Method and device for identifying broken wire deformation defects of steel wire rope by magnetic flux leakage signal

Abstract

The application provides a kind of steel wire rope broken wire deformation defect's magnetic flux leakage signal identification method and device, belong to magnetic flux leakage detection field, this method includes: according to the original signal of steel wire magnetic flux leakage detection, determine the signal to be decomposed;The mode decomposition is carried out to the signal to be decomposed by CEEMDAN technology, obtain multiple target IMF signals;From multiple target IMF signals, extract IMF4 and IMF5 modal component, and calculate the similarity of two, according to the size relationship of the similarity and preset threshold value determine the signal class of original signal;Wherein, the signal class includes deformation signal and broken wire signal.This method can effectively suppress the influence of noise, thereby improving the accuracy of signal decomposition, and adapting to different types and intensity of noise, reduces the false positive rate and the false negative rate, has strong robustness, and then can effectively reduce the safety risk caused by steel wire rope defect, safeguards the safe operation of equipment.

Description

Technical Field

[0001] This invention relates to the field of magnetic flux leakage detection, and in particular to a method and apparatus for identifying magnetic flux leakage signals in steel wire rope with broken wire deformation defects. Background Technology

[0002] Wire ropes are widely used in structures such as cranes, cableways, and bridges, serving as crucial components for bearing and supporting heavy loads. Due to their long-term exposure to heavy loads and harsh environments, wire ropes are prone to various defects, such as broken wires, deformation, and corrosion. These defects can severely affect the load-bearing capacity of the wire rope, even leading to structural failure and potentially causing major safety accidents. Traditional wire rope inspection methods include visual inspection, ultrasonic testing, and magnetic particle testing, but these methods have certain limitations. For example, visual inspection struggles to detect internal defects, ultrasonic testing is sensitive to surface roughness, and magnetic particle testing requires specific conditions, making it complex and difficult to adapt to on-site environments.

[0003] Magnetic flux leakage (MFL) testing, as a non-destructive testing method, has been widely used in the defect detection of wire ropes. This technology determines the health status of the wire rope by measuring the MFL signal generated in a magnetic field. However, various noises often exist in the testing environment, interfering with the accuracy of the MFL signal and leading to unclear defect identification. Current MFL signal processing methods, such as Fourier transform and wavelet transform, struggle to effectively separate noise and signal characteristics when dealing with complex signals, thus failing to fully meet the demands of high-precision testing. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a method and device for identifying magnetic leakage signals of wire rope broken wire and deformation defects, so as to improve the detection accuracy of wire rope broken wire and deformation defects.

[0005] This invention provides a method for identifying magnetic flux leakage signals of wire rope deformation defects caused by broken wires, comprising: determining a signal to be decomposed based on the original signal detected by the wire flux leakage detection; performing mode decomposition on the signal to be decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technology to obtain multiple target Intrinsic Mode Function (IMF) signals; extracting IMF4 and IMF5 mode components from the multiple target IMF signals and calculating their similarity; and determining the signal category of the original signal based on the relationship between the similarity and a preset threshold; wherein the signal category includes deformation signals and broken wire signals.

[0006] The present invention provides a method for identifying leakage magnetic flux signals of wire rope breakage and deformation defects. This method utilizes CEEMDAN technology to perform mode decomposition on the signal to be decomposed, obtaining multiple target IMF signals. The method includes: adding Gaussian white noise of different orders to the signal to be decomposed to obtain multiple sets of new signals; performing Empirical Mode Decomposition (EMD) on each set of new signals to generate the first IMF component of each set; averaging the first IMF component of each set to obtain the target IMF signal; calculating the residual signal from the target IMF signal; repeating the process of adding Gaussian white noise of different orders to the residual signal to obtain multiple sets of new signals, performing EMD on each set of new signals to generate the first IMF component of each set, averaging the first IMF component of each set to obtain the target IMF signal, and calculating the residual signal from the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained; finally, all target IMF signals are obtained, constituting the multiple target IMF signals.

