CN110596233B - Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling - Google Patents
Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling Download PDFInfo
- Publication number
- CN110596233B CN110596233B CN201910796522.XA CN201910796522A CN110596233B CN 110596233 B CN110596233 B CN 110596233B CN 201910796522 A CN201910796522 A CN 201910796522A CN 110596233 B CN110596233 B CN 110596233B
- Authority
- CN
- China
- Prior art keywords
- leakage
- magnetic
- image
- signal
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/0094—Sensor arrays
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/02—Measuring direction or magnitude of magnetic fields or magnetic flux
- G01R33/10—Plotting field distribution ; Measuring field distribution
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
The invention discloses a steel wire rope magnetic leakage imaging real-time processing method under continuous sampling, wherein magnetic leakage signals of a steel wire rope are acquired in sections during magnetic leakage signal acquisition, magnetic leakage imaging processing is carried out on each section of magnetic leakage signals by using a method combining primary screening and fine diagnosis, then a current magnetic leakage image is spliced with an existing spliced magnetic leakage image, wherein overlapped data sections are divided into four conditions to be processed, and the spliced magnetic leakage image which is to be obtained after splicing is completed each time is output and displayed in real time until all signals are processed. The invention can effectively improve the processing speed of magnetic flux leakage imaging, can avoid the phenomenon of misjudgment or misjudgment of the damage of the steel wire rope caused by signal segmentation, and realizes the real-time processing of the magnetic flux leakage imaging of the steel wire rope.
Description
Technical Field
The invention belongs to the technical field of steel wire rope damage diagnosis, and particularly relates to a steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling.
Background
The steel wire rope has the characteristics of high strength, high reliability, high stability and the like, is widely applied to various industrial scenes, and has the functions of lifting, traction, bearing and the like. Since the steel wire rope is usually used as a key component of large machinery, the working state of the steel wire rope will affect the running condition and safety performance of the whole equipment. Therefore, the fault diagnosis of the steel wire rope is of great significance to ensure the stability of equipment and the production safety. There are many methods for performing non-destructive testing of steel wire ropes. One common method is to collect magnetic leakage signals on the surface of the steel wire rope and use magnetic leakage images to diagnose the damage of the steel wire rope. Fig. 1 is a schematic diagram illustrating a principle of diagnosing damage to a wire rope based on a leakage flux image. As shown in fig. 1, since the leakage flux image of a defect on the surface of a wire rope is different from that of a normal wire rope surface, it is possible to perform wire rope damage diagnosis based on the leakage flux image. However, most of these methods are continuous batch data processing methods, and are difficult to use for online monitoring.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a steel wire rope magnetic leakage imaging real-time processing method under continuous sampling, which comprises the steps of collecting magnetic leakage signals of steel wire ropes in a segmented mode, carrying out magnetic leakage imaging processing on the segmented signals by using a method combining primary screening and fine diagnosis, splicing and fusing integral magnetic leakage images according to the processed imaging result, effectively improving the processing speed of magnetic leakage imaging, and avoiding the phenomenon of misjudgment or misjudgment of steel wire rope damage caused by signal segmentation.
In order to achieve the aim, the method for processing the magnetic flux leakage imaging of the steel wire rope under continuous sampling comprises the following steps:
s1: arranging a magnetic sensor array which comprises M magnetic sensors, wherein the M magnetic sensors are arranged at equal intervals along the circumferential direction of the steel wire rope, and annular armatures are arranged on the outer sides of the M magnetic sensors;
s2: the method comprises the steps that a steel wire rope generates relative motion in the axial direction of magnetic sensors, a data acquisition system periodically samples current signals of M magnetic sensors, the length of a sampling signal of each magnetic sensor in each sampling is L, the setting of a sampling period needs to enable the overlapping of two adjacent acquired signals, the overlapping length is S, S is more than 0 and less than L/2, and the sampling signal of the ith section of the steel wire rope is X i I =1,2, \ 8230;, sampling signal X i Is a data matrix of size M × L, wherein the nth sampling point data of the mth magnetic sensor is X i [m,n](ii) a After sampling is finished each time, the step S3 is carried out;
s3: sampling signal X for each segment i Respectively carrying out magnetic flux leakage imaging, wherein the specific method comprises the following steps:
two magnetic flux leakage imaging processing methods are set as required, one of the magnetic flux leakage imaging processing methods is low in calculation complexity and is marked as a magnetic flux leakage imaging processing method A, and the other of the magnetic flux leakage imaging processing methods is high in calculation complexity and accuracy and is marked as a magnetic flux leakage imaging processing method B; for each segment of the sampled signal X i Firstly, the magnetic leakage imaging processing method A is adopted to carry out magnetic leakage imaging processing and carry out damage diagnosis to obtain a magnetic leakage image Y with the size of M' multiplied by L i A M' is a preset magnetic leakage image width parameter, and if the damage diagnosis result shows that no damage exists, the magnetic leakage image Y is subjected to magnetic leakage image detection i A As a sampling signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i A If the diagnosis result shows that the damage exists, the magnetic leakage imaging processing method B is adopted to carry out magnetic leakage imaging processing and carry out damage diagnosis, and the obtained magnetic leakage image Y with the size of M' multiplied by L is obtained i B As a sampling signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i B And in the leakage magnetic image Y i Marking out the diagnosed damage area;
