Cable defect positioning method

Abstract

The invention relates to the field of defect detection, and particularly discloses a cable defect positioning method which comprises the following steps: injecting a Gaussian envelope linear chirp signal into a to-be-detected cable, and obtaining a corresponding reflection signal; adding a generalized adjustment factor in the reflected signal to carry out generalized adjustment so as to obtain first time-frequency distribution after generalized adjustment; performing energy rearrangement on the first time-frequency distribution under the same frequency to obtain second time-frequency distribution after energy arrangement; determining a cross-correlation function of the incident signal and the second video distribution to obtain a defect positioning curve of the to-be-detected cable; and determining the defect position of the to-be-detected cable based on the defect positioning curve. Generalized adjustment and energy rearrangement are carried out on traditional S transformation, so that cross terms are inhibited, and meanwhile, the positioning and distinguishing capability of the method is improved; inverse transformation is carried out on time-frequency distribution in an effective frequency band range of the signal, and 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 cable defect location method. Background Art

[0002] Cables are generally buried in specific underground pipelines in cities and are not susceptible to mechanical stress from the external environment. However, adverse environmental factors such as humidity and high temperature cannot be avoided, and local insulation deterioration sections will appear in the cables. If they are not checked and replaced in time, the insulation deterioration sections may further deteriorate into grounding faults and cause power outages. Power companies will regularly carry out maintenance work on cables in operation to reduce the possibility of accidents. The main methods currently used include insulation resistance detection, withstand voltage test, time domain reflection method, and oscillation wave partial discharge detection method.

[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 local 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 comprises:

[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; a 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; based on the defect location curve, the defect position of the cable to be detected is determined.

[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 by the following formula: Where G1(t,f) is the time-frequency distribution after generalized adjustment; t, f, τ are the signal time, signal frequency and integration time variables respectively; K, P, M are generalized adjustment factors. When K = 1, P = 1, M = 0, the time-frequency distribution is equivalent to the traditional S transform; j is an imaginary number; s represents the 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, adding a generalized adjustment factor to the reflected signal specifically includes: determining a value of the generalized adjustment factor by the following formula: Among them, G2(t,f) is the generalized adjusted time-frequency distribution of 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 its formula is:

[0008]

[0009] In one example, the energy rearrangement of the first time-frequency distribution at the same frequency to obtain the second time-frequency distribution after energy arrangement specifically includes: the energy rearrangement of the first time-frequency distribution at the same frequency is performed by the following formula: G S (t,f)=G1(t,f)·W(tt (g) (f),f); 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.

[0010] In one example, the cross-correlation function is defined as: Where 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, where G S (t,f) is the time-frequency distribution of the reflected signal collected at the cable test end.

[0011] In one example, determining the defect location of the cable to be detected based on the defect location curve specifically includes: determining the peak value existing in the defect location curve based on the defect location curve corresponding to the cross-correlation function; determining a target peak value that represents the two ends of the cable to be detected and is greater than a preset constant; 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.

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

[0013] In one example, the inverse transformation function is defined as: Among them, s′(t) is the inverse transformation function, G S ′(t,f) 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 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) (f) time difference.

[0014] In one example, based on the inverse transformation function, the time-frequency distribution corresponding to each target peak is independently inversely transformed, specifically including: retaining the value of the effective component of the time-frequency distribution signal of each target peak and setting the non-signal component to zero through the following formula: Where i represents the i-th peak in the positioning curve, t C (i) represents the time point of the peak.

[0015] In one example, based on the reconstructed signal, determining the defect type corresponding to each target peak specifically includes: determining the peak positions corresponding to the target peaks in the reconstructed signal; determining the extreme point closest to the peak position of the target peak in the reconstructed signal; if the extreme point is a maximum value, then the defect type corresponding to the target peak is a positive polarity defect; if the extreme point is a minimum value, then the defect type corresponding to the target peak is a negative polarity defect.