[0007] According to the present invention, a method for identifying magnetic flux leakage signals of wire rope breakage and deformation defects is provided. The signal to be decomposed is a signal acquired multiple times. Accordingly, calculating the similarity between the two signals includes:

[0008] Calculate the similarity coefficient

[0009] in,

[0010] Where r' is the similarity coefficient, i is the number of times the signal was acquired, and IMF4 i and IMF5 i , are the values ​​of IMF4 and IMF5 obtained in the i-th acquisition of the signal, respectively. IMF4 and IMF5 are the average values ​​of IMF4 and IMF5, respectively.

[0011] According to the present invention, a method for identifying magnetic leakage signals of wire rope breakage and deformation defects includes determining the signal category of the original signal based on the relationship between the similarity and a preset threshold, comprising:

[0012] When the similarity coefficient r' > 0.2, it is a deformation signal; when the similarity coefficient r' < 0.2, it is a broken wire signal.

[0013] According to the present invention, a method for identifying magnetic flux leakage signals of wire rope breakage and deformation defects is provided, wherein adding Gaussian white noise to the signal to be decomposed yields a new signal including:

[0014] y(t)+(-1) q εν j (t);

[0015] Correspondingly, in the process of repeatedly adding Gaussian white noise to the residual signal to obtain multiple sets of new signals, the amount of white noise added is also (-1). q εν j (t);

[0016] Where y(t) is the signal to be decomposed, q = 1, 2, ν j To satisfy the standard normal distribution of Gaussian white noise signal, j = 1, 2, ..., N, where j is the number of times white noise is added, and ε is the amplitude coefficient of white noise, used to adjust the noise intensity.

[0017] According to the present invention, a method for identifying magnetic flux leakage signals of wire rope breakage and deformation defects includes determining the signal to be decomposed based on the original signal of the wire flux leakage detection, which includes preprocessing the original signal of the wire flux leakage detection to obtain the signal to be decomposed; wherein the preprocessing includes removing DC bias and low-frequency noise.

[0018] The present invention also provides a magnetic flux leakage signal identification device for wire rope broken wire deformation defects, comprising: a preprocessing module for determining the signal to be decomposed based on the original signal detected by wire flux leakage; a signal decomposition module for performing mode decomposition on the signal to be decomposed using CEEMDAN technology to obtain multiple target IMF signals; and a signal discrimination module for extracting IMF4 and IMF5 mode components from the multiple target IMF signals, calculating their similarity, and determining the signal category of the original signal based on the relationship between the similarity and a preset threshold; wherein the signal category includes deformation signals and broken wire signals.

[0019] According to the present invention, a magnetic flux leakage signal identification device for wire rope breakage and deformation defects is provided. The signal decomposition module is specifically used for: adding Gaussian white noise of different orders to the signal to be decomposed to obtain multiple sets of new signals; performing Empirical Mode Decomposition (EMD) processing on each set of new signals to generate the first IMF component of each set; averaging the first IMF component of each set to obtain a target IMF signal; calculating the residual signal of the target IMF signal; repeating the process of adding Gaussian white noise of different orders to the residual signal to obtain multiple sets of new signals, performing EMD processing on each set of new signals to generate the first IMF component of each set, averaging the first IMF component of each set to obtain the target IMF signal, and calculating the residual signal of the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained; the final obtained target IMF signals are the multiple target IMF signals.

[0020] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the leakage magnetic signal identification method for wire rope broken wire deformation defects as described above.

[0021] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying leakage magnetic signals of wire rope broken wire deformation defects as described above.