s4: judging whether to sample the signal X i If yes, go to step S5, otherwise go to step S6;
s5: make concatenation magnetic leakage image Z 1 ＝Y 1 ；
S6: the current leakage magnetic image Y i With the existing spliced leakage image Z i-1 Splicing is carried out, and the following four conditions are adopted:
if the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 th segment i-1 If there is no damage, the coincidence data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (L-S + 1L), ": denotes all elements in the corresponding dimension," α: β "denotes the corresponding dimension from the α -th element to the β -th element;
if the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 segment i-1 If there is a damage, the coincidence data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (:,L-S+1:L)；
If the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 segment i-1 If there is no damage, the superimposed data segment is used in the leakage magnetic image Y i Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i (:,1:S)；
If the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 th segment i-1 If there is a damage, the first half of the superimposed data segment is used in the leakage image Y i-1 The second half of the corresponding leakage image is used in the leakage image Y i Corresponding leakage images, i.e. processed coincident data segments
Represents rounding down;
finally, according to the processing result of the overlapped data segments, the current magnetic leakage image Y is processed i With the existing spliced leaky magnetic image Z i-1 Splicing is carried out, and the existing spliced magnetic leakage image Z is recorded i-1 Is M' x D, a new spliced leakage flux image Z i The expression is as follows:
Z i ＝[Z i-1 (:,1:D-S)P i Y i (:,S+1:L)]
s7: the obtained spliced magnetic leakage image Z i Outputting and displaying in real time;
s8: judging whether the processing of the sampling signal is finished or not, if not, entering a step S9, otherwise, finishing the processing;
s9: let i = i +1, return to step S3.
The invention discloses a steel wire rope magnetic leakage imaging real-time processing method under continuous sampling, which comprises the steps of acquiring magnetic leakage signals of a steel wire rope in sections during magnetic leakage signal acquisition, carrying out magnetic leakage imaging processing on each section of magnetic leakage signals by using a method combining primary screening and fine diagnosis, splicing a current magnetic leakage image and an existing spliced magnetic leakage image, wherein an overlapped data section is divided into four conditions to be processed, and outputting and displaying the spliced magnetic leakage image obtained after splicing is completed each time in real time until all signals are processed. The invention can effectively improve the processing speed of magnetic flux leakage imaging, can avoid the phenomenon of misjudgment or misjudgment of the damage of the steel wire rope caused by signal segmentation, and realizes the real-time processing of the magnetic flux leakage imaging of the steel wire rope.
Drawings
FIG. 1 is a schematic diagram of a wire rope damage diagnosis principle based on a leakage magnetic image;
FIG. 2 is a flowchart of an embodiment of a method for real-time processing of magnetic flux leakage imaging of a steel wire rope under continuous sampling according to the present invention;
FIG. 3 is a schematic diagram of a magnetic sensor array according to the present invention;
fig. 4 is a flowchart of a leakage flux imaging processing method a in the present embodiment;
fig. 5 is a flowchart of a leakage flux imaging processing method B in the present embodiment;
FIG. 6 is a schematic diagram of coincidence of sampling signals in the present invention;
FIG. 7 is a diagram illustrating a first case of overlapping data segments in the present invention;
FIG. 8 is a diagram illustrating a second case of overlapping data segments according to the present invention;
FIG. 9 is a diagram of a third case of overlapping data segments in the present invention;
fig. 10 is a diagram illustrating a fourth case of overlapping data segments in the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
Fig. 2 is a flowchart of a specific embodiment of the method for real-time processing of magnetic flux leakage imaging of a steel wire rope under continuous sampling according to the present invention. As shown in fig. 2, the method for real-time processing of magnetic flux leakage imaging of a steel wire rope under continuous sampling comprises the following specific steps:
s201: magnetic sensor array setup:
in order to collect the magnetic flux leakage signal of the steel wire rope, the invention is provided with a magnetic sensor array for collecting the magnetic flux leakage signal of the steel wire rope in the radial direction. FIG. 3 is a schematic diagram of a magnetic sensor array according to the present invention. As shown in fig. 3, the magnetic sensor array of the present invention includes M magnetic sensors, the M magnetic sensors are arranged along the circumferential direction of the steel wire rope at equal intervals, and the outer sides of the M magnetic sensors are provided with ring-shaped armatures.
S202: acquiring a magnetic flux leakage signal of the steel wire rope:
the steel wire rope generates relative motion in the axial direction of the magnetic sensors, the data acquisition system periodically samples the current signals of the M magnetic sensors, and each sampling time isThe length of a sampling signal of each magnetic sensor is L, the setting of a sampling period needs to ensure that the two adjacent sampling signals are overlapped, the overlapping length is recorded as S, S is more than 0 and less than L/2, and the sampling signal of the i-th section of steel wire rope is recorded as X i I =1,2, \8230;, sample signal X i Is a data matrix of size M × L, wherein the nth sampling point data of the mth magnetic sensor is X i [m,n]M =1,2, \ 8230;, M, n =1,2, \ 8230;, L. The invention adopts a processing mode of sampling and imaging, namely, after sampling is finished each time, the step S203 is carried out.
