A cable fault diagnosis method, system and terminal

Abstract

The application relates to the technical field of fault diagnosis, and discloses a cable fault diagnosis method, a cable fault diagnosis system and a terminal. The method comprises the following steps: a broadband excitation signal is applied to a target cable to excite the target cable to generate a first response signal; multi-dimensional time-frequency features of the first response signal after self-adaptive wavelet threshold denoising are extracted, and a fault template of a preset cable reference model is matched according to energy distribution features in the multi-dimensional time-frequency features; a fault area in the fault template corresponding to the target cable is determined according to a propagation time and a propagation speed of the first response signal; a narrowband excitation signal is applied to the fault area of the target cable to obtain an impedance spectrum; a typical fault mode in a fault feature database is compared with a resonance frequency point and a corresponding impedance amplitude value of the impedance spectrum, so that a fault type and a fault position of the target cable are obtained; a diagnosis report of the target cable is generated according to the fault type and the fault position; and the application can improve the accuracy of cable fault diagnosis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault diagnosis, in particular to a cable fault diagnosis method, system and terminal. BACKGROUND

[0002] In the field of key infrastructures such as power transmission and communication networks, cables are the core signal and energy transmission carriers, and their operating state is directly related to the overall stability of the system. Once a fault occurs, it can easily cause serious consequences such as large-scale downtime and signal interruption. Therefore, accurate fault diagnosis is a core requirement for ensuring the reliable operation of infrastructure. However, existing cable fault diagnosis techniques have significant defects in the signal excitation and feature extraction stages. Most techniques use fixed parameter excitation signals that do not dynamically adjust to the target cable's material, length, and initial response signal quality, resulting in low signal-to-noise ratio of the excited response signal and insufficient effective information proportion. At the same time, traditional denoising methods have limited filtering effect on complex interference, and the time-frequency feature extraction dimension is single, which cannot fully capture the energy distribution law related to the fault, making the matching process with the preset fault template prone to deviation and difficult to accurately preliminarily lock the fault type.

[0003] In addition, the existing technology has difficulty in meeting the actual demand in the fault area positioning and final fault determination stage. When determining the fault area based on the propagation characteristics of the response signal, traditional methods have difficulty in accurately identifying the weak signal distortion point caused by the reflection of the fault point, and the propagation speed parameter selection is not dynamically adapted to the cable type and operating environment, resulting in large error in the approximate distance calculation of the fault point. In the impedance spectrum comparison stage, the traditional matching degree calculation does not assign differential weights to key resonance frequency points, and the reference features of typical fault patterns in the fault feature database are not fully covered, making the comparison result of the resonance frequency and impedance amplitude value have low reliability. Not only can different types of faults not be accurately distinguished, but the fault location positioning deviation can also exceed the engineering allowable range, seriously affecting the fault repair efficiency and system recovery speed. SUMMARY

[0004] The present disclosure provides a cable fault diagnosis method, system and terminal.

[0005] In a first aspect, the present disclosure provides a cable fault diagnosis method, comprising:

[0006] S1: applying a broadband excitation signal to a target cable to excite the target cable to generate a first response signal;

[0007] S2: extracting multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and matching the energy distribution characteristics in the multi-dimensional time-frequency features with the fault templates of a preset cable reference model;

[0008] S3: determining a fault region in a fault template corresponding to the target cable according to the propagation time and the propagation speed of the first response signal;

[0009] S4: applying a narrowband excitation signal to the fault region of the target cable to obtain an impedance spectrum of the target cable;

[0010] S5: comparing the resonance frequency point and the corresponding impedance amplitude value of the impedance spectrum with a typical fault mode in a fault feature database to obtain a fault type and a fault position of the target cable;

[0011] S6: generating a diagnostic report of the target cable according to the fault type and the fault position.

[0012] In a preferred embodiment, the wideband excitation signal applied to the target cable to excite the target cable to generate the first response signal comprises:

[0013] selecting an initial frequency band range and a signal form corresponding to the target cable from a preset excitation parameter library;

[0014] generating an initial wideband excitation signal of the target cable according to the initial frequency band range and the signal form;

[0015] applying the initial wideband excitation signal to the start end of the target cable, and collecting an initial response of the target cable;

[0016] adaptively adjusting the amplitude of the initial wideband excitation signal according to the signal quality of the initial response to obtain the wideband excitation signal of the target cable.

[0017] In a preferred embodiment, the matching of the energy distribution feature in the multi-dimensional time-frequency feature to the fault template of the preset cable reference model comprises:

[0018] decomposing the first response signal after adaptive wavelet threshold denoising into intrinsic mode components representing different oscillation modes;

[0019] combining the skewness value, the pulse factor and the energy proportion of the intrinsic mode components to obtain the multi-dimensional time-frequency feature of the target cable.

[0020] In a preferred embodiment, the matching of the energy distribution feature in the multi-dimensional time-frequency feature to the fault template of the preset cable reference model comprises:

[0021] extracting the energy distribution feature from the multi-dimensional time-frequency feature;

[0022] The extracted energy distribution features are compared one by one with the reference energy distributions under various typical fault states stored in the preset cable reference model to calculate the morphological similarity of the target cable.

[0023] The fault state corresponding to the reference energy distribution with the highest morphological similarity is selected as the fault template of the target cable.

[0024] In a preferred embodiment, the formula for calculating the morphological similarity is as follows:

[0025] ;

[0026] in, The morphological similarity is... For the first Adaptive weights for frequency band allocation The vectorized energy distribution characteristics in the first... Energy value in each frequency band For vectorized baseline features in the first Energy value in each frequency band The total number of preset frequency bands, This is the ordinal number of the preset frequency band.

[0027] In a preferred embodiment, determining the fault region in the fault template corresponding to the target cable based on the propagation time and propagation speed of the first response signal includes:

[0028] Identify significant distortion points caused by reflections from fault points in the first response signal, and use the offset of the distortion points relative to the time when the broadband excitation signal was emitted as the propagation time of the first response signal.

[0029] Based on the cable type indicated by the fault template, a preset propagation speed parameter table is queried to obtain the signal propagation speed corresponding to the cable type, which is used as the propagation time of the first response signal;

[0030] The product of the propagation time and the propagation speed is used as the approximate distance between the fault point and the measurement point in the target cable;

[0031] The approximate distance is compared with the predefined area distance boundary in the fault template to determine the specific segment of the fault point, and the specific segment is output as the fault area of ​​the target cable.

[0032] In a preferred embodiment, the resonant frequency point based on the impedance spectrum and the corresponding impedance amplitude value are compared with typical fault modes in the fault feature database to obtain the fault type and fault location of the target cable, including:

[0033] identify a resonance peak with an amplitude exceeding a preset threshold from the impedance spectrum, and record a frequency value and a corresponding impedance amplitude value of the resonance peak to obtain a feature set to be analyzed of the resonance peak;

[0034] perform matching degree calculation on the feature set to be analyzed and a reference feature set corresponding to a typical fault mode in the fault feature database;

[0035] According to the matching degree calculation result, the candidate fault mode with the highest matching degree is screened out, wherein the matching degree calculation is as follows:

[0036] ;

[0037] In the formula, is the matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is an adaptive weight coefficient determined by the feature saliency index of the reference feature set at the key resonance frequency point, is an initial matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is a normalized value of the relative error of impedance amplitude of the feature set to be analyzed and the reference feature set at the preset key resonance frequency point;

[0038] Based on the fault location calculation rule corresponding to the candidate fault mode and the resonance frequency points in the feature set to be analyzed, the fault location of the target cable is determined, and the candidate fault mode with the highest matching degree is determined as the fault type.

[0039] In a preferred embodiment, the determination of the final fault location of the target cable based on the fault location calculation rule corresponding to the candidate fault mode and the resonance frequency points in the feature set to be analyzed comprises:

[0040] The fault location calculation rule corresponding to the candidate fault mode is called from a pre-stored rule library;

[0041] The resonance frequency points in the feature set to be analyzed are sorted according to the frequency values to obtain a resonance frequency point sequence of the feature set to be analyzed;

[0042] According to the mapping relationship of the fault location calculation rule, the fault location of the resonance frequency point sequence is derived.

[0043] Compared with the prior art, the present application has the following beneficial effects:

[0044] 1.The cable fault diagnosis method of the present application has significant advantages in the signal excitation and feature extraction link, which can effectively improve the accuracy and reliability of the early diagnosis. It selects the initial frequency band range and signal form from the preset excitation parameter library to generate an initial wideband excitation signal, and adjusts the amplitude according to the signal quality of the initial response to ensure that the wideband excitation signal applied to the target cable is highly adapted to the cable characteristics, thereby exciting a high-quality first response signal. At the same time, after adaptive wavelet threshold denoising, the first response signal is decomposed into intrinsic mode components representing different oscillation modes, and the skewness value, pulse factor and energy ratio are combined to form multi-dimensional time-frequency features, and then based on the morphological similarity calculation with adaptive weight, the fault template of the preset cable reference model is matched, which can comprehensively and accurately capture the fault related information, and lay a solid foundation for subsequent fault location.

[0045] 2.The design of the present application in the fault area determination and final fault judgment link further improves the accuracy and pertinence of the overall diagnosis. It determines the propagation time by identifying the significant distortion points caused by the fault point reflection in the first response signal, and calculates the approximate distance of the fault point by combining the preset propagation speed of the corresponding cable type, and then compares the area distance boundary in the fault template to determine the fault area, which realizes the accurate division of the fault area. Then narrowband excitation is applied to the fault area to obtain the impedance spectrum, and based on the matching degree calculation with adaptive weight containing the key resonance frequency point, the resonance frequency point and impedance amplitude value of the impedance spectrum are compared with the typical fault mode in the fault feature database, which can accurately identify the fault type and determine the fault location. The final diagnosis report can directly provide accurate basis for fault repair, greatly improving the diagnosis effect of cable fault. BRIEF DESCRIPTION OF DRAWINGS

[0046] In the following, the present disclosure will be described in more detail based on embodiments and with reference to the accompanying drawings:

[0047] Figure 1 A work flow diagram of a cable fault diagnosis method of the first embodiment of the present application is shown;

[0048] Figure 2 A functional module diagram of a cable fault diagnosis system of the second embodiment of the present application is shown;

[0049] Figure 3 A composition structure schematic diagram of a terminal for implementing the cable fault diagnosis method of the third embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] In order for those skilled in the technical field to better understand the technical solutions of the present disclosure, and to fully understand and implement the implementation process of the present disclosure how to apply technical means to solve technical problems and achieve the corresponding technical effects, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all. The embodiments of the present disclosure and various features in the embodiments can be combined with each other without conflict, and the technical solutions formed thereby are all within the protection scope of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present disclosure.

[0051] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0052] Embodiment one

[0053] Figure 1 A flowchart of a cable fault diagnosis method provided by the embodiments of the present disclosure is shown. As shown in the figure, a smart device control method comprises: Figure 1

[0054] S1: A broadband excitation signal is applied to a target cable to excite the target cable to generate a first response signal;

[0055] In the embodiments of the present disclosure, the broadband excitation signal is applied to the target cable to excite the target cable to generate the first response signal, comprising:

[0056] Selecting an initial frequency band range and signal form corresponding to the target cable from a preset excitation parameter library;

[0057] Generating an initial broadband excitation signal of the target cable according to the initial frequency band range and signal form;

[0058] Applying the initial broadband excitation signal to the beginning end of the target cable, and collecting the initial response of the target cable;

[0059] According to the signal quality of the initial response, the amplitude of the initial broadband excitation signal is adaptively adjusted to obtain the broadband excitation signal of the target cable.

[0060] Specifically, the model and specification of the target cable are confirmed from the preset excitation parameter library, the matching entry is found and the corresponding content is extracted to obtain the initial frequency band range and signal form of the target cable. ​

[0061] Further, the initial frequency band range and signal form are input into the signal generating device, the device is started after the frequency band and signal type are set, and the signal output by the device is the initial broadband excitation signal of the target cable.

[0062] Further, the signal generating device is connected to the initial end of the target cable by a shielded cable, and the initial broadband excitation signal is output after being started; at the same time, the data acquisition device is connected to the end of the cable, and the end signal is collected after the device is started, and the collected signal is the initial response of the target cable.

[0063] Further, it is judged whether the waveform of the initial response is complete and the amplitude is within the effective range of the data acquisition device, and if not, the amplitude knob of the signal generating device is adjusted, and then the signal is applied again, the response is collected, and the judgment is made until the signal quality meets the requirements, at which time the signal output by the signal generating device is the broadband excitation signal of the target cable.

[0064] In summary, the initial frequency band range and signal form corresponding to the target cable are selected from the preset excitation parameter library, the initial broadband excitation signal is generated, and the signal amplitude is adaptively adjusted according to the signal quality of the initial response, so that the finally applied broadband excitation signal can accurately match the characteristics such as material and length of the target cable, avoiding the problem of poor adaptability of the fixed parameter excitation signal to the cable, effectively improving the signal-to-noise ratio and effective information ratio of the first response signal, and ensuring that the fault-related features in the signal are clear and identifiable.

[0065] In summary, the high-quality first response signal provides a reliable data basis for subsequent adaptive wavelet threshold denoising, intrinsic modal component decomposition, multi-dimensional time-frequency feature extraction, and fault template matching, reduces the feature extraction deviation caused by insufficient signal quality, ensures the accuracy of the subsequent fault template matching link, and lays a key foundation for preliminarily locking the fault state and promoting the overall diagnosis process.

[0066] S2: Extracting multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and matching a fault template of a preset cable reference model according to energy distribution characteristics in the multi-dimensional time-frequency features;

[0067] In the embodiment of the application, the matching of the fault template of the preset cable reference model according to the energy distribution characteristics in the multi-dimensional time-frequency features comprises:

[0068] The first response signal after adaptive wavelet threshold denoising is decomposed into intrinsic modal components representing different oscillation modes;

[0069] The skewness value, pulse factor and energy proportion of the intrinsic modal components are combined to obtain the multi-dimensional time-frequency features of the target cable.

[0070] In this embodiment of the invention, the step of matching a fault template of a preset cable reference model based on the energy distribution characteristics in the multi-dimensional time-frequency characteristics includes:

[0071] Extract the energy distribution features from the multi-dimensional time-frequency features;

[0072] The extracted energy distribution features are compared one by one with the reference energy distributions under various typical fault states stored in the preset cable reference model to calculate the morphological similarity of the target cable.

[0073] The fault state corresponding to the reference energy distribution with the highest morphological similarity is selected as the fault template of the target cable.

[0074] In this embodiment of the invention, the formula for calculating the morphological similarity is as follows:

[0075] ;

[0076] in, The morphological similarity is... For the first Adaptive weights for frequency band allocation The vectorized energy distribution characteristics in the first... Energy value in each frequency band For vectorized baseline features in the first Energy value in each frequency band The total number of preset frequency bands, This is the ordinal number of the preset frequency band.

[0077] Specifically, the first response signal after adaptive wavelet threshold denoising is obtained and processed using the empirical mode decomposition method. First, all local extrema in the signal are identified. Cubic spline curves are used to connect all maximum points to form an upper envelope and connect all minimum points to form a lower envelope. The average value of the upper and lower envelopes is calculated. The average value is subtracted from the original signal to obtain a component. It is checked whether the component meets the conditions of intrinsic mode components, that is, the number of extrema points is equal to or differs from the number of zero-crossing points by at most one, and the average value of the upper and lower envelopes at any point is zero. If it meets the conditions, it is taken as the first intrinsic mode component. The component is subtracted from the original signal to obtain the remaining signal. The above process is repeated for the remaining signal until the remaining signal is a monotonic function or a constant. At this time, all components that meet the conditions are the intrinsic mode components that characterize different oscillation modes.

[0078] Furthermore, for each intrinsic mode component, the skewness value is determined by analyzing the difference between its data distribution and the symmetrical distribution. A positive value indicates a right-skewed distribution, a negative value indicates a left-skewed distribution, and zero indicates a symmetrical distribution. When calculating the impulse factor, the peak value in the component is first identified, and then the average of the absolute values ​​of all data points is calculated. The peak value is divided by the average value to obtain the result. