A method for locating defects in a cable

Abstract

The application relates to the field of defect detection, and specifically discloses a cable defect positioning method, which comprises the following steps: injecting a Gaussian envelope linear chirp signal into a cable to be detected, and obtaining a corresponding reflection signal; adding a generalized adjustment factor to the reflection signal to perform generalized adjustment, and obtaining a first time-frequency distribution after the generalized adjustment; performing energy rearrangement under the same frequency on the first time-frequency distribution to obtain a second time-frequency distribution after the energy rearrangement; determining a cross-correlation function of the incident signal and the second video distribution to obtain a defect positioning curve of the cable to be detected; and determining a defect position of the cable to be detected based on the defect positioning curve. The traditional S transform is subjected to generalized adjustment and energy rearrangement, so that the cross terms are suppressed and the positioning resolution of the method is improved; the time-frequency distribution in the effective frequency band range of the signal is subjected to inverse transformation, so that the interference of noise on defect polarity discrimination is effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of defect detection, and in particular to a method for locating cable defects. Background Art

[0002] Cables are typically buried in designated underground pipelines in cities, making them less susceptible to mechanical stress from the external environment. However, adverse environmental factors such as humidity and high temperatures are unavoidable, and localized insulation degradation can occur within cables. If not promptly identified and replaced, these degraded sections can further deteriorate into ground faults, causing power outages. Power companies regularly inspect and maintain operating cables to minimize the likelihood of accidents. Currently, the main methods used include insulation resistance testing, withstand voltage testing, time domain reflectometry, and oscillatory wave partial discharge testing.

[0003] Among them, the insulation resistance test method and the voltage withstand test method are methods for evaluating the overall condition of the cable, but they cannot effectively locate local defects; the time domain reflection method is mainly used to locate ground faults and open circuit faults in the cable, but it cannot accurately locate weak defects; the oscillation wave partial discharge detection method can stimulate local defects to generate discharge signals through charging, and then detect the location of the discharge signal, but improper pressurization and charging may cause damage to the cable. Summary of the Invention

[0004] In order to solve the above problems, the present application proposes a cable defect location method, wherein the method includes:

[0005] A Gaussian envelope linear chirp signal is injected into the cable to be detected, and a corresponding reflected signal is obtained; a generalized adjustment factor is added to the reflected signal to perform generalized adjustment to obtain a first time-frequency distribution after generalized adjustment; the energy of the first time-frequency distribution is rearranged at the same frequency to obtain a second time-frequency distribution after energy arrangement; the cross-correlation function between the incident signal and the second video distribution is determined to obtain a defect location curve of the cable to be detected; and the defect position of the cable to be detected is determined based on the defect location curve.

[0006] In one example, adding a generalized adjustment factor to the reflected signal to perform generalized adjustment to obtain a first time-frequency distribution after generalized adjustment specifically includes: performing generalized adjustment on the reflected signal using the following formula: Where G1(t,f) is the time-frequency distribution after generalized conditioning; t, f, and τ are the signal time, signal frequency, and integration time variables, respectively; K, P, and M are generalized conditioning factors. When K = 1, P = 1, and M = 0, the time-frequency distribution is equivalent to the traditional S transform; j is an imaginary number; and s represents a Gaussian envelope linear chirp signal, which is defined as: Among them, α, β, and f0 are the signal period factor, frequency change factor, and the center frequency of the signal respectively.

[0007] In one example, the increasing the generalized modulation factor in the reflected signal specifically includes determining the value of the generalized modulation factor by the following formula: Wherein, G2(t,f) is the generalized modulation after the Gaussian envelope linear chirp signal as the incident signal, and the formula is: W(t,f) is the Wigner-Ville time-frequency distribution of the Gaussian envelope linear chirp signal as the incident signal, and the formula is:

[0008]

[0009] In one example, the energy rearrangement of the first time-frequency distribution under the same frequency to obtain the second time-frequency distribution after energy rearrangement specifically includes: rearranging the energy of the first time-frequency distribution under the same frequency by the following formula: G S (t,f) = G1(t,f)·W(t-t (g) (f),f); Wherein t (g) (f) represents the time corresponding to the gth peak in the time-frequency function G1(t,f) at frequency f, that is, the time corresponding to the instantaneous frequency.

[0010] In one example, the cross-correlation function is defined as: Wherein, t' is the integral time variable, T s is the period of the incident signal, G Si is the time-frequency distribution of the incident signal, and G S (t,f) is the time-frequency distribution of the reflected signal collected by the cable test end.

[0011] In one example, the defect position of the cable to be detected is determined based on the defect positioning curve, specifically including: determining the peak value existing in the defect positioning curve based on the defect positioning curve corresponding to the cross-correlation function; determining the target peak value greater than the preset constant except representing the two ends of the cable to be detected; the abscissa corresponding to the target peak value is the distance between the defect position and the test end of the cable to be detected.

[0012] In one example, after determining the defect position of the cable to be detected, the method further includes: determining the inverse transform function based on the second time-frequency distribution; independently inverse transforming the time-frequency distribution corresponding to each target peak value based on the inverse transform function; superimposing the time-frequency distribution after independent inverse transformation of each target peak value to form a reconstructed signal; determining the defect type corresponding to each target peak value based on the reconstructed signal.

[0013] In one example, the inverse transform function is defined as: where s'(t) is the inverse transform function, G S is the normalized second time-frequency distribution, which is defined as: where f max , f min are the upper and lower limits of the frequency of the incident signal, respectively; t N and t1 are the last and first time points of the collected signal, respectively; T1 (g) (f) and T2 (g) (f) represent the time difference between the lower and upper limits of the time axis corresponding to the peak in the gth time-frequency distribution at frequency f and t (g) (f), respectively.

[0014] In one example, the inverse transform function is used to independently inverse transform the time-frequency distribution corresponding to each target peak, specifically including: the numerical value of the signal component of each target peak is retained and the non-signal component is set to zero by the following formula: where i represents the ith peak in the positioning curve, t C (i) represents the time point of the peak.

[0015] In one example, the reconstructed signal is used to determine the defect type corresponding to each target peak, specifically including: the peak position corresponding to each target peak is determined in the reconstructed signal; the extreme point closest to the peak position of the target peak is determined in the reconstructed signal; if the extreme point is a maximum value, the defect type corresponding to the target peak is a positive polarity defect; if the extreme point is a minimum value, the defect type corresponding to the target peak is a negative polarity defect.

[0016] The method proposed in the present application can bring the following beneficial effects: by adjusting the traditional S transform and rearranging the energy, the cross term is suppressed while the positioning resolution of the method is improved; the time-frequency distribution in the effective frequency band of the signal is inverse transformed, effectively reducing the interference of noise on defect polarity discrimination. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate the illustrative embodiments of the present application and their description serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0018] Figure 1 is a schematic diagram of the implementation scene of a cable defect positioning method in the embodiments of the present application;

[0019] Figure 2 is a schematic diagram of the time-domain waveform of the incident signal and the reflected signal in the embodiments of the present application;

[0020] Figure 3 A time-frequency distribution diagram of a Wigner-Ville distribution in an embodiment of the present application;

[0021] Figure 4 A time-frequency distribution diagram of a traditional S transform in an embodiment of the present application;

[0022] Figure 5 A time-frequency distribution diagram of a cable defect positioning method in an embodiment of the present application;

[0023] Figure 6 A defect positioning comparison diagram in an embodiment of the present application;

[0024] Figure 7 A signal polarity reconstruction discrimination diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0026] In the prior art for detecting cable defects, time-frequency domain reflection method is a new method with weak defect positioning capability combining time domain and frequency domain fusion information. A Gaussian envelope linear chirp signal is injected into a target cable. The signal produces reflection at the defect and propagates back to the signal injection end. The waveform measured at the signal injection end is converted to time-frequency domain and correlation calculation is performed to obtain a defect positioning curve.

[0027] The traditional time-frequency domain conversion method adopts Wigner-Ville distribution. The distribution has optimal time-frequency concentration for single signal component, but for defect cable signals with multiple reflected signal components, the distribution will produce cross-interference components between each two signal components, which will cause misjudgment of the positioning result. In addition, the distribution has a second-order relationship with the time domain waveform, which is difficult to restore the time domain waveform based on the time-frequency distribution, and is not convenient for polarity type identification of defects. The existing S transform method avoids the interference of cross terms by means of first-order time-frequency distribution and has the ability of time-frequency inverse transformation to time domain waveform, but the time-frequency distribution time domain width of the transform is too large, which will affect the resolution capability of defect positioning. Therefore, there is an urgent need for a method without cross term interference, with time-frequency inverse transformation function, and with strong positioning resolution capability, so as to realize effective detection of cable defects.