[0022] The present invention provides a method and apparatus for identifying leakage magnetic signals of wire rope broken wire deformation defects. By using CEEMDAN technology to perform modal decomposition on the signal to be decomposed, the influence of noise can be effectively suppressed, thereby improving the accuracy of signal decomposition and adapting to different types and intensities of noise. Then, IMF4 and IMF5 modal components are extracted from multiple target IMF signals to determine the signal category of the original signal, further reducing the false alarm rate and false alarm rate. It has strong robustness and can effectively reduce the safety risks caused by wire rope defects, ensuring the safe operation of equipment. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the method for identifying magnetic leakage signals of wire rope breakage and deformation defects provided by the present invention.

[0025] Figure 2 This is one of the schematic diagrams of the original signals for online magnetic flux leakage detection of typical steel wire rope defects provided by the present invention;

[0026] Figure 3 This is the second schematic diagram of the original signal for online magnetic flux leakage detection of typical steel wire rope defects provided by the present invention;

[0027] Figure 4 This is a schematic diagram of the CEEMDAN algorithm provided by this invention processing the original signal of the second impact;

[0028] Figure 5 This is a schematic diagram of the CEEMDAN algorithm provided by this invention processing the original signal of the third impact;

[0029] Figure 6 This is a graph showing the variation of the wire rope similarity index provided by the present invention;

[0030] Figure 7 This is a schematic diagram of the magnetic leakage signal identification device for wire rope broken wire deformation defects provided by the present invention.

[0031] Figure 8This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0033] The following is combined Figures 1-8 The present invention describes a method and apparatus for identifying magnetic leakage signals of wire rope breakage and deformation defects. Figure 1 This is a flowchart illustrating the method for identifying magnetic leakage signals of wire rope breakage and deformation defects provided by the present invention. Figure 1 As shown, the present invention provides a method for identifying magnetic leakage signals of wire rope breakage and deformation defects, comprising:

[0034] 101. Based on the original signal from the wire leakage magnetic field detection, determine the signal to be decomposed.

[0035] In some embodiments, determining the signal to be decomposed based on the original signal of the wire leakage magnetic flux detection includes: preprocessing the original signal of the wire leakage magnetic flux detection to obtain the signal to be decomposed; wherein the preprocessing includes removing DC bias and low-frequency noise.

[0036] Original signal such as Figure 2 and Figure 3 As shown, the raw signal from the magnetic flux leakage detection is first preprocessed. This step mainly involves removing DC bias and low-frequency noise to ensure baseline stability. This processing helps improve the accuracy of subsequent decomposition. The preprocessed data can lay the foundation for the application of CEEMDAN technology, ensuring more reliable decomposition results.

[0037] 102. The signal to be decomposed is subjected to mode decomposition using CEEMDAN technology to obtain multiple target IMF signals.

[0038] CEEMDAN is an improved EMD method. Traditional EMD methods decompose a signal into multiple IMFs and a residual term, but the decomposition results are easily affected by noise, leading to instability. To overcome this problem, the concept of an adaptive noise set is introduced. By superimposing noise with adjusted amplitude into the signal and performing multiple decomposition processes, the influence of noise is effectively suppressed, resulting in a more stable decomposition result.

[0039] After signal preprocessing, this invention applies CEEMDAN technology to perform multi-level decomposition of the signal. Specifically, firstly, adaptive noise is superimposed on the original signal. This noise has similar statistical characteristics to the original signal, but its amplitude can be adjusted according to actual needs. Next, the signal with superimposed noise is subjected to EMD processing to generate a series of IMF signals and residual terms. Through multiple noise superposition and decomposition processes, CEEMDAN technology can obtain a more accurate and stable set of IMFs. Compared with traditional EMD methods, this technology performs better when processing nonlinear and non-stationary signals.

[0040] 103. Extract IMF4 and IMF5 mode components from multiple IMF signals, calculate their similarity, and determine the signal category of the original signal based on the relationship between the similarity and a preset threshold; wherein the signal category includes deformed signals and broken wire signals.

[0041] The decomposed IMFs contain different features of the signal, with IMF4 and IMF5 typically containing important information related to defects in the wire leakage flux signal. By analyzing these key modal components, anomalous features can be extracted. Specifically, by calculating the similarity between IMF4 and IMF5, normal signals and signals containing defects can be further distinguished. Similarity calculations can be based on methods such as correlation coefficients and mutual information to assess the degree of matching between modal components.

[0042] Based on the similarity calculation results, an appropriate threshold needs to be set to identify abnormal signals. When the similarity between IMF4 and IMF5 is lower than the set threshold, it indicates that there may be a defect in the signal. This method makes defect identification more accurate and efficient, and can accurately detect problems such as broken wires and deformation in wire ropes.

[0043] The present invention provides a method for identifying magnetic flux leakage signals of wire rope broken wire deformation defects. By using CEEMDAN technology to perform modal decomposition on the signal to be decomposed, the method can effectively suppress the influence of noise, thereby improving the accuracy of signal decomposition and adapting to different types and intensities of noise. Furthermore, it extracts IMF4 and IMF5 modal components from multiple target IMF signals to determine the signal category of the original signal, further reducing the false alarm rate and false alarm rate. This method has strong robustness and can effectively reduce the safety risks caused by wire rope defects, ensuring the safe operation of equipment.

[0044] In some embodiments, mode decomposition of the signal to be decomposed is performed using CEEMDAN technology to obtain multiple target IMF signals. This includes: adding Gaussian white noise of different orders to the signal to be decomposed to obtain multiple sets of new signals; performing Empirical Mode Decomposition (EMD) processing on each set of new signals to generate the first IMF component of each set; averaging the first IMF component of each set to obtain the target IMF signal; calculating the residual signal of the target IMF signal; repeating the process of adding Gaussian white noise of different orders to the residual signal to obtain multiple sets of new signals, performing EMD processing on each set of new signals to generate the first IMF component of each set, averaging the first IMF component of each set to obtain the target IMF signal, and calculating the residual signal of the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained; all the final target IMF signals are the multiple target IMF signals. In some embodiments, the new signal obtained by adding Gaussian white noise to the signal to be decomposed includes: y(t)+(-1) q εν j (t); Correspondingly, in the process of repeatedly adding Gaussian white noise to the residual signal at different frequencies to obtain multiple sets of new signals, the added white noise is also (-1). q εν j (t); where y(t) is the signal to be decomposed, q = 1, 2, ν j To satisfy the standard normal distribution of Gaussian white noise signal, j = 1, 2, ..., N, where j is the number of times white noise is added, and ε is the coefficient of white noise.

[0045] Specifically, let E i (g) represents the i-th intrinsic mode component obtained after EMD decomposition, and the i-th intrinsic mode component obtained after CEEMDAN decomposition is C. i (t), ν j To satisfy a standard normally distributed Gaussian white noise signal, j = 1, 2, ..., N, where j represents the number of times white noise is added, ε is the amplitude coefficient of the white noise used to adjust the noise intensity, and y(t) is the signal to be decomposed. The CEEMDAN decomposition steps of this invention are as follows:

[0046] Different orders of Gaussian white noise are added to the signal to be decomposed to obtain N new signals. Depending on the different values ​​of j (j = 1, 2, ..., N), they are y(t) + (-1) respectively. q εν j (t), perform EMD decomposition on the new signal to obtain the first IMF component of each group, denoted as . The first IMF component of all new signals is averaged. This yields the first modal component of the CEEMDAN decomposition (i.e., the first target IMF signal):

[0047]

[0048] Calculate the residual signal of the target IMF signal:

[0049]

[0050] Gaussian noise is added to the residual signal multiple times, and the signals are treated as multiple new signals. The EMD process is repeated, and the first IMF component of each group is obtained, denoted as... This leads to the average of the second target IMF signal:

[0051]

[0052] Calculate the residual corresponding to the second target IMF signal:

[0053]

[0054] Repeat the above steps until a target IMF signal containing IMF4 and IMF5 components is obtained, at which point the algorithm terminates. The number of target IMF signal components obtained at this point is K, and the original signal y(t) is decomposed into:

[0055]

[0056] In some embodiments, the signal to be decomposed is a signal acquired multiple times; accordingly, calculating the similarity between the two signals includes:

[0057] Calculate the similarity coefficient:

[0058]

[0059] in,

[0060] Where r' is the similarity coefficient, i is the number of times the signal was acquired, and IMF4 i and IMF5 i These are the values ​​obtained by IMF4 and IMF5 in the i-th signal acquisition, respectively. and These are the average values ​​of all occurrences of IMF4 and IMF5, respectively.

[0061] In some embodiments, determining the signal category of the original signal based on the relationship between the similarity and the preset threshold includes:

[0062] When the similarity coefficient r' > 0.2, it is a deformation signal; when the similarity coefficient r' < 0.2, it is a broken wire signal.

[0063] This application is illustrated by experiments, in which multiple impact experiments were conducted, and a signal was collected after each impact. Figure 4 , Figure 5 This diagram illustrates the processing of the original signals from the second and third impacts using the CEEMDAN algorithm. It shows the results obtained by decomposing the signals using the CEEMDAN algorithm. The diagram contains multiple sub-graphs, from top to bottom: the original signal and the eight intrinsic mode functions (IMF1 to IMF8). Figure 6 This is a similarity coefficient graph for two different wire ropes (No. 1 and No. 2) after multiple impacts. The horizontal axis represents the number of data collections, and the vertical axis represents the similarity coefficient. After the third impact, the similarity coefficient r' of wire rope No. 1 is less than 0.2, and the damage signal begins to change from deformation to wire breakage. After the second impact, the similarity coefficient r' of wire rope No. 2 is less than 0.2, and the damage signal also begins to change from deformation to wire breakage.

[0064] The following describes the magnetic flux leakage signal identification device for wire rope broken wire deformation defects provided by the present invention. The magnetic flux leakage signal identification device for wire rope broken wire deformation defects described below can be referred to in correspondence with the magnetic flux leakage signal identification method for wire rope broken wire deformation defects described above.

[0065] Figure 7 This is a schematic diagram of the magnetic flux leakage signal identification device for wire rope broken wire deformation defects provided by the present invention, as shown below. Figure 7 As shown, the magnetic flux leakage signal identification device for wire rope broken wire deformation defects includes: a preprocessing module 701, a signal decomposition module 702, and a signal discrimination module 703. The preprocessing module 701 determines the signal to be decomposed based on the original signal detected by the wire flux leakage detection. The signal decomposition module 702 performs mode decomposition on the signal to be decomposed using CEEMDAN technology to obtain multiple target IMF signals. The signal discrimination module 703 extracts IMF4 and IMF5 mode components from the multiple target IMF signals, calculates their similarity, and determines the signal category of the original signal based on the relationship between the similarity and a preset threshold. The signal categories include deformation signals and broken wire signals.

[0066] In some embodiments, the signal decomposition module 702 is specifically used for: adding Gaussian white noise of different orders to the signal to be decomposed to obtain multiple sets of new signals; performing Empirical Mode Decomposition (EMD) processing on each set of new signals to generate the first IMF component of each set; averaging the first IMF component of each set to obtain the target IMF signal, and calculating the residual signal of the target IMF signal; repeating the process of adding Gaussian white noise of different orders to the residual signal to obtain multiple sets of new signals, performing EMD processing on each set of new signals to generate the first IMF component of each set, averaging the first IMF component of each set to obtain the target IMF signal, and calculating the residual signal of the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained; finally, all target IMF signals are obtained, which are the multiple target IMF signals.

[0067] In some embodiments, adding Gaussian white noise to the signal to be decomposed results in a new signal including:

[0068] y(t)+(-1) q εν j (t);

[0069] Correspondingly, in the process of repeatedly adding Gaussian white noise to the residual signal to obtain multiple sets of new signals, the amount of white noise added is also (-1). q εν j (t);

[0070] Where y(t) is the signal to be decomposed, q = 1, 2, ν j To satisfy the standard normal distribution of Gaussian white noise signal, j = 1, 2, ..., N, where j is the number of times white noise is added, and ε is the coefficient of white noise.

[0071] The apparatus embodiments provided in this invention are for implementing the above-described method embodiments. For specific processes and details, please refer to the above-described method embodiments, which will not be repeated here.

[0072] The magnetic flux leakage signal identification device for wire rope broken wire deformation defects provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned embodiment of the magnetic flux leakage signal identification method for wire rope broken wire deformation defects. For the sake of brevity, any parts not mentioned in the embodiment of the magnetic flux leakage signal identification device for wire rope broken wire deformation defects can be referred to the corresponding content in the aforementioned embodiment of the magnetic flux leakage signal identification method for wire rope broken wire deformation defects.

[0073] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 8As shown, the electronic device may include: a processor 801, a communication interface 802, a memory 803, and a communication bus 804, wherein the processor 801, the communication interface 802, and the memory 803 communicate with each other through the communication bus 804. The processor 801 can call logical instructions in the memory 803 to execute a method for identifying magnetic flux leakage signals of wire rope breakage and deformation defects. This method includes: determining the signal to be decomposed based on the original signal detected by the wire flux leakage detection; performing mode decomposition on the signal to be decomposed using the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) technique to obtain multiple target IMF signals; extracting IMF4 and IMF5 mode components from the multiple target IMF signals and calculating their similarity; and determining the signal category of the original signal based on the relationship between the similarity and a preset threshold; wherein the signal category includes deformation signals and wire breakage signals.

[0074] Furthermore, the logical instructions in the aforementioned memory 803 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for identifying magnetic flux leakage signals of wire rope deformation defects provided by the methods described above. The method includes: determining a signal to be decomposed based on the original signal detected by wire flux leakage; performing mode decomposition on the signal to be decomposed using the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) technique to obtain multiple target IMF signals; extracting IMF4 and IMF5 mode components from the multiple target IMF signals and calculating their similarity; and determining the signal category of the original signal based on the relationship between the similarity and a preset threshold. The signal category includes deformation signals and wire breakage signals.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying a broken wire deformation defect of a steel wire rope by a magnetic flux leakage signal, characterized by, include: Based on the original signal from the wire magnetic flux leakage detection, the signal to be decomposed is determined; The signal to be decomposed is modally decomposed using the fully adaptive noise set empirical mode decomposition (CEEMDAN) technique to obtain multiple target intrinsic mode function (IMF) signals. IMF4 and IMF5 mode components are extracted from multiple target IMF signals, and their similarity is calculated. The signal category of the original signal is determined based on the relationship between the similarity and a preset threshold. The signal categories include deformation signals and broken wire signals; The original signal is a signal acquired multiple times; correspondingly, calculating the similarity between the two includes: Calculate the similarity coefficient: ； in, ; in, Here, is the similarity coefficient, and i is the number of times the signal was sampled. and These are the values ​​obtained by IMF4 and IMF5 in the i-th signal acquisition, respectively. and These are the average values ​​of all IMF4 and IMF5 trials, respectively. The step of determining the signal category of the original signal based on the relationship between the similarity and the preset threshold includes: When similarity coefficient When the similarity coefficient is greater than 0.2, it is a deformation signal; when the similarity coefficient is less than 0.2, it is a broken wire signal.

2. The method for identifying magnetic leakage signals of wire rope breakage and deformation defects according to claim 1, characterized in that, The signal to be decomposed is subjected to mode decomposition using CEEMDAN technology to obtain multiple target IMF signals, including: Different orders of Gaussian white noise are added to the signal to be decomposed to obtain multiple sets of new signals; Empirical Mode Decomposition (EMD) is performed on each new signal group to generate the first IMF component of each group; The target IMF signal is obtained by averaging the first IMF component of each group, and the residual signal is calculated from the target IMF signal. The process involves repeatedly adding Gaussian white noise to the residual signal at different times to obtain multiple new signals, performing EMD processing on each new signal to generate the first IMF component of each group, averaging the first IMF component of each group to obtain the target IMF signal, and calculating the residual signal from the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained. The final result of all target IMF signals is the plurality of target IMF signals.

3. The method for identifying magnetic leakage signals of wire rope breakage and deformation defects according to claim 2, characterized in that, The addition of Gaussian white noise to the signal to be decomposed results in the following new signals: ； Correspondingly, in the process of repeatedly adding Gaussian white noise to the residual signal at different frequencies to obtain multiple sets of new signals, the amount of white noise added is also... ; Where y(t) is the signal to be decomposed, and q = 1, 2, To satisfy the standard normal distribution of Gaussian white noise signal, the mean is 0 and the standard deviation is 1. The number of times white noise is added. This is the amplitude coefficient of white noise, used to adjust the noise intensity.

4. The method for identifying magnetic leakage signals of wire rope breakage and deformation defects according to claim 1, characterized in that, The process of determining the signal to be decomposed based on the original signal detected by the magnetic flux leakage of the steel wire includes: The original signal from the magnetic flux leakage detection of the steel wire is preprocessed to obtain the signal to be decomposed; The preprocessing includes removing DC bias and low-frequency noise.

5. A magnetic flux leakage signal identification device for wire rope breakage and deformation defects, characterized in that, include: The preprocessing module is used to determine the signal to be decomposed based on the raw signal from the wire magnetic flux leakage detection. The signal decomposition module is used to perform mode decomposition on the signal to be decomposed using CEEMDAN technology to obtain multiple target IMF signals. The signal discrimination module is used to extract IMF4 and IMF5 mode components from multiple target IMF signals, calculate their similarity, and determine the signal category of the original signal based on the relationship between the similarity and a preset threshold. The signal categories include deformation signals and broken wire signals; The original signal is a signal acquired multiple times; correspondingly, calculating the similarity between the two includes: Calculate the similarity coefficient: ； in, ; in, Here, is the similarity coefficient, and i is the number of times the signal was sampled. and These are the values ​​obtained by IMF4 and IMF5 in the i-th signal acquisition, respectively. and These are the average values ​​of all IMF4 and IMF5 trials, respectively. The step of determining the signal category of the original signal based on the relationship between the similarity and the preset threshold includes: When similarity coefficient When the similarity coefficient is greater than 0.2, it is a deformation signal; when the similarity coefficient is less than 0.2, it is a broken wire signal.

6. The magnetic flux leakage signal identification device for wire rope broken wire deformation defects according to claim 5, characterized in that, The signal decomposition module is specifically used for: Different orders of Gaussian white noise are added to the signal to be decomposed to obtain multiple sets of new signals; Empirical Mode Decomposition (EMD) is performed on each new signal group to generate the first IMF component of each group; The target IMF signal is obtained by averaging the first IMF component of each group, and the residual signal is calculated from the target IMF signal. The process involves repeatedly adding Gaussian white noise to the residual signal at different times to obtain multiple new signals, performing EMD processing on each new signal to generate the first IMF component of each group, averaging the first IMF component of each group to obtain the target IMF signal, and calculating the residual signal from the target IMF signal, until a target IMF signal containing IMF4 and IMF5 components is obtained. The final result of all target IMF signals is the plurality of target IMF signals.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the magnetic flux leakage signal identification method for wire rope broken wire deformation defects as described in any one of claims 1 to 4.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the magnetic flux leakage signal identification method for wire rope broken wire deformation defects as described in any one of claims 1 to 4.

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