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

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of an implementation scenario of a cable defect location method in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of the time domain waveforms of an incident signal and a reflected signal in an embodiment of the present application;

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

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

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

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

[0024] Figure 7 This is a schematic diagram of reconstructing signal polarity determination in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] Among the existing technologies for detecting cable defects, the time-frequency domain reflection method is a new method that combines the weak defect location capability of time domain and frequency domain fusion information. By injecting a Gaussian envelope linear chirp signal into the target cable, the signal will be reflected at the defect and propagate back to the signal injection end. The waveform measured at the signal injection end is converted to the time-frequency domain and cross-correlation calculation is performed to obtain the defect location curve.

[0027] The traditional time-frequency domain conversion method uses the Wigner-Ville distribution, which has the best time-frequency concentration for a single signal component. However, for defective cable signals with multiple reflected signal components, this distribution will produce cross-interference components between every two signal components, which will lead to misjudgment of the positioning results. In addition, this distribution has a second-order relationship with the time domain waveform, and it is difficult to restore the time domain waveform based on the time-frequency distribution, which is not convenient for identifying the polarity type of the defect. The existing S-transformation method avoids the interference of cross-terms through the first-order time-frequency distribution and has the ability to inversely transform the time-frequency into a time domain waveform. However, the time domain width of the transformed time-frequency distribution is too large, which will affect the resolution of defect location. Therefore, there is an urgent need for a method without cross-term interference, with inverse time-frequency transformation function, and with strong positioning resolution, so as to achieve effective detection of cable defects.

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

[0029] One or more embodiments of the present application provide a cable defect location method, which 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 some 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 the convenience of understanding and description, the following embodiments are described in detail by taking 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 limitations on this.

[0032] The present application provides a cable defect location method, including:

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

[0034] S102: adding a generalized adjustment factor to the reflected signal to perform generalized adjustment to obtain a first time-frequency distribution after generalized adjustment.

[0035] The improvement 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 for the generalized adjusted time-frequency distribution, which has the characteristics of cross-term suppression, inverse time-frequency transformation, and strong positioning resolution. The generalized adjusted time-frequency distribution is defined as:

[0036]

[0037] Where G1(t,f) is the time-frequency distribution after generalized adjustment; t, f, τ are the signal time, signal frequency and integration time variables respectively; K, P, M are generalized adjustment factors. When K = 1, P = 1, M = 0, the time-frequency distribution is equivalent to the traditional S transform; j is an imaginary number; s represents the 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 generalized adjustment factors, which can 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 0, the values ​​of the three generalized adjustment factors can be determined. It should be noted that Φ(K, P, M) is essentially a similarity calculation in the time-frequency domain between the generalized adjusted time-frequency distribution and the Wigner-Ville distribution with the best time-frequency performance of the single-component signal; G2 and W here are the generalized adjusted time-frequency distribution with the Gaussian envelope linear chirp signal as the incident signal, and the Wigner-Ville time-frequency distribution with the Gaussian envelope linear chirp signal as the incident signal.

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

[0045] After the first time-frequency distribution is obtained, when performing energy rearrangement, the energy of the first time-frequency distribution can be rearranged at the same frequency by 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 integral 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 by the cable test end. The function curve corresponding to the cross-correlation function is the defect location curve.

[0052] S105: Determine the defect position of the cable to be detected 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 will be a lot of noise interference when measuring the field signal, 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 limits of the frequency 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) (f) The time difference 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 value respectively;

[0061] 2) When the amplitude of the time-frequency distribution increases or the amplitude of the time-frequency distribution is less than 0.001 times of the peak value at a certain time point during the process of time axis reduction, it is determined that the lower limit of the time axis 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 of 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 transformation will introduce sidelobe interference. The sidelobe of the signal with a higher amplitude may drown out the signal of a weak defect. In order to reduce the sidelobe interference, this patent independently inversely transforms the time-frequency distribution corresponding to the peak value of each positioning curve, and superimposes them to form a reconstructed signal. The independent inverse transformation needs to retain the value of the effective component of the signal and set the non-signal component to zero. The range and function of zeroing are defined as:

[0064]

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

[0066] In one embodiment, when determining the type of polarity defect, the following steps may be used to determine:

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

[0068] In order to intuitively demonstrate the improvement effects of the time-frequency distribution and inverse transformation proposed in this patent, a cable defect detection experiment was carried out with a total length of 200m, aging defects set at 70m, and breakage defects set at 130m. Figure 2 In the time domain waveform -0.5×10 -6 s to 0.5×10 -6 The part of s is the incident signal, with a center frequency of 5MHz, a bandwidth of 10MHz, and a signal duration of 680ns, located at 2×10 -6 s to 3×10 -6 The signal of s is the signal reflected from the open end of the 200m cable.

[0069] Figure 3 There are obvious cross terms in the Wigner-Ville distribution, which leads to Figure 6 The middle positioning curve cannot effectively locate the two defects, but locates the cross-term component near 100m; Figure 4 Although the traditional S-transform shown in the figure has no cross-term interference, the time span between the incident signal and the reflected signal at the end of the cable is too large, resulting in Figure 6 There are no peaks except for the 0m cable head end and the 200m cable end, and the defects in the cable cannot be effectively identified; 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 the positioning results of 69.8m and 130.4m respectively, which are very close to the actual positions. The remaining peaks within 0 to 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] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description 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 as the 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 may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions 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 (CPU), input / output interfaces, network interfaces, and memory.

[0077] The 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. The 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 disk read-only memory (CD-ROM), digital versatile disk (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 media such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0080] The above is only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A cable defect location method, characterized in that: include: Injecting a Gaussian envelope linear chirp signal into the cable to be detected and obtaining a corresponding reflection 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 the energy of the first time-frequency distribution at the same frequency to obtain a second time-frequency distribution after energy arrangement; Determining a cross-correlation function between an incident signal and the second video distribution to obtain a defect location curve of the cable to be detected; Based on the defect location curve, the defect position of the cable to be detected is determined.

2. The method according to claim 1, characterized in that The adding of the generalized adjustment factor to the reflected signal to perform generalized adjustment to obtain the first time-frequency distribution after generalized adjustment specifically includes: The reflected signal is generally conditioned by the following formula: Among them, G1(t,f) is the time-frequency distribution after generalized adjustment; t, f, τ are signal time, signal frequency and integration time variables respectively; K, P, M are generalized adjustment factors. When K = 1, P = 1, M = 0, the time-frequency distribution is equivalent to the traditional S transform; j is an imaginary number; s represents the 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.

3. The method according to claim 2, characterized in that 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: Among them, G2(t,f) is the generalized adjusted time-frequency distribution of 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 its formula is:

4. The method according to claim 3, characterized in that The rearrangement of 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 of the first time-frequency distribution at the same frequency is rearranged by the following formula: G S (t,f)=G1(t,f)·W(t-t (g) (f),f) 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.

5. The method according to claim 4, characterized in that The cross-correlation function is defined as: Where 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, where G S (t,f) is the time-frequency distribution of the reflected signal collected at the cable test end.

6. The method according to claim 1, characterized in that The step of determining the defect position of the cable to be detected based on the defect location curve specifically includes: Based on the defect location curve corresponding to the cross-correlation function, determining a peak value existing in the defect location curve; Determine a target peak value other than 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.

7. The method according to claim 5, characterized in that After determining the defect position 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.

8. The method according to claim 7, characterized in that The inverse transform function is defined as: Among them, s′(t) is the inverse transformation function, G S ′(t,f) 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 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) (f) time difference.

9. The method according to claim 8, characterized in that Based on the inverse transformation function, independently inverse transforming the time-frequency distribution corresponding to each target peak value 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: Where i represents the i-th peak in the positioning curve, t C (i) represents the time point of the peak.

10. The method according to claim 9, 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 the peak position of the 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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