Generally, for convenience of comparing the steel wire rope scrap judgment standard, the sampling signal length L is set to be the signal length corresponding to the steel wire rope lay length or a multiple thereof, namely L = lambda alpha, alpha represents the steel wire rope lay length, lambda =1,2, \ 8230. The size of the overlap length S can be controlled by adjusting the relative movement speed of the steel wire rope and the signal sampling period, and the overlap length S generally needs to be more than twice the maximum possible damage sample length, namely f max ≤S＜L/2，f max Representing the maximum lesion sample length.
S203: steel wire rope magnetic flux leakage signal segmented imaging:
next, it is necessary to sample each segment of the signal X i Respectively carrying out magnetic flux leakage imaging, and in order to achieve the purpose of saving imaging processing time, the invention adopts a magnetic flux leakage imaging processing method combining coarse screening and fine diagnosis, and the specific method comprises the following steps:
two magnetic flux leakage imaging processing methods are arranged as required, one is low in calculation complexity and is recorded as a magnetic flux leakage imaging processing method A, and the other is high in calculation complexity and accuracy and is recorded as a magnetic flux leakage imaging processing method B. For each section of sampling signal X i Firstly, the magnetic leakage imaging processing method A is adopted to carry out magnetic leakage imaging processing and carry out damage diagnosis to obtain a magnetic leakage image Y with the size of M' multiplied by L i A M' is a preset magnetic leakage image width parameter, and if the damage diagnosis result shows that no damage exists, the magnetic leakage image Y is subjected to magnetic leakage image detection i A As sampled signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i A If the diagnosis result is that the damage exists, magnetic leakage imaging is adoptedThe processing method B carries out leakage flux imaging processing and damage diagnosis on the magnetic flux image, and obtains a leakage flux image Y with the size of M' x L i B As sampled signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i B And in the leakage magnetic image Y i The diagnosed lesion area is marked.
The leakage flux imaging processing methods A and B can be set according to actual needs, and in the embodiment, the leakage flux imaging processing method A refers to a method described in a patent ' Chengdu Chiuchi technologies Co., ltd. ' a method for processing and imaging a radial leakage flux signal of a steel wire rope, china, 201810789584.3.2018-11-30 '. Fig. 4 is a flowchart of the leakage flux imaging processing method a in the present embodiment. As shown in fig. 4, the specific steps of the leakage flux imaging processing method a in this embodiment include:
s401: moving average:
for the sampling signal X i The sampling signal X corresponding to each magnetic sensor i [m](i.e., sampling signal X i Row vector of data matrix) respectively obtain trend line data X of sampling signal of each magnetic sensor by using moving average method i ′[m]Sampling the signal X i [m]Subtracting trend line data X i ′[m]To obtain data DS i [m]. Data DS of M magnetic sensors i [m]Combined into a trend-removed sampling signal DS i 。
S402: determining a re-sampling line:
determining a sampling signal DS i Then sample the signal DS i And searching for a resampling line with the highest coincidence degree with the strand corrugation line and the oblique filtering span of K.
S403: data interpolation:
sampling signal DS in the circumferential direction i In each group of M leakage signals DS i [n](i.e. the sampling signal DS i Column vectors of a data matrix) to obtain a signal DI of length M i [n]Reconstructing to obtain a signal DI with a size of M' × L i In the presence of signal DI i Respectively connecting a zero matrix with the size of M 'multiplied by M' in front and back to obtain signalsDR i . At signal DR i The data is extracted along the corresponding re-sampling line, and the re-sampling line traverses all the data for filtering to remove the signal DR i A matrix of two M 'x M' in front and back, i.e. extracting the signal DR i The data of the middle L columns obtain a signal DF with the size of M' multiplied by L i 。
S404: and (3) envelope calculation:
for signal DF i In each path of data DF i [m′]Respectively calculating the amplitude of the envelope estimation, and storing the obtained data in a matrix DE with the size of M' x L i In (1).
S405: and (4) defect judgment:
presetting a defect judgment threshold th, defining a matrix DB with a size of M' × L i Traversal matrix DE i If the value of each element in the matrix is greater than or equal to the threshold th, the element is determined to belong to a defect, and the matrix DB is formed i The middle corresponding element is set to 1, otherwise to 0.
S406: calculating a Hadamard product:
will matrix DE i And matrix DB i Obtaining the matrix DO by doing Hadamard product i I.e. the matrix DO i The element in (1) is a matrix DE i And matrix DB i The product of the corresponding elements.
S407: and (3) generating a leakage magnetic image:
for the matrix DO i Imaging to obtain leakage magnetic image Y i A Wherein the matrix DO i The maximum value of the elements in (1) corresponds to the maximum value of the gray scale, and the value 0 corresponds to the minimum value of the gray scale. The resultant leakage magnetic image Y is seen i A The information includes the presence or absence of a local defect, the position, and the magnetic leakage signal intensity.
And the leakage flux imaging processing method B is further improved on the basis of the method A. Fig. 5 is a flowchart of the leakage flux imaging processing method B in the present embodiment. As shown in fig. 5, the specific steps of the leakage flux imaging processing method B in this embodiment include:
s501: moving average:
for the sampling signal X i The sampling signal X corresponding to each magnetic sensor i [m](i.e., sampling signal X i Row vector of data matrix) respectively obtain trend line data X of sampling signal of each magnetic sensor by using moving average method i ′[m]Sampling the signal X i [m]Subtract trend line data X i ′[m]To obtain data DS i [m]. Data DS of M magnetic sensors i [m]Combined into a trend-removed sampling signal DS i 。
S502: determining a resampling line:
determining a sampling signal DS i Then sample the signal DS i And searching for a resampling line with the highest coincidence degree with the strand-corrugated line and the slant filtering span of K.
S503: data interpolation:
for the sampled signals DS in the circumferential direction i In each group of M leakage signals DS i [n](i.e. the sampling signal DS i Column vectors of a data matrix) to obtain a signal DI of length M i [n]Reconstructing to obtain signal DI with size of M' x L i In the presence of signal DI i The front and the rear are respectively connected with a zero matrix with the size of M 'multiplied by M' to obtain a signal DR i . At signal DR i The data is extracted along the corresponding re-sampling line, and the re-sampling line traverses all the data for filtering to remove the signal DR i Two matrices of M 'x M' one after the other, i.e. extracting the signal DR i The data of the middle L columns obtain a signal DF with the size of M' multiplied by L i 。
S404: and (3) envelope calculation:
for signal DF i In each path of data DF i [m′]Respectively calculating the amplitude of the envelope estimation, and storing the obtained data in a matrix DE with the size of M' multiplied by L i In (1).
S405: median filtering:
in the matrix DE i Respectively connecting a zero matrix with the size of M 'multiplied by M' to obtain a matrix DP i To the matrix DP i The signal corresponding to each magnetic sensor (i.e. matrix DP) i Row vector of) to obtain a matrix DP i '. By using median filteringAnd removing the influence of the shaking of the steel wire rope. Then removing the matrix DP i The two preceding and succeeding M 'x M' matrices, i.e. the extraction matrix DP i The data of the middle L columns obtain a matrix DD with the size of M' multiplied by L i 。
S406: and (4) defect judgment:
presetting a defect judgment threshold th, defining a matrix DB with a size of M' × L i Traversal matrix DD i If the value of each element in the array is greater than or equal to the threshold th, the element is determined to belong to the defect, and the matrix DD is formed i The middle corresponding element is set to 1, otherwise to 0.
S407: calculating a Hadamard product:
the matrix DD i And matrix DB i Obtaining the matrix DO by doing Hadamard product i I.e. the matrix DO i The element in (3) is a matrix DD i And matrix DB i The product of the corresponding elements.
S408: and (3) generating a magnetic leakage image:
for the matrix DO i Imaging to obtain leakage magnetic image Y i A Wherein the matrix DO i The maximum value of the element(s) in (1) corresponds to the maximum value of the gray scale, and the value 0 corresponds to the minimum value of the gray scale.
S204: judging whether to sample the signal X i I =1, and if so, indicates the currently obtained leakage flux image Y i And (4) the leakage flux image of the first section of steel wire rope is not required to be spliced, and the step S205 is executed, otherwise, the step S206 is executed.
S205: make concatenation magnetic leakage image Z 1 ＝Y 1 。
S206: splicing steel wire rope magnetic flux leakage images:
next, the current leakage image Y needs to be displayed i With the existing spliced leaky magnetic image Z i-1 And (6) splicing. In the invention, in the process of signal acquisition, the sampled signals of the front steel wire rope and the rear steel wire rope are overlapped. Fig. 6 is a schematic diagram of coincidence of sampling signals in the present invention. As shown in fig. 6, since the overlapping length of the sampled signals of the front and rear steel cables is S, the overlapping data segment can be represented by the following formula:
Q i ＝X i [m,n]＝X i-1 [m,L-S+n],1≤n≤S，1≤m≤M
in order to avoid misjudgment or misjudgment of the damage result caused by improper splicing, firstly, the current i-th section of leakage magnetic image Y needs to be subjected to i And the i-1 th segment of magnetic leakage image Y i-1 The coincidence data segment is distinguished and selected. According to the i-th section of the overlapped data section, the leakage magnetic image Y i And the i-1 th segment of magnetic leakage image Y i-1 The processing of the coincident data segments is divided into four cases:
FIG. 7 is a diagram illustrating a case one of the coincident data segments of the present invention. As shown in FIG. 7, if the data segment is overlapped, the leakage magnetic image Y is formed in the i-th segment i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 segment i-1 If there is no damage, the superimposed data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (: L-S + 1L), ": denotes all elements in the corresponding dimension, and" α: β "denotes the corresponding dimension from the α -th element to the β -th element.
FIG. 8 is a diagram of a second case of overlapping data segments in the present invention. As shown in FIG. 8, if the data segment is overlapped, the leakage magnetic image Y of the i-th segment i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 th segment i-1 If there is a damage, the superimposed data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (:,L-S+1:L)。
Fig. 9 is a diagram illustrating a third case of overlapping data segments in the present invention. As shown in FIG. 9, if the data segment is overlapped, the leakage magnetic image Y is formed in the i-th segment i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 th segment i-1 If there is no damage, the coincidence data segment is used in the leakage magnetic image Y i Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i (:,1:S)。
Fig. 10 is a diagram illustrating a fourth case of overlapping data segments in the present invention.As shown in FIG. 10, if the data segment is overlapped, the leakage magnetic image Y is formed in the i-th segment i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 th segment i-1 If there is a damage, the first half of the superimposed data segment is used in the leakage flux image Y i-1 The second half of the corresponding leakage image is used in the leakage image Y i Corresponding magnetic leakage image, i.e. processed coincident data segment
Meaning rounding down.
Finally, according to the processing result of the overlapped data segments, the current magnetic leakage image Y is processed i With the existing spliced leaky magnetic image Z i-1 Splicing is carried out, and the existing spliced magnetic leakage image Z is recorded i-1 Is M' x D, a new spliced leakage flux image Z i The expression is as follows:
Z i ＝[Z i-1 (:,1:D-S)P i Y i (:,S+1:L)]
s207: displaying the spliced magnetic flux leakage image in real time:
the obtained spliced magnetic leakage image Z i And outputting and displaying in real time.
S208: and judging whether the processing of the sampling signal is finished, if not, if so, the data acquisition system still has a new sampling signal which is not processed, then the step S209 is executed, otherwise, the processing is finished.
S209: let i = i +1, return to step S203.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (4)
1. A steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling is characterized by comprising the following steps:
s1: arranging a magnetic sensor array which comprises M magnetic sensors, wherein the M magnetic sensors are arranged at equal intervals along the circumferential direction of the steel wire rope, and annular armatures are arranged on the outer sides of the M magnetic sensors;
s2: the steel wire rope generates relative motion in the axial direction of the magnetic sensors, a data acquisition system periodically samples current signals of M magnetic sensors, the length of a sampling signal of each magnetic sensor in each sampling is L, the setting of a sampling period needs to enable the overlapping of two adjacent acquired signals, the overlapping length is S, S is more than 0 and less than L/2, the sampling signal of the ith section of the steel wire rope is X i I =1,2, \ 8230;, sampling signal X i Is a data matrix of size M × L, wherein the nth sampling point data of the mth magnetic sensor is X i [m,n](ii) a After sampling is finished each time, the step S3 is carried out;
s3: sampling signal X for each segment i Respectively carrying out magnetic flux leakage imaging, wherein the specific method comprises the following steps:
two magnetic flux leakage imaging processing methods are set as required, one of the magnetic flux leakage imaging processing methods is low in calculation complexity and is marked as a magnetic flux leakage imaging processing method A, and the other of the magnetic flux leakage imaging processing methods is high in calculation complexity and accuracy and is marked as a magnetic flux leakage imaging processing method B; for each section of sampling signal X i Firstly, the magnetic leakage imaging processing method A is adopted to carry out magnetic leakage imaging processing and carry out damage diagnosis to obtain a magnetic leakage image Y with the size of M' multiplied by L i A M' is a preset magnetic leakage image width parameter, and if the damage diagnosis result shows that no damage exists, the magnetic leakage image Y is subjected to magnetic leakage image detection i A As a sampling signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i A If the diagnosis result shows that the damage exists, the magnetic leakage imaging processing method B is adopted to carry out magnetic leakage imaging processing and carry out damage diagnosis, and the obtained magnetic leakage image Y with the size of M' multiplied by L is obtained i B As sampled signal X i Corresponding leakage magnetic image Y i I.e. Y i ＝Y i B And in the leakage magnetic image Y i Marking out the diagnosed damage area;
s4: judging whether to sample the signal X i If yes, go to step S5, otherwise go to step S6;
s5: make concatenation magnetic leakage image Z 1 ＝Y 1 ；
S6: the current leakage magnetic image Y i With the existing spliced leaky magnetic image Z i-1 Splicing is carried out, and the following four conditions are adopted:
if the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 segment i-1 If there is no damage, the superimposed data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (L-S + 1L), ": denotes all elements in the corresponding dimension," α: β "denotes the corresponding dimension from the α -th element to the β -th element;
if the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that no damage exists and the leakage magnetic image Y is in the i-1 segment i-1 If there is a damage, the coincidence data segment is used in the leakage magnetic image Y i-1 Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i-1 (:,L-S+1:L)；
If the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 segment i-1 If there is no damage, the coincidence data segment is used in the leakage magnetic image Y i Corresponding leakage magnetic image, i.e. processed coincident data segment P i ＝Y i (:,1:S)；
If the overlapped data segment is in the i-th segment of the leakage magnetic image Y i The damage diagnosis result in (1) is that there is damage and the leakage magnetic image Y is in the i-1 th segment i-1 If there is a damage, the first half of the superimposed data segment is used in the leakage flux image Y i-1 Middle corresponding magnetic flux leakage image, latterSemi-using it in leakage magnetic image Y i Corresponding leakage images, i.e. processed coincident data segments
Represents rounding down;
finally, according to the processing result of the overlapped data segments, the current magnetic leakage image Y is processed i With the existing spliced leaky magnetic image Z i-1 Splicing, recording the spliced magnetic leakage image Z i-1 Is M' x D, a new spliced leakage flux image Z i The expression is as follows:
Z i ＝[Z i-1 (:,1:D-S) P i Y i (:,S+1:L)]
s7: the obtained spliced magnetic leakage image Z i Outputting and displaying in real time;
s8: judging whether the processing of the sampling signal is finished, if not, entering the step S9, otherwise, finishing the processing;
s9: let i = i +1, return to step S3.
2. The method for real-time processing of magnetic flux leakage imaging of steel wire rope under continuous sampling according to claim 1, wherein the length of the sampled signal L = λ α, α represents the lay length of steel wire rope, λ =1,2, \ 8230;, in step S2.
3. The method for real-time processing of magnetic flux leakage imaging of steel wire rope under continuous sampling according to claim 1, wherein the overlapping length S in the step S2 satisfies f max ≤S＜L/2，f max Representing the maximum lesion sample length.
4. The method for processing the leakage flux of the steel wire rope under the condition of continuous sampling according to claim 1, wherein the method for processing the leakage flux of the steel wire rope in the step S3 comprises the following specific steps:
1) For the sampling signal X i The sampling signal X corresponding to each magnetic sensor i [m]Trend line data X 'of sampling signals of each magnetic sensor are obtained by a method of moving average' i [m]Sampling the signal X i [m]Subtract trend line data X' i [m]To obtain data DS i [m](ii) a Data DS of M magnetic sensors i [m]Combined into a trend-removed sampling signal DS i ；
2) Determining a sampling signal DS i Then sample the signal DS i Searching a resampling line with the highest coincidence degree with the strand corrugation line and the oblique filtering span of K;
3) For the sampled signals DS in the circumferential direction i In each group of M leakage signals DS i [n]Interpolating to obtain a signal DI of length M i [n]Reconstructing to obtain a signal DI with a size of M' × L i In the presence of signal DI i Respectively connecting a zero matrix with the size of M 'x M' in front and back to obtain a signal DR i (ii) a At signal DR i The data is extracted along the corresponding re-sampling line, and the re-sampling line traverses all the data for filtering to remove the signal DR i Two matrices of M 'x M' one after the other, i.e. extracting the signal DR i The data of the middle L columns obtain a signal DF with the size of M' multiplied by L i ；
4) For signal DF i In each path of data DF i [m′]Respectively calculating the amplitude of the envelope estimation, and storing the obtained data in a matrix DE with the size of M' multiplied by L i The preparation method comprises the following steps of (1) performing;
5) Presetting a defect judgment threshold th, defining a matrix DB of size M' × L i Traversal matrix DE i If the value of each element in the matrix is greater than or equal to the threshold th, the element is determined to belong to a defect, and the matrix DB is formed i The middle corresponding element is set to 1, otherwise, the middle corresponding element is set to 0;
6) General matrix DE i And matrix DB i Obtaining the matrix DO by doing Hadamard product i ；
7) For the matrix DO i Imaging to obtain leakage magnetic image Y i A Wherein the matrix DO i The maximum value of the element in (1) corresponds to the maximum value of the gray scale, and the value is 0 pairThe minimum value of the corresponding gray scale;
the magnetic leakage imaging processing method B specifically comprises the following steps:
1) For the sampling signal X i Obtaining trend line data X 'of sampling signals of each magnetic sensor by respectively using a moving average method for the sampling signals corresponding to each magnetic sensor' i [m]Sampling the signal X i [m]Subtract trend line data X' i [m]To obtain data DS i [m](ii) a Data DS of M magnetic sensors i [m]Combined into a trend-removed sampling signal DS i ；
2) Determining a sampling signal DS i Then sample the signal DS i Searching a resampling line with the highest coincidence degree with the strand corrugation line and the oblique filtering span of K;
3) Sampling signal DS in the circumferential direction i In each group of M leakage signals DS i [n]Interpolating to obtain a signal DI of length M i [n]Reconstructing to obtain signal DI with size of M' x L i In the presence of signal DI i The front and the rear are respectively connected with a zero matrix with the size of M 'multiplied by M' to obtain a signal DR i (ii) a At signal DR i The data is extracted along the corresponding re-sampling line, and the re-sampling line traverses all the data for filtering to remove the signal DR i Two matrices of M 'x M' one after the other, i.e. extracting the signal DR i The data of the middle L columns obtain a signal DF with the size of M' multiplied by L i ；
4) For signal DF i In each path of data DF i [m′]Respectively calculating the amplitude of the envelope estimation, and storing the obtained data in a matrix DE with the size of M' x L i The preparation method comprises the following steps of (1) performing;
5) In the matrix DE i Respectively connecting a zero matrix with the size of M 'x M' to obtain a matrix DP i To the matrix DP i Carrying out median filtering on signals corresponding to each magnetic sensor to obtain a matrix DP' i (ii) a The median filtering can be adopted to remove the influence of the wire rope shaking; then removing the matrix DP i Two matrices of M 'x M' one after the other, i.e. the extracted matrix DP i The data of the middle L column is obtained to obtain the moment with the size of M' multiplied by LArray DD i ；
6) Presetting a defect judgment threshold th, defining a matrix DB with a size of M' × L i Traversal matrix DE i If the value of each element in the matrix is greater than or equal to the threshold th, the element is determined to belong to a defect, and the matrix DB is formed i The middle corresponding element is set to 1, otherwise, the middle corresponding element is set to 0;
7) Will matrix DE i And matrix DB i Obtaining the matrix DO by doing Hadamard product i ；
8) For matrix DO i Imaging to obtain leakage magnetic image Y i A Wherein the matrix DO i The maximum value of the element(s) in (1) corresponds to the maximum value of the gray scale, and the value 0 corresponds to the minimum value of the gray scale.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910796522.XA CN110596233B (en) | 2019-08-27 | 2019-08-27 | Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910796522.XA CN110596233B (en) | 2019-08-27 | 2019-08-27 | Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110596233A CN110596233A (en) | 2019-12-20 |
| CN110596233B true CN110596233B (en) | 2022-11-22 |
Family
ID=68855820
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910796522.XA Active CN110596233B (en) | 2019-08-27 | 2019-08-27 | Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110596233B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112833761B (en) * | 2021-01-08 | 2021-12-10 | 电子科技大学 | Method for estimating detection speed and displacement of steel wire rope based on magnetic flux leakage signal |
| CN112902821B (en) * | 2021-01-08 | 2021-12-10 | 电子科技大学 | Method for measuring lay length on line and evaluating health state of steel wire rope according to lay length |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS59217159A (en) * | 1983-05-26 | 1984-12-07 | Mitsubishi Electric Corp | Defect detectng apparatus |
| JPH10197488A (en) * | 1996-12-30 | 1998-07-31 | Tokyo Gas Co Ltd | Detection method of weld line position of pipe using leakage flux pig |
| JP2004093209A (en) * | 2002-08-29 | 2004-03-25 | Hamamatsu Photonics Kk | Nondestructive inspection system |
| CN1928543A (en) * | 2006-09-15 | 2007-03-14 | 哈尔滨工业大学深圳研究生院 | Hall sensor array based steel ropes nondestructive examination method and device |
| CN101482540A (en) * | 2009-01-19 | 2009-07-15 | 哈尔滨工业大学深圳研究生院 | Steel wire rope damage detection apparatus and method based on electromagnetic chromatography imaging technique |
| WO2009138479A1 (en) * | 2008-05-14 | 2009-11-19 | Bühler AG | System and method for ground material characterization in a grinding system |
| US8949042B1 (en) * | 2012-10-27 | 2015-02-03 | Valerian Goroshevskiy | AUV pipeline inspection using magnetic tomography |
| CN104458895A (en) * | 2014-12-08 | 2015-03-25 | 清华大学 | Three-dimensional pipeline leakage flux imaging detection method and system |
| CN104990977A (en) * | 2015-06-29 | 2015-10-21 | 清华大学 | Three-dimensional magnetic flux leakage inspection and defect compound inversion imaging method |
| CN105116046A (en) * | 2015-09-09 | 2015-12-02 | 大同煤矿集团有限责任公司 | Coal mine steel wire rope real-time online detection device |
| JP2018096977A (en) * | 2016-12-15 | 2018-06-21 | ティッセンクルップ ラッセルシュタイン ゲー エム ベー ハー | Method of inspecting steel strips |
| CN108776171A (en) * | 2018-09-12 | 2018-11-09 | 中国计量大学 | Steel wire rope nondestructive inspection sensing device based on multiloop excitation and image analysis |
| CN108918654A (en) * | 2018-07-17 | 2018-11-30 | 成都众柴科技有限公司 | A kind of processing of wirerope radial direction magnetic leakage signal and imaging method |
| CN109186842A (en) * | 2018-10-17 | 2019-01-11 | 成都众柴科技有限公司 | A kind of cable method for measuring stress based on magnetoelasticity |
| JP2019078640A (en) * | 2017-10-25 | 2019-05-23 | Ｊｆｅスチール株式会社 | Surface defect detection method and surface defect detector |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9671371B2 (en) * | 2014-06-27 | 2017-06-06 | Schlumberger Technology Corporation | Anomaly recognition system and methodology |
-
2019
- 2019-08-27 CN CN201910796522.XA patent/CN110596233B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS59217159A (en) * | 1983-05-26 | 1984-12-07 | Mitsubishi Electric Corp | Defect detectng apparatus |
| JPH10197488A (en) * | 1996-12-30 | 1998-07-31 | Tokyo Gas Co Ltd | Detection method of weld line position of pipe using leakage flux pig |
| JP2004093209A (en) * | 2002-08-29 | 2004-03-25 | Hamamatsu Photonics Kk | Nondestructive inspection system |
| CN1928543A (en) * | 2006-09-15 | 2007-03-14 | 哈尔滨工业大学深圳研究生院 | Hall sensor array based steel ropes nondestructive examination method and device |
| WO2009138479A1 (en) * | 2008-05-14 | 2009-11-19 | Bühler AG | System and method for ground material characterization in a grinding system |
| CN101482540A (en) * | 2009-01-19 | 2009-07-15 | 哈尔滨工业大学深圳研究生院 | Steel wire rope damage detection apparatus and method based on electromagnetic chromatography imaging technique |
| US8949042B1 (en) * | 2012-10-27 | 2015-02-03 | Valerian Goroshevskiy | AUV pipeline inspection using magnetic tomography |
| CN104458895A (en) * | 2014-12-08 | 2015-03-25 | 清华大学 | Three-dimensional pipeline leakage flux imaging detection method and system |
| CN104990977A (en) * | 2015-06-29 | 2015-10-21 | 清华大学 | Three-dimensional magnetic flux leakage inspection and defect compound inversion imaging method |
| CN105116046A (en) * | 2015-09-09 | 2015-12-02 | 大同煤矿集团有限责任公司 | Coal mine steel wire rope real-time online detection device |
| JP2018096977A (en) * | 2016-12-15 | 2018-06-21 | ティッセンクルップ ラッセルシュタイン ゲー エム ベー ハー | Method of inspecting steel strips |
| JP2019078640A (en) * | 2017-10-25 | 2019-05-23 | Ｊｆｅスチール株式会社 | Surface defect detection method and surface defect detector |
| CN108918654A (en) * | 2018-07-17 | 2018-11-30 | 成都众柴科技有限公司 | A kind of processing of wirerope radial direction magnetic leakage signal and imaging method |
| CN108776171A (en) * | 2018-09-12 | 2018-11-09 | 中国计量大学 | Steel wire rope nondestructive inspection sensing device based on multiloop excitation and image analysis |
| CN109186842A (en) * | 2018-10-17 | 2019-01-11 | 成都众柴科技有限公司 | A kind of cable method for measuring stress based on magnetoelasticity |
Non-Patent Citations (7)
| Title |
|---|
| Development of a high sensitive magnetic flux leakage instrument for imaging of localised flaws in small diameter ferromagnetic steel tubes;Waikhom Sharatchandra Singh等;《IET Science, Measurement & Technology》;20180801;第12卷(第8期);第932-936页 * |
| Evaluation of Composite Wire Ropes Using Unsaturated Magnetic Excitation and Reconstruction Image with Super-Resolution;Xiaojiang Tan等;《Applied Science》;20180511;第8卷;第1-17页 * |
| Fault diagnosis for planetary gearboxes using multi-criterion fusion feature selection framework;Zhiliang Liu等;《Proc IMechE Part C: J Mechanical Engineering Science》;20130930;第227卷(第9期);第2064-2076页 * |
| Flaw Detection in Welded Metal Using Magnetic Induction Tomography;Dona Sutisna等;《Advanced Materials Research》;20140219;第896卷;第722-725页 * |
| 基于霍尔阵列传感器的钢丝绳缺陷定量检测技术的研究;赵敏 等;《无损检测》;20121110;第34卷(第11期);第57-60页 * |
| 漏磁内检测数据中的管壁缺陷特征提取方法;刘金海等;《北京工业大学学报》;20140731;第40卷(第7期);第1041-1047页 * |
| 缺陷漏磁成像技术综述;黄松岭等;《电工技术学报》;20161031;第31卷(第20期);第55-63页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110596233A (en) | 2019-12-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4675949B2 (en) | Method and apparatus for measuring crack width of structures and products using image processing technique | |
| CN110596233B (en) | Steel wire rope magnetic flux leakage imaging real-time processing method under continuous sampling | |
| CN102680480A (en) | Intelligent detecting method for cracks of concrete structures | |
| CN104535356A (en) | Method and system for monitoring rope arrangement faults of drum steel wire rope on basis of machine vision | |
| CN116563273B (en) | Detection and early warning method and system for steel defects | |
| EP3951381A1 (en) | Non-destructive testing method and device for testing and distinguishing internal and external defects of steel wire rope | |
| JP2020128877A (en) | Linear object abnormality detection device and abnormality detection method | |
| CN108918654B (en) | Method for processing and imaging radial magnetic flux leakage signal of steel wire rope | |
| CN111178392A (en) | Aero-engine hole-exploring image damage segmentation method based on deep neural network | |
| CN116416497A (en) | Bearing fault diagnosis system and method | |
| CN110702786A (en) | Beam structure damage identification method based on multi-scale singular attractor prediction error | |
| CN114897855A (en) | Method for judging defect type based on X-ray picture gray value distribution | |
| CN107392216B (en) | Method for quickly identifying circumferential seams of shield tunnel segments based on gray data | |
| CN114689298A (en) | Fault detection method and device for walking part of coal mining machine | |
| CN115272336A (en) | Metal part defect accurate detection method based on gradient vector | |
| CN109767426B (en) | Shield tunnel water leakage detection method based on image feature recognition | |
| CN112330633B (en) | Jumper wire adhesive tape damage fault image segmentation method based on self-adaptive band-pass filtering | |
| CN117522854A (en) | Steel wire rope defect identification method based on magnetic flux leakage image target detection | |
| JP3437074B2 (en) | Apparatus and method for detecting position of broken reinforcing bar in concrete pole | |
| CN113436216A (en) | Electrical equipment infrared image edge detection method based on Canny operator | |
| JP4910412B2 (en) | Appearance inspection method | |
| CN116558476A (en) | High pier large span continuous rigid frame bridge pier settlement displacement monitoring method and system | |
| CN115690098A (en) | Method for detecting breakage and loss of iron wire | |
| CN115439667A (en) | Transformer mechanical fault diagnosis method and system based on sound field distribution diagram | |
| CN115424075A (en) | Method and system for monitoring pipeline state |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |