U.S. patent application number 17/687566 was filed with the patent office on 2022-09-15 for system for inspecting equipment and materials for quality.
This patent application is currently assigned to SMARTINSIDE AI Inc.. The applicant listed for this patent is SMARTINSIDE AI Inc.. Invention is credited to Han Sun KIM, Seung Hee PARK.
Application Number | 20220291168 17/687566 |
Document ID | / |
Family ID | 1000006212095 |
Filed Date | 2022-09-15 |
United States Patent
Application |
20220291168 |
Kind Code |
A1 |
PARK; Seung Hee ; et
al. |
September 15, 2022 |
SYSTEM FOR INSPECTING EQUIPMENT AND MATERIALS FOR QUALITY
Abstract
Disclosed herein is a system for inspecting equipment and
materials for quality. The system for inspecting equipment and
materials for quality includes: a magnetic sensor configured to
generate a magnetic field in an inspection target object, and to
detect magnetic flux density; and a quality inspection server
configured to determine the presence of a defect, a portion where
the detect is present, and the type of defect for the inspection
target object based on magnetic flux density waveforms over a range
from one end of the inspection target object to the other end
thereof that are generated via signals detected by the magnetic
sensor.
Inventors: |
PARK; Seung Hee;
(Seongnam-si, KR) ; KIM; Han Sun; (Miryang-si,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SMARTINSIDE AI Inc. |
Suwon-si |
|
KR |
|
|
Assignee: |
SMARTINSIDE AI Inc.
Suwon-si
KR
|
Family ID: |
1000006212095 |
Appl. No.: |
17/687566 |
Filed: |
March 4, 2022 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R 33/02 20130101;
G01N 27/83 20130101 |
International
Class: |
G01N 27/83 20060101
G01N027/83; G01R 33/02 20060101 G01R033/02 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 11, 2021 |
KR |
10-2021-0031876 |
Claims
1. A system for inspecting equipment and materials for quality, the
system comprising: a magnetic sensor formed in a hollow form, and
configured to generate a magnetic field in an inspection target
object passing through a central empty space of the magnetic
sensor, and to detect magnetic flux density; and a quality
inspection server configured to determine a presence of a defect
and a type of defect for the inspection target object based on
magnetic flux density waveforms over a range from one end of the
inspection target object to a remaining end thereof that are
generated via signals detected by the magnetic sensor; wherein the
quality inspection server: determines that a crack has occurred in
a portion where one peak point appears in an area from the one end
of the inspection target object to the remaining end thereof when
in the magnetic flux density waveforms, there is a region in which
a magnitude of the magnetic flux density exceeds a first threshold
value and there is only the corresponding one peak point in a
waveform of the magnetic flux density which exceeds the first
threshold value; and determines that corrosion has occurred in a
region of the inspection target object where the magnetic flux
density exceeding a second threshold value appears in the area from
the one end of the inspection target object to the remaining end
thereof when in the magnetic flux density waveforms, there is a
region in which a magnitude of the magnetic flux density exceeds
the second threshold value and there is a plurality of peak points
in a waveform of the magnetic flux density which exceeds the second
threshold value; wherein the peak point is a point where a slope
changes from a positive (+) value to a negative (-) value when a
curve increases along a positive (+) slope and then decreases along
a negative (-) slope in a corresponding magnetic flux density
waveform; and wherein the quality inspection server: when it is
determined that a crack has occurred in the inspection target
object, calculates a crack score via the following equation: P
.times. 1 = k .times. 1 .times. .DELTA. .times. F .DELTA. .times. L
.times. F p .times. .DELTA. .times. W ##EQU00003## where P1 is the
crack score, k1 is a constant, .DELTA.L is a unit length, .DELTA.F
is a change value of the magnetic flux density, F.sub.p is a
maximum magnitude value of the magnetic flux density, and .DELTA.W
is a width value of a region where magnetic flux density exceeding
the first threshold value appears; and when it is determined that
corrosion has occurred in the inspection target object, calculates
a corrosion score via the following equation:
P2=k2.times..DELTA.H.times..DELTA.W where P2 is the corrosion
score, k2 is a constant, .DELTA.H is a difference value between a
maximum magnitude in a magnetic flux density waveform exceeding the
second threshold value and the second threshold value, and .DELTA.W
is a width value of a region where magnetic flux density exceeding
the second threshold value appears.
2. The system of claim 1, wherein the quality inspection server
comprises a defect signal determination criteria setting unit
configured to perform learning based on a machine learning
algorithm by utilizing indexing information about types of defects
for respective magnetic flux density waveforms.
3-5. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of Korean Patent
Application No. 10-2021-0031876 filed on Mar. 11, 2021, which is
hereby incorporated by reference herein in its entirety.
BACKGROUND
1. Technical Field
[0002] The present invention relates generally to a system for
inspecting equipment and materials for quality, and more
particularly to a system capable of determining the presence of a
defect and the type of defect for equipment and materials based on
the analysis of magnetic flux density characteristics.
2. Description of the Related Art
[0003] In construction sites, there are many temporary equipment
and materials such as structures that are temporarily installed and
then dismantled for main construction.
[0004] These temporary equipment and materials are temporarily used
during a construction process and dismantled after installation,
and may be reused in other construction sites. Furthermore, for
this reason, a lot of deformation attributable to external force
inevitably occurs in temporary equipment and materials.
[0005] In large-scale construction sites, large quantities of
temporary equipment and materials are used, and it is not easy to
manage such temporary equipment and materials.
[0006] Most temporary equipment and materials are composed of steel
products. Defects occurring in these steel products may be
classified into surface defects that appear on the surfaces thereof
and internal defects that are present inside the products.
[0007] In general, surface defects appearing on steel products are
detected by a human eye or a surface inspection device equipped
with a camera or the like.
[0008] However, it is difficult to accurately detect internal
defects or defects that are not detected with the naked eye or a
camera, and a lot of manpower, time, or money is required for
detailed defect detection.
[0009] When the defect detection of temporary equipment and
materials is not performed appropriately, structural instability
may occur in the work in construction sites utilizing such
temporary equipment and materials, and work efficiency decreases
inevitably.
[0010] In addition, even when a defect is detected, it is difficult
to accurately detect the specific type of defect, so that it is not
easy to determine measures to compensate for or deal with a
defect.
SUMMARY
[0011] An object of the present invention is to overcome the
problems of the conventional technologies described above.
[0012] An object of the present invention is to enable the presence
of a defect and the type of defect for equipment and materials to
be determined in an easy way.
[0013] The objects of the present invention are not limited to the
objects described above, and other objects not described above will
be clearly understood from the following description.
[0014] According to an aspect of the present invention, there is
provided a system for inspecting equipment and materials for
quality, the system including: a magnetic sensor configured to
generate a magnetic field in an inspection target object, and to
detect magnetic flux density; and a quality inspection server
configured to determine the presence of a defect, a portion where
the detect is present, and the type of defect for the inspection
target object based on magnetic flux density waveforms over a range
from one end of the inspection target object to the other end
thereof that are generated via signals detected by the magnetic
sensor.
[0015] The quality inspection server may include a defect signal
determination criteria setting unit configured to perform learning
based on a machine learning algorithm by utilizing indexing
information about the types of defects for respective magnetic flux
density waveforms.
[0016] The quality inspection server may determine that a crack has
occurred in a portion where one peak point appears when in the
magnetic flux density waveforms, there is a region in which the
magnitude of the magnetic flux density exceeds a first threshold
value and there is only the corresponding one peak point in a
waveform of the magnetic flux density which exceeds the first
threshold value.
[0017] The quality inspection server may determine that corrosion
has occurred in a region of the inspection target object where the
magnetic flux density exceeding a second threshold value appears
when in the magnetic flux density waveforms, there is a region in
which the magnitude of the magnetic flux density exceeds a second
threshold value and there is a plurality of peak points in a
waveform of the magnetic flux density which exceeds the second
threshold value.
[0018] The quality inspection server may calculate: when it is
determined that a crack has occurred in the inspection target
object, a crack score that is proportional to a slope value of a
corresponding waveform, the maximum magnitude value of the magnetic
flux density, and the width value of the region where the magnetic
flux density exceeding a first threshold value appears, in the
magnetic flux density waveforms; and, when it is determined that
corrosion has occurred in the inspection target object, a corrosion
score that is proportional to the width value of a region where
magnetic flux density equal to or larger than a second threshold
value appears and a difference value between the maximum value of
the magnetic flux density and the second threshold value, in the
magnetic flux density waveforms.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The above and other objects, features, and advantages of the
present invention will be more clearly understood from the
following detailed description taken in conjunction with the
accompanying drawings, in which:
[0020] FIG. 1 is a view showing the schematic configuration of a
system for inspecting equipment and materials for quality according
to an embodiment of the present invention;
[0021] FIG. 2 is a block diagram illustrating the function and
detailed configuration of a quality inspection server according to
an embodiment of the present invention;
[0022] FIG. 3 shows examples of magnetic flux density waveforms
appearing for the respective types of defects of equipment or
materials according to an embodiment of the present invention;
[0023] FIG. 4 is a view illustrating a method of calculating a
crack score according to an embodiment of the present invention;
and
[0024] FIG. 5 is a view illustrating a method of calculating a
corrosion score according to an embodiment of the present
invention.
DETAILED DESCRIPTION
[0025] For the following detailed description of the present
invention, reference is made to the accompanying drawings that show
by way of illustration specific embodiments via which the present
invention may be practiced. These embodiments will be described in
sufficient detail to enable those skilled in the art to practice
the present invention. It should be understood that various
embodiments of the present invention are different but are not
necessarily mutually exclusive. For example, a specific shape,
structure, and/or feature described herein may be implemented as
another embodiment without departing from the spirit and scope of
the invention with respect to one embodiment. In addition, it
should be understood that the locations or arrangement of
individual components within each disclosed embodiment may be
changed without departing from the spirit and scope of the present
invention. Accordingly, the following detailed description is not
intended to be taken in a limiting sense, and the scope of the
present invention, together with all ranges equivalent to the
appended claims if appropriately described, is limited only by the
appended claims. Like reference numerals in the drawings refer to
the same or similar functions throughout various aspects.
[0026] The embodiments of the present invention will be described
in detail below with reference to the accompanying drawings so that
those of ordinary skill in the art to which the present invention
pertains can easily practice the present invention.
[0027] FIG. 1 is a view showing the schematic configuration of a
system for inspecting equipment and materials for quality according
to an embodiment of the present invention.
[0028] Referring to FIG. 1, the system for inspecting equipment and
materials for quality according to the present embodiment may
include a magnetic sensor 100, a signal collection device 200, a
quality inspection server 300, and a user terminal 400.
[0029] The magnetic sensor 100, the signal collection device 200,
the quality inspection server 300, and the user terminal 400 may
communicate over an intercommunication network, e.g., a LoRa
communication network, a mobile communication network, a local area
network (LAN), a metropolitan area network (MAN), a wide area
network (WAN), the World Wide Web (WWW), and/or a wireless fidelity
(Wi-Fi) communication network.
[0030] The magnetic sensor 100 according to an embodiment serves to
generate a magnetic field in an inspection target object, e.g.,
equipment or a material, and detect a magnetic flux density formed
in the inspection target object and an area around the inspection
target object due to the generated magnetic field.
[0031] To this end, the magnetic sensor 100 may include at least
one magnetic field generation unit and at least one magnetic flux
sensor.
[0032] According to an embodiment, an inspection target object A
may generally be rod-shaped equipment or a rod-shaped material,
e.g., rod-shaped temporary equipment or a rod-shaped material. In
this case, at least one magnetic field generation unit provided in
the magnetic sensor 100, e.g., a magnet, needs to generate a
magnetic field while moving from one end of the inspection target
object A to the other end thereof in the state of maintaining a
predetermined distance to the surface of the inspection target
object A.
[0033] To this end, the magnetic sensor 100 provided with the at
least one magnetic field generation unit may be formed in a hollow
shape. In this case, the inspection target object A, e.g.,
rod-shaped equipment or a rod-shaped material, may be passed
through the central empty space of the magnetic sensor 100, and
then the inspection of the equipment or material for quality may be
performed.
[0034] The information detected by the magnetic sensor 100, i.e.,
information about a magnetic flux density for each piece of
equipment or each material, is transmitted to the signal collection
device 200. To this end, the magnetic sensor 100 may include an
information transmission unit, and the signal collection device 200
may include a multi-channel signal reception unit capable of
receiving information from a plurality of magnetic sensors 100.
[0035] The signal collection device 200 transmits collected
information to the quality inspection server 300. Such a signal
collecting device 200 may be provided, e.g., for each construction
site, factory, or equipment and material quality inspection
institution.
[0036] The quality inspection server 300 according to an embodiment
performs the inspection of each piece of equipment or each material
for quality based on the collected information. The quality
inspection is performed based on the analysis of the measured
magnetic flux characteristic of each piece of equipment or each
material. Through this, the type of defect for each piece of
equipment or each material may be determined. The analysis and
determination may be performed based on a machine learning
algorithm.
[0037] The user terminal 400 serves to receive information about
the results of the analysis from the quality inspection server 300
and to display the information in a form that can be checked by an
administrator. The user terminal 400 has a computation function,
and may be implemented in any form as long as it is a device
capable of communicating with the outside. For example, the user
terminal 400 may be implemented as a smartphone, a tablet personal
computer (PC), a desktop, a laptop, a notebook, a personal digital
assistant (PDA), or the like, but is not limited thereto.
[0038] FIG. 2 is a block diagram illustrating the function and
detailed configuration of a quality inspection server according to
an embodiment of the present invention.
[0039] Referring to FIG. 2, the quality inspection server 300 may
include an information collection unit 310, a signal processing
unit 320, a defect determination criteria setting unit 330, a
defect determination unit 340, and a determination result
transmission unit 350.
[0040] The information collection unit 310, the signal processing
unit 320, the defect determination criteria setting unit 330, the
defect determination unit 340, and the determination result
transmission unit 350 may be program modules or hardware capable of
communicating with external devices. The program modules or
hardware may be included in the quality inspection server 300 in
the form of an operating system, application program modules, or
other program modules, and may physically be stored in various
types of known storage devices. Meanwhile, these program modules or
hardware include, but are not limited to, routines, subroutines,
programs, objects, components, and/or data structures that perform
specific tasks to be described later or execute specific abstract
data types according to the present invention.
[0041] The information collection unit 310 serves to receive
information, i.e., magnetic flux density information about each
piece of equipment or each material, detected by the magnetic
sensor 100, through the signal collection device 200. Since it is
necessary to identify an inspection target object for which each
piece of received information has been detected, the received
magnetic flux-related information may include the identification
information of a corresponding inspection target object.
Alternatively, the inspection sequence of inspection target objects
may be stored in the quality inspection server 300 in advance, the
information transmitted by the magnetic sensor 100 may sequentially
be received, and then the pieces of magnetic flux-related
information may be matched with the respective inspection target
objects.
[0042] The signal processing unit 320 serves to process the signals
collected by the information collection unit 320.
[0043] According to an embodiment, the magnetic flux-related
information for each inspection target object may be a waveform
representative of a magnetic flux density change pattern that the
magnetic sensor 100 detects while moving from one end of the
corresponding inspection target object, e.g., temporary equipment,
a temporary material, or a steel rod, to the other end thereof.
[0044] In this case, the signal processing unit 320 may perform
noise removal, drift correction, and offset correction in the
corresponding waveform. The noise removal may be performed using a
low-pass filter having preset parameters. The drift correction may
be performed by moving a waveform by a predetermined range with
respect to an axis when the reference point or start point of the
waveform does not match a preset standard point. In addition, the
offset correction may be performed in such a manner as to correct
the origin of a waveform when the origin of the waveform received
by the information collection unit 310 is incorrect. The operations
performed by the signal processing unit 320 are not limited
thereto, and various types of signal processing operations may be
further added.
[0045] The defect determination criteria setting unit 330 serves to
index the types of defects based on the signals processed by the
signal processing unit 320, which are waveforms appearing for the
respective types of inspection target objects, and then learn them,
and also serves to set threshold values for the determination of
the types of defects for respective waveforms.
[0046] First, the magnetic flux density detection results of
inspection target objects, i.e., waveforms for magnetic flux
density patterns obtained from the surfaces of the inspection
target objects, are learned through a neural network structure. In
this case, the process of inputting information about the types of
defects of actual inspection target objects for respective
waveforms, i.e., an indexing process, may also be performed.
[0047] The present invention adopts a learning method using a deep
learning technique, which is a type of machine learning. Machine
learning is a branch of artificial intelligence, and has evolved
from studies of pattern recognition and computer learning
theories.
[0048] Machine learning improves a knowledge base by using
surrounding environments as training elements. A specific task is
performed using the improved knowledge base, and the information
obtained during the performance of the task is reflected in the
training elements again. Machine learning is a technique that
studies and constructs a system and algorithm for performing
learning based on empirical data in the above manner, making
predictions, and improving its own performance. Machine learning
algorithms use a method of constructing a specific model to make a
prediction or decision based on input data.
[0049] Machine learning may be classified into a method of rote
learning and direct provision of new knowledge, a supervised
learning method, a learning-by-analogy method, and an inductive
learning method according to their learning strategies. The present
invention may use at least one of the above learning methods.
[0050] Through the above-described learning, it may be possible to
determine the presence of a defect and the type of defect in
equipment or a material for each magnetic flux density pattern
appearing in the equipment or the material.
[0051] FIG. 3 shows examples of magnetic flux density waveforms
appearing for the respective types of defects of equipment or
materials according to an embodiment of the present invention.
[0052] Referring to FIG. 3(a), when the magnitude of a waveform
corresponding to a magnetic flux density pattern that the magnetic
sensor 100 measures while moving from one end of equipment or a
material to the other end thereof is not constant, there is a
region in which the magnitude of a magnetic flux density exceeds a
first threshold value TH1, and there is only one peak point A1 in
the waveform of the magnetic flux density exceeding the first
threshold value TH1, it may be determined that a crack has occurred
in the portion where the corresponding peak point appears. Since
the occurrence of a crack implies that a cross-sectional loss has
occurred at a single point, only the single peak point P1 appears
at the corresponding cracking point, as described above.
[0053] Referring to FIG. 3(b), when the magnitude of a waveform
corresponding to a magnetic flux density pattern that the magnetic
sensor 100 measures while moving from one end of equipment or a
material to the other end thereof is not constant, there is a
region in which the magnitude of a magnetic flux density exceeds a
second threshold value TH2, and there is a plurality of peak points
B1, B2, and B3 in the waveform of the magnetic flux density
exceeding the second threshold value TH2, it may be determined that
corrosion has occurred in the region of the inspection target
object where the magnetic flux density exceeding the second
threshold value TH2 appears.
[0054] Corrosion is caused by the rusting of the cross sections of
an inspection target object, so that a cross section loss occurs
over a wide range. Accordingly, a plurality of peak points occurs
in a magnetic flux density waveform exceeding the second threshold
value TH2 and the magnetic flux density rises at each of the
cross-sectional loss points, with the result that the largest
magnetic flux density appears in the central region of the region
where corrosion occurs.
[0055] In the above description, the "peak point" refers to a point
where a slope changes from a positive (+) value to a negative (-)
value when a curve increases along a positive (+) slope and then
decreases along a negative (-) slope in a magnetic flux density
waveform. Furthermore, although the first threshold value TH1 and
the second threshold value TH2 may be the same as each other, they
may be different from each other.
[0056] Meanwhile, the defect determination criteria setting unit
330 may further serve to set threshold values for the determination
of the respective types of defects, e.g., the first threshold value
TH1 and the second threshold value TH2 in the above description,
based on pieces of waveform pattern information for the respective
types of defects learned through machine learning.
[0057] The defect determination criteria setting unit 330 may
determine the presence of a defect and the type of defect for each
magnetic flux density waveform via the threshold values set as
described above, and may perform a learning operation thereon.
However, only one of the learning via a machine learning algorithm
and the operation of determining the presence of a defect and the
defect type via the set threshold values may be performed.
[0058] Referring back to FIG. 2, the defect determination unit 340
serves to determine the presence of a defect and the type of defect
for a received magnetic flux density waveform based on the results
of the learning performed by the defect determination criteria
setting unit 330 or the threshold values or the results of learning
set by the combination determination criteria setting unit 330.
[0059] According to an embodiment, the defect determination unit
340 may serve to determine a defect present in a corresponding
inspection target object for the magnetic flux density waveform
information received by the information collection unit 310 and
processed by the signal processing unit 320 based on the results of
the learning performed by the defect determination criteria setting
unit 330. Alternatively, the defect determination unit 340 may
determine a defect based on corresponding received magnetic flux
density waveform information with reference to the threshold values
set by the defect determination criteria setting unit 330.
[0060] According to an embodiment, when there is only one peak
point in a waveform exceeding the first threshold value TH1 among
waveforms representing magnetic flux densities, the defect
determination unit 340 may determine that a "crack" has occurred at
the corresponding peak point. When there is a plurality of peak
points in a waveform exceeding the second threshold value TH2, the
defect determination unit 340 may determine that "corrosion" has
occurred in a corresponding region.
[0061] In addition, in the cases of a "crack" and "corrosion", the
degrees thereof may be determined as follows.
[0062] First, referring to FIG. 4, in the case of a crack, in a
waveform exceeding the first threshold value TH1, the slope
value
.DELTA. .times. F .DELTA. .times. L ##EQU00001##
of the waveform, i.e., a change value .DELTA.F in the magnetic flux
density with respect to a unit length .DELTA.L in an inspection
target object extending in the longitudinal direction, is
proportional to the depth of the crack. Furthermore, the maximum
magnitude value F.sub.p of the magnetic flux density is also
proportional to the depth of the crack. Meanwhile, the width value
.DELTA.W of a region where magnetic flux density exceeding the
first threshold value TH1 in the inspection target object appears
is proportional to the width of the crack.
[0063] Accordingly, a crack score indicating the degree of crack
may be represented as follows:
P .times. 1 = k .times. 1 .times. .DELTA. .times. F .DELTA. .times.
L .times. F p .times. .DELTA. .times. W ( 1 ) ##EQU00002##
[0064] where k1 is a constant.
[0065] Meanwhile, referring to FIG. 5, in the case of corrosion, in
a waveform exceeding the second threshold value TH2, the difference
value .DELTA.H between the maximum magnitude and the second
threshold value TH2 is proportional to the degree of corrosion, and
the width value .DELTA.W of a region where magnetic flux density
exceeding the second threshold value TH2 appears increases as the
range of corrosion increases.
[0066] Accordingly, a corrosion score indicating the degree of
corrosion may be represented as follows:
P2=k2.times..DELTA.H.times..DELTA.W (2)
[0067] where k2 is a constant.
[0068] As described above, the defect determination unit 340 may
calculate a crack score indicating the degree of crack or a
corrosion score indicating the degree of corrosion through a
magnetic flux density waveform for an inspection target object.
[0069] The determination result transmission unit 350 serves to
transmit the results of the determination performed by the defect
determination unit 340 to the user terminal 400. Furthermore, the
determination result transmission unit 350 may transmit information
about a magnetic flux density waveform processed by the signal
processing unit 320, information about the results of the learning
performed by the defect determination criteria setting unit 330,
and information about set threshold values to the user terminal
400.
[0070] The identification information of an inspection target
object to be determined and the defect-related determination
information of the inspection target object may be transmitted
together to the user terminal 400.
[0071] Via this information, an administrator may check, manage,
and classify the quality of equipment or a material.
[0072] According to an embodiment, the presence or absence of a
defect and the type of defect for equipment or a material may be
determined in an easy way.
[0073] Furthermore, according to an embodiment, the presence or
absence of a defect and the type of defect may be accurately
determined through machine learning on the magnetic flux density
patterns of inspection target objects.
[0074] The foregoing description of the present invention is
intended for illustration purposes. It will be understood by those
of ordinary skill in the art to which the present invention
pertains that each of the embodiments described above may be easily
modified into other specific forms without changing the technical
spirit or essential features of the present invention. Accordingly,
it should be understood that the embodiments described above are
illustrative but not restrictive in all respects. For example, each
component described as being in a single form may be implemented in
a distributed form, and, likewise, components described as being in
a distributed form may also be implemented in an integrated
form.
[0075] The scope of the present invention is defined by the
attached claims, and all variations or modifications derived from
the meanings and scope of the claims and their equivalents should
be construed as falling within the scope of the present
invention.
* * * * *