or
other normal or intentional anomaly. Identification and
characterization of defects and irregularities may be assisted
using information from different sensor types and prior magnetic
sensor data for the same location. Method 1400 provides advantages
for identifying and characterizing phenomena in ferromagnetic
material including that the method may be automated and is
repeatable.
[0082] FIG. 17 shows an exemplary method 1700 for determining a
model, and thus a signature, for observed magnetic field gradients.
Method 1700 is an embodiment of aspects of FIG. 14.
[0083] In one embodiment, method 1700 includes a step 1710 of
plotting magnetic field data for a plurality of locations and a
plurality of sensors via interface 265 for analysis by a user to
determine a nearest sensor to a magnetic field source. For example,
plot 800 of FIG. 8 may be analyzed for a weld signature from
measurements made of weld 535 of FIG. 5 using method 600 of FIG. 6.
Step 1710 may occur in method 1400 prior to step 1410.
[0084] In a step 1720, a nearest sensor of the sensor array to a
phenomenon of the ferromagnetic material is determined. In one
embodiment, data processing module 150 determines the nearest
sensor. In an example of this embodiment of step 1720, processor
264 executes a portion of software 263 and/or firmware 261 to
process magnetic field data generated by sensors 301-310 of FIG. 3
to determine that sensor 304 of FIG. 3 is nearest defect 350. In
another embodiment, a user identifies sensor 304 as the nearest
sensor to defect 350 by visually inspecting magnetic field plots
displayed in step 1710. Step 1720 may occur in method 1400 between
steps 1410 and 1420.
[0085] In a step 1730, magnetic field data from the nearest sensor,
measured over a plurality of scan positions, are analyzed for known
signatures. In an example of step 1730, using data processing
module 150, magnetic field data from nearest sensor 304 of FIG. 3
are analyzed for signatures of one or more known phenomena in
ferromagnetic material, such as weld 535 of FIG. 5. In an
embodiment of step 1730, measured magnetic fields versus scan
position along the ferromagnetic material, such as in plot 800 of
FIG. 8, are compared to a magnetic dipole model versus scan
position, such as in plot 900 of FIG. 9. In an embodiment of step
1730, known signatures are analyzed via data processing module 150
using matched filters and statistical-detection algorithms.
[0086] If a signature is found in step 1730, a step 1740 isolated a
portion of the magnetic field data that matches a known signature.
In an example of step 1740, using data processing module 150,
magnetic field data corresponding to a weld signature from weld 535
of FIG. 5 are isolated from magnetic field data of first and second
pipe segments 531, 532. According to an embodiment, a user crops
magnetic field data using data processing module 150 to isolate a
weld signature. For example, plot 800 of FIG. 8 may be cropped
between scan positions to a narrower window ranging from -1.7 m to
1.8 m to isolate the weld signature.
[0087] Steps 1730 and 1740 may occur in method 1400 between steps
1440 and 1450. For example, if steps 1730 and 1740 are used in
method 1400, step 1730 may act to filter out known non-defects
(such as welds) from the phenomenon identified in step 1440. Steps
1730 and 1740 may utilize non-magnetic sensors, such as GPS, and
ground penetrating radar, as discussed above with respect to step
1442 to further enhance identification of known non-defects in
method 1700.
[0088] In a step 1750, a characterization is determined for the
segment of ferromagnetic material having a phenomenon. Step 1750
acts to identify the phenomenon as defects, and then potentially
characterize said identified phenomenon as a specific type of
defect. The characterization and phenomenon location are then
reported in step 1460, FIG. 14. In an example of step 1750, using
data processing module 150, magnetic flux leakage at weld 535 of
pipe 530, FIG. 5 is analyzed to determine a magnetization direction
and a magnetization amplitude (or strength) for first and second
segments 531, 532.
[0089] In an embodiment, using data processing module 150 (or
server 160), modeled magnetic data is modeled as a linear subspace
of components of the magnetic signal over scan position, such as
gradients, wavelets, and power spectral density. The magnetic
signal components are extracted from a physics-based model, such as
a dipole model, and corrupted with noise and interference from one
or more magnetic sources to make the model more realistic. Magnetic
sensor measurements are then projected onto the subspace spanned by
dipole moments, or any function of the magnetic dipole moments,
such as gradients, Hessians, wavelets, power spectral density, or
fractal dimension of other magnetic field derived features
discussed above. Equation 5(a) shows an example linear subspace
model.
X=S.theta.+F.phi.+U.psi.+n Equation 5(a):
[0090] In Equation 5(a), Xis a gradient measurement vector across
scan positions, S is a feature subspace basis matrix across scan
positions in terms of gradients, F is a known magnetic interference
subspace such as a bias or flange, U is an unknown magnetic
interference subspace matrix, n is a noise vector, and .theta.,
.phi., and .psi. are scaling parameter vectors determined from
measurements. U may be constructed as the matrix orthogonal to a
concatenation of S and F.
[0091] Again, it should be appreciated that X may represent feature
measurement vectors other than gradient. For example, within
Equation 5(a), the subspace basis matrix S is based on gradients,
but it should be appreciated that the subspace basis matrix S may
be based on other magnetic field measurements such as those
magnetic field derived features discussed above. In an embodiment,
subspace basis matrix S is physics dipole moment based. In this
embodiment, the phenomena of interest within the measured magnetic
field data are made of dipoles (geometric shapes discussed above),
with a varying magnitude (small vs. large defects, defects vs.
weld, etc.) In another embodiment, the subspace basis matrix S is
constructed based on learning techniques such as Singular Value
Decomposition (SVD), Espirit, and Music algorithms.
[0092] Equation 5(a) linearly models the phenomenon identified
within the magnetic field raw data. Using equation 5(a), data
processing module 150 (or server 160) can both identify and
characterize a detected phenomenon within the measured magnetic
field data. For example, within data processing module 150 (or
server 160) and using equation 5(a), for a given phenomenon, a
window size W is selected. Within that window, magnetic field
derived features are determined. The window size W may be adjusted
for sensitivity to features of different sizes. For example, a
small window size W may be used to aim detection at small-scale
features, whereas a larger window size W may be used to aim
detection at larger-scale features. In another example, the same
dataset may be analyzes using two or more different window sizes to
be sensitive to features of a variety of sizes. In the above
example of gradients, computations of equations 2-4, over the
determined window W, derived from all possible pairs of sensor
measurements, provide the canonical shape of what a gradient of the
magnetic field for any event looks like. Equation 5(a)'s modulation
by the vector .theta. determines whether a dipole moment based
phenomenon is present. If the magnitude of .theta. is above a
threshold, then the phenomena contains a defect (or in other words
a defect is detected). The direction of the vector .theta. may be
utilized to characterize the phenomena, as discussed below. 4) The
matrix F represents other known events that may be non-dipole
moment based, or different. F is computed as in equation 3.
[0093] It should be appreciated that non-linear models may be
utilized instead of the linear model shown in equation 5(a). For
example, non-linear models would include an equation 5(b).
X=S(.theta.)+F(.phi.)+n Equation 5(b):
[0094] S, F are a non-linear function of .theta., .phi.. Under
equation 5(b), either S, F, or both, may be learned using
non-linear curve fitting, neural networks, deep-learning
algorithms, etc. For each phenomenon within the measured magnetic
field data, S (or F) may have its own shape.
[0095] A hypothesis test may be used to determine whether the
measured magnetic field data does not (null hypothesis, H0) or does
(first hypothesis, H1) include a phenomenon signature that is a
defect. Equations 6 and 7 state an exemplary hypothesis test based
on equation 5(a), but may be modified as understood by those of
ordinary skill based on equation 5(b), above.
H0: X=F.phi.+N.psi.+n Equation 6:
[0096] Equation 6 shows null hypothesis, H0, which states that the
gradient measurement vector across scan positions, X, is due to (a)
known interference subspace, F, plus (b) a subspace N which is the
subspace orthogonal to the projection of subspace S onto the
subspace orthogonal to known interference subspace F, and (c) noise
vector n. Herein, each of F, N, and S interchangeably refers to the
respective matrix as well as the subspace spanned by the columns of
the matrix.
H1: X=S.theta.+F.phi.+n Equation 7:
[0097] Equation 7 shows first hypothesis, H1, which states that the
gradient measurement vector across scan positions, X, is due to
feature subspace basis matrix across scan positions in terms of
gradients, S, plus known interference subspace, F, and noise vector
n.
[0098] The output of the hypothesis test of Equations 6 and 7 is a
statistic proportional to the likelihood, L, of a phenomenon being
present. Equations 6 and 7 may be graphically understood with
respect to FIGS. 9A-D, where hypothesis H0 is shown in FIGS. 9A and
9B because only welds are shown and the likelihood never crosses
threshold. By contrast, FIGS. 9C-D show hypothesis H1 because
defects 1 and 2 are shown and the likelihood crosses the
threshold.
[0099] Thus, it is shown that a defect may be identified in a
binary manner (e.g. presence versus absence of defect, but not yet
classified to determine the type of defect). The likelihood
compares the observed value X of equation 5 to a threshold. This
decision may be made by selecting the most likely event, which is
the phenomenon in a dictionary of phenomena that most closely
resembles the measurement X, preferably (but not necessarily) after
accounting for noise in the data. This decision may utilize a
hypothesis test, as shown in equations 6 and 7, or
alternatively/additionally, a nearest neighbor model, or any other
pattern classification/machine learning/deep-learning algorithm. To
compensate for noise, statistic used thereby may be a Chi-Square
statistic, an F statistic, or non-Gaussian generalization of the
Chi-Square or F statistic such as those discussed in: M N Desai, R
S Mangoubi, "Robust Gaussian and non-Gaussian matched subspace
detection," IEEE Transactions on Signal Processing, 2003.
[0100] It should be appreciated that functions other than the
likelihood function may be utilized, such as the robust likelihood
function which is a trimmed version of the likelihood function that
protects against noise outliers. Moreover, the estimate of .theta.,
.phi., or {circumflex over (.theta.)}, {circumflex over (.phi.)},
may be obtained by inverting the matrix or functions (non-linear
models) S, F, respectively. The magnitude and direction of these
vectors may then be used instead of the likelihood function.
Embodiments where the noise model is unknown and the non-parametric
approach is used, may use non-parametric statistics such as the
sign test, the rank sum test, rank histograms of the noise,
etc.
[0101] The magnitude of phenomenon scaling parameter vector,
.theta., may be a statistic for determining the presence of a
phenomenon, the size of the phenomenon, and the magnetization
direction of the phenomenon.
[0102] In an embodiment, modeled magnetic data is modeled as a
non-linear subspace of components of the magnetic signal versus
scan position, such as a polynomial, neural network, or
learning-based technique, fitted to a measured magnetic field data
curve. The coefficients of the non-linear subspace may include
components that determine the presence of phenomena and
characterize the nature of those phenomena. In another embodiment,
a fractal dimension of the measured magnetic field data is used to
determine the presence of phenomena and to characterize the nature
of those phenomena.
[0103] It should be appreciated that the models of Eq. 5(a) and
5(b) may be replaced by models not based on feature subspaces S and
F.
[0104] FIG. 18 is a flowchart for a method 1800 to identify a
phenomenon within ferromagnetic material by comparing modeled and
magnetic field data over a variable window of scan positions. In
step 1810, method 1800 compares modeled and magnetic field data,
such as gradient data, from a small window of scan positions
corresponding to a portion of a ferromagnetic material. In one
example of operation of step 1800, data processing module 150
compares modeled magnetic field data to captured magnetic field
data, captured using one or more of sensors 350 of FIG. 3,
corresponding to a window of scan positions along ferromagnetic
material 130. Method 1800 is an example of steps 1440-1450 and 1750
of FIGS. 14 and 17, respectively.
[0105] Step 1820 is a decision. If step 1820 determines that a
likelihood, L, has crossed a predefined threshold indicating that a
phenomenon is present in the ferromagnetic material, then method
1800 proceeds with step 1860. Otherwise, method 1800 proceeds with
step 1830 to increase window size. In an example of step 1820, L
has crossed a predefined likelihood threshold of for example one
(L>1), as shown in FIGS. 9C and 9D, indicating presence of a
defect within a scan position window from zero to one along the
x-axis. In another example of step 1820, L has not crossed the
predefined threshold of one (L<1) in a scan window from zero to
one, as shown in FIGS. 9A and 9B, indicating absence of a defect.
The predefined likelihood threshold may take on other values than
one, without departing from the scope thereof. For example, the
predefined likelihood threshold may depend on whether or not the
likelihood L has been normalized and the nature of such
normalization. Step 1820 is an example of step 1450 and 1750 of
methods 1400 and 1700, respectively.
[0106] In optional step 1830, the window size is increased. In an
example of step 1830, the window for comparing measured and modeled
magnetic field data is increased to the entire range of zero to two
shown in FIGS. 9A-9D. Window as used herein means the number of
data points surrounding, or beginning from, a given scan position
in the measured magnetic field data.
[0107] Step 1840 is a decision. If, in step 1840, the window size
has been increased to maximum, method 1800 proceeds to step 1850,
which determines that no defect is present in the corresponding
portion of ferromagnetic material. Otherwise, method 1800 returns
step 1820 to determine if the likelihood threshold has been
crossed. In an example of step 1840, the window size corresponds to
scan positions taken along first segment 531 of pipe 530, FIG. 5,
which is not a maximum window and method 1800 returns to step 1820.
Steps 1830 to 1860 together form an example of step 1440 of method
1400.
[0108] In step 1860, a magnetic field source is identified. In an
example of step 1860, a magnetic field phenomenon is identified
from defect 450, FIG. 4.
[0109] Step 1870 is a decision. If in step 1870, a large window is
determined to have been used, then a non-defect is determined. In
an example of step 1830, a window covering scan positions for first
and second pipe segments 531, 532 of FIG. 5 was used and the
magnetic source identified in step 1860 was from weld 535.
Otherwise, if a large window was not used, for example the window
includes data from only first pipe segment 531, method 1800
proceeds to step 1890, which determines that a defect is present
within the scan positions of the ferromagnetic material
corresponding to the window. Steps 1860 and 1870 are examples of
step 1450 of FIG. 14.
[0110] Method 1800 uses data windows and may apply steps 1820 to
1840 repeatedly to identify phenomena having different sizes. For
example, method 1800 may repeat for each, or a portion, of scan
positions within the measured magnetic field data received from
sensors 110, 310, 410. Method 1800 may be implemented in a parallel
or hierarchical manner, using multiple windows without departing
from the scope hereof.
[0111] FIG. 19 shows a pairwise statistical comparison plot 1900
for characterizing ferromagnetic material. Pairwise statistical
comparison plot 1900 may be utilized by methods 1400, 1700, and
1800 to specifically characterize the type of phenomenon, and in
some embodiments the type of defect. That is, in addition to
determining that a phenomenon occurs within the measured magnetic
field data, methods 1400, 1700, and 1800 may utilize plot 1900, or
the data therefrom, to determine what the phenomenon is (i.e. type
of weld, type of defect, type of anomaly, etc.). Plot 1900 can be
stored in server 160 or data processing module 150 and can identify
a library of phenomenon that can been seen in the field by systems
100, 200, 400, as well as how different one known phenomenon is to
another known phenomenon.
[0112] Pairwise statistical comparison plot 1900 is built by
comparing the measure of divergence for each pair of phenomena.
Specifically, FIG. 19 shows pairwise statistical comparison plot
1900 of features extracted from the measured magnetic field data
1920 (such as the angle between the vector .theta. for different
phenomena) versus modeled magnetic field 1930 for ten different
phenomena 1901-1910. In another embodiment, a finite element based
model is used in place of modeled magnetic field 1930. The ten
phenomena include for example three welds 1901, 1902, 1903, which
are examples of weld 535, FIG. 5; phenomenon 1904 which is a small
defect; phenomenon 1905 which is a detectable defect, such as
defect 450, FIG. 4; and, phenomena 1906-1910 which include other
miscellaneous anomalies. Each value in the matrix represents a
numerical divergence between pairwise comparisons of measured and
modeled magnetic field data for each of the ten phenomena
1901-1910. For example, column 4 "1904", row 1 "1901" represents a
pairwise comparison of small defect 1904 to weld 1901. A difference
between measured and modeled data is shown with legend 1940. The
entries in FIG. 19 are a measure of the statistical divergence
between two phenomena, such as a weld and a defect. As such, in
FIG. 19, phenomena 1901 and 1908 are separated by a small
divergence and are therefore relatively similar, when contrasted to
phenomena 1901 and 1905. Alternatively, phenomenon 1901 is more
similar to phenomenon 1908, than it is to phenomenon 1905.
[0113] The measure of divergence may be based on many variables,
and more than one variable may be used to build the pairwise
statistical plot of FIG. 19. For example, for two phenomena, we
have two estimates of the vector .theta., or , . The angle between
these vectors may be a measure of divergence. The larger the
angles, the more distinct are the phenomena (e.g. the larger the
divergence), and vice versa. If that angle is not above a
threshold, then the phenomena pair is not distinguishable. The
threshold may be based on the quality of the measurement, or the
sensor noise variance or signal to noise ratio. Other divergences
may also be utilized, for example, when non-parametric noise
methods are preferred, divergence between histograms or rank
histograms may be used. One example is the Kullback Leibler
divergence. Divergences derived from machine learning methods are
also possible.
[0114] To specifically characterize a detected phenomenon using
plot 1900, data processing module 150 (or server 160), implementing
methods 1400, 1700, or 1800 may utilize a statistic from the test
of equations (6) or (7), for instance. Take the case where the
matrix F is zero (which could also mean that the matrices S and F
are aggregated). The likelihood ratio is compared to a threshold,
determining that a phenomenon of interest is present, as discussed
above. In turn, data processing module 150 may obtain the estimate
of vector {circumflex over (.theta.)}, and compare it to the value
vector , where e can be any of the events 1901 thru 1910. The
comparison is based on the angle between vector {circumflex over
(.theta.)} and the given vector . The comparison yielding the
smallest angle indicates the observed phenomenon.
[0115] Pairwise statistical plot 1900 may include a machine
learning feature where, if the smallest angle between .theta., and
.theta._e, for all events e is above a certain threshold, then the
answer would be "event or phenomenon not seen before".
[0116] It should be appreciated that the plot 1900 may be just one
of many plots analyzed by data processing module 150 (or server
160). For example, there may be multiple plots for each given
window size. In such a case, data processing module 150 may obtain
multiple divergences for the same pair and fuse at the higher
decision level using decision fusion methods, which may be learned
using machine learning. Moreover, the system could fuse at the
divergence level, and obtain a single fused diversion method, prior
to decision.
[0117] FIGS. 20A, 20B, and 20C show three diagrams of exemplary
schemes for combining magnetic field data with data from other
sensing modalities, such as ground penetrating radar, multimodal
cameras, tomographic measurements, ultrasonic measurements, and
active modulated magnetic signals for signal-to-noise ratio
enhancement. FIGS. 20A-C are for example diagrams of schemes
implementing step 1442 of FIG. 14. Any details extracted from
different measurements may be fused, at different levels, such as a
measurement level, a data extraction level, or a determination of
defect versus non-defect level. FIG. 20A shows a diagram for fusing
data from first, second, third and fourth modalities 2010, 2020,
2030, 2040 at a measurement level in step 2050, followed by
extracting phenomenon data in step 2060, determining defect versus
non-defect in step 2070, and optionally characterizing a defect and
its location in step 2080.
[0118] FIG. 20B shows a diagram for fusing data at a phenomenon
level. Specifically, phenomenon data are extracted for each of the
four modalities in steps 2011, 2021, 2031, 2041 and fused in step
2061, followed by determining defect versus non-defect in step 2071
and optionally characterizing a defect and its location in step
2080.
[0119] FIG. 20C shows a diagram for fusing data at a defect
determining level. Specifically, a defect versus non-defect is
determined in steps 2012, 2022, 2032, 2042 from the four modalities
2010, 2020, 2030, 2040 and the determinations are fused in step
2072 to determine defect versus non-defect, and optionally
characterizing a defect and its location in step 2080.
[0120] Changes may be made in the above methods and systems without
departing from the scope hereof. It should thus be noted that the
matter contained in the above description or shown in the
accompanying drawings should be interpreted as illustrative and not
in a limiting sense. The following claims are intended to cover all
generic and specific features described herein, as well as all
statements of the scope of the present method and system, which
might be said to fall there between.
* * * * *