[0028] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0029] One or more embodiments of this application provide a cable defect location method that can be applied to cable defect detection. The test environment is as follows: Figure 1 As shown, the process can be executed by a computing device in the corresponding field, and certain input parameters or intermediate results in the process allow manual intervention and adjustment to help improve accuracy.

[0030] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For ease of understanding and description, the following embodiments are described in detail using a server as an example.

[0031] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and this application does not make any specific restrictions on this.

[0032] The present invention provides a method for locating cable defects, including:

[0033] S101: injecting a Gaussian envelope linear chirp signal into a cable to be detected, and obtaining a corresponding reflected signal.

[0034] S102: Adding a generalized adjustment factor to the reflected signal to perform generalized adjustment, thereby obtaining a first time-frequency distribution after generalized adjustment.

[0035] The improved method proposed in this patent is to add a generalized adjustment factor to the distribution of the S transform and rearrange the energy at the same frequency in the time-frequency distribution after generalized adjustment. It has the characteristics of cross-term suppression, inverse time-frequency transformation, and strong positioning resolution. The time-frequency distribution after generalized adjustment is defined as:

[0036]

[0037] Where G1(t,f) is the time-frequency distribution after generalized conditioning; t, f, and τ are the signal time, signal frequency, and integration time variables, respectively; K, P, and M are generalized conditioning factors. When K = 1, P = 1, and M = 0, the time-frequency distribution is equivalent to the traditional S transform; j is an imaginary number; and s represents a Gaussian envelope linear chirp signal, which is defined as:

[0038]

[0039] Among them, α, β, and f0 are the signal period factor, frequency change factor, and the center frequency of the signal respectively.

[0040] This patent also defines the selection of a generalized adjustment factor to ensure that the adjusted time-frequency distribution can effectively characterize the time-frequency relationship of the signal. The selection formula is as follows:

[0041]

[0042]

[0043] When the partial derivatives of Φ with respect to K, P, and M are all zero, the values ​​of the three generalized conditioning factors can be determined. It should be noted that Φ(K, P, M) essentially calculates the time-frequency domain similarity between the generalized conditioning time-frequency distribution and the Wigner-Ville distribution with optimal time-frequency performance for single-component signals. Here, G2 and W are the generalized conditioning time-frequency distribution and the Wigner-Ville time-frequency distribution, respectively, with a Gaussian envelope linear chirp signal as the incident signal.

[0044] S103: Rearrange the energy at the same frequency on the first time-frequency distribution to obtain a second time-frequency distribution after energy arrangement.

[0045] After obtaining the first time-frequency distribution, when performing energy rearrangement, the energy of the first time-frequency distribution at the same frequency can be rearranged using the following formula:

[0046] G S (t,f)=G1(t,f)·W(tt (g) (f),f)

[0047] where t (g) (f) represents the time corresponding to the g-th peak in the time-frequency function G1(t,f) at frequency f, that is, the time corresponding to the instantaneous frequency.

[0048] S104: Determine a cross-correlation function between the incident signal and the second video distribution to obtain a defect location curve of the cable to be inspected.

[0049] The cross-correlation function is defined as:

[0050]

[0051] Where t′ is the integration time variable, T s is the period of the incident signal, G Si is the time-frequency distribution of the incident signal, where G S (t,f) is the time-frequency distribution of the reflected signal collected at the cable test end. The function curve corresponding to the cross-correlation function is the defect location curve.

[0052] S105: Determine the defect location of the cable to be inspected based on the defect location curve.

[0053] In one embodiment, when determining the defect location, it is necessary to determine the peak value in the defect location curve based on the defect location curve corresponding to the cross-correlation function, and determine the target peak value that represents the two ends of the cable to be detected and is greater than a preset constant (such as 0.3). At this time, the horizontal coordinate corresponding to the target peak value is the distance between the defect location and the test end of the cable to be detected. At this time, the distance can be obtained by Determine where v and x are the wave velocity and the distance from the test end of the cable, respectively.

[0054] In one embodiment, since there is a lot of noise interference when measuring field signals, which will affect the polarity discrimination of the defect signal in the time domain waveform, this patent also defines an inverse transformation function for the proposed time-frequency distribution function:

[0055]

[0056] Among them, s′(t) is the inverse transformation function, G S ′(t,f) is the normalized second time-frequency distribution, which is defined as:

[0057]

[0058] where f max 、f min are the upper and lower frequency limits of the incident signal respectively; t N and t1 are the last time point and the first time point of the collected signal respectively; T1 (g) (f) and T2 (g) (f) represents the lower and upper limits of the time axis corresponding to the peak value in the g-th time-frequency distribution at frequency f and t (g) The time difference of (f) can keep the rearranged frequency domain curve unchanged, ensuring the accuracy of signal restoration;

[0059] This can be determined by the following principles:

[0060] 1) At the peak point t (g) (f) In the direction of decreasing and increasing toward the time axis, find the lower limit and upper limit of the time corresponding to the peak respectively;

[0061] 2) When the amplitude of the time-frequency distribution increases at a certain time point during the time axis decreases, or the amplitude of the time-frequency distribution is less than 0.001 times the peak value, it is determined that the time axis lower limit of the peak value has been reached;

[0062] 3) When the amplitude of the time-frequency distribution increases or the amplitude of the time-frequency distribution is less than 0.001 times the peak value at a certain time point during the increase of the time axis, it is determined that the time axis upper limit of the peak value has been reached.

[0063] The signal collected by the cable is discrete data, so the actual inverse transform will introduce sidelobe interference, and the sidelobe of the signal with high amplitude may drown the signal of weak defects. In order to reduce the sidelobe interference, the patent independently inversely transforms the time-frequency distribution corresponding to the peak value of each positioning curve, and then superimposes to form a reconstructed signal. Independent inverse transform needs to retain the numerical value of the effective component of the signal, and set the part of the non-signal component to zero. The range and function definition of zero setting are:

[0064]

[0065] Where i represents the i th peak value in the positioning curve, t C (i) represents the time point of the peak value.

[0066] In one embodiment, when distinguishing the polarity defect type, the following steps can be used for judgment:

[0067] In the reconstructed signal, the peak value position corresponding to the target peak value is determined; in the reconstructed signal, the extreme point closest to the peak value position of the target peak value is determined; if the extreme point is a maximum value, the defect type corresponding to the target peak value is a positive polarity defect; if the extreme point is a minimum value, the defect type corresponding to the target peak value is a negative polarity defect.

[0068] In order to intuitively show the improvement effect of the time-frequency distribution and inverse transform proposed in the patent, a cable defect detection experiment was carried out, in which the full length of the cable was 200 m, the aging defect was set at 70 m, and the damage defect was set at 130 m. Figure 2 The part of -0.5x10 -6 s to 0.5x10 -6 s in the time domain waveform is the incident signal, the center frequency is 5 MHz, the bandwidth is 10 MHz, the signal duration is 680 ns, and the signal is located at 2x10 -6 s to 3x10 -6 s is the signal reflected at the open end of the 200 m cable.

[0069] Figure 3 The Wigner-Ville distribution in the middle has obvious cross terms, which leads to Figure 6 The positioning curve in the middle cannot effectively locate the two defects, but locates the cross term component near 100 m; Figure 4 The traditional S transform shown in the figure has no cross term interference, but the time span of the incident signal and the reflected signal at the end of the cable is too large, which leads to Figure 6 There is no peak value in the middle except the cable head at 0 m and the cable end at 200 m, which cannot effectively distinguish the defects existing in the cable; and Figure 5The time-frequency distribution proposed in this patent does not have the interference of cross terms, and the span of the time domain is close to the single-component optimal Wigner-Ville distribution. Figure 6 Two defects were successfully located in the positioning curve, with positioning results of 69.8m and 130.4m respectively, which are very close to the actual positions. The remaining peaks between 0 and 200m are less than 0.3 and are considered to be negligible interference. Figure 7 The inverse transformed curve can also successfully identify the positive and negative polarity of the defect location point. The polarity near 70m is negative and the polarity near 130m is positive, which is the same as the polarity of the actual defect. Figure 2 Due to noise, the polarity cannot be effectively identified.

[0070] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0071] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0072] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0078] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transit media), such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0080] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A cable defect location method, characterized in that: include: Inject a Gaussian envelope linear chirp signal into the cable to be tested and obtain the corresponding reflected signal; Adding a generalized adjustment factor to the reflected signal to perform generalized adjustment to obtain a first time-frequency distribution after generalized adjustment; Rearranging energy at the same frequency on the first time-frequency distribution to obtain a second time-frequency distribution after energy arrangement; determining a cross-correlation function between the incident signal and the second time-frequency distribution to obtain a defect location curve of the cable to be inspected; Determining the defect location of the cable to be detected based on the defect location curve; The adding of the generalized adjustment factor to the reflected signal to perform generalized adjustment to obtain a first time-frequency distribution after generalized adjustment specifically includes: The reflected signal is generally conditioned by the following formula: in, is the time-frequency distribution after generalized adjustment; 、 、 are signal time, signal frequency and integration time variables respectively; K, P and M are generalized adjustment factors. When K=1, P=1 and M=0, the time-frequency distribution is equivalent to the traditional S transform; is an imaginary number; represents a Gaussian envelope linear chirp signal, which is defined as: ,in 、 、 are the signal period factor, frequency variation factor and the center frequency of the signal respectively; The adding of a generalized adjustment factor to the reflected signal specifically includes: The value of the generalized adjustment factor is determined by the following formula: When the partial derivatives of Φ with respect to K, P, and M are all 0, the values ​​of the three generalized adjustment factors can be determined; among them, To take the Gaussian envelope linear chirp signal as the generalized conditioned time-frequency distribution of the incident signal, the formula is: The Wigner-Ville time-frequency distribution of the incident signal with a Gaussian envelope linear chirp signal is given by: ; Rearranging the energy at the same frequency on the first time-frequency distribution to obtain a second time-frequency distribution after energy arrangement specifically includes: The energy at the same frequency of the first time-frequency distribution is rearranged using the following formula: in Represents the time-frequency function at frequency f Middle The time corresponding to the peak value is the time corresponding to the instantaneous frequency; The cross-correlation function is defined as: in, is the integration time variable, is the period of the incident signal, is the time-frequency distribution of the incident signal, where This is the time-frequency distribution of the reflected signal collected at the cable test end.

2. The method according to claim 1, characterized in that Determining the defect location of the cable to be detected based on the defect location curve specifically includes: Determining a peak value in the defect location curve based on the defect location curve corresponding to the cross-correlation function; Determine a target peak value other than that representing the two ends of the cable to be detected and greater than a preset constant; The horizontal coordinate corresponding to the target peak value is the distance between the defect position and the test end of the cable to be detected.

3. The method according to claim 1, characterized in that After determining the defect location of the cable to be detected, the method further includes: determining an inverse transformation function based on the second time-frequency distribution; Based on the inverse transformation function, independently inverse transform the time-frequency distribution corresponding to each target peak; The time-frequency distributions of each target peak after independent inverse transformation are superimposed to form a reconstructed signal; Based on the reconstructed signal, the defect type corresponding to each target peak is determined.

4. The method according to claim 3, characterized in that The inverse transform function is defined as: in, is the inverse transformation function, is the normalized second time-frequency distribution, which is defined as: in 、 are the upper and lower frequency limits of the incident signal respectively; and are the last time point and the first time point of the collected signal respectively; and Represents the frequency Next The peaks in the time-frequency distribution correspond to the lower and upper limits of the time axis and time difference.

5. The method according to claim 4, characterized in that The step of independently inverse transforming the time-frequency distribution corresponding to each target peak based on the inverse transform function specifically includes: The following formula is used to retain the value of the effective component of the time-frequency distribution signal of each target peak, and set the non-signal component to zero: in Represents the first position in the positioning curve Peak value, represents the time point of the peak.

6. The method according to claim 5, characterized in that The determining, based on the reconstructed signal, the defect type corresponding to each target peak value specifically includes: In the reconstructed signal, determining peak positions corresponding to the target peaks respectively; In the reconstructed signal, determining an extreme point to which a peak position of a target peak is closest; If the extreme value point is a maximum value, the defect type corresponding to the target peak value is a positive polarity defect; If the extreme value point is a minimum value, the defect type corresponding to the target peak value is a negative polarity defect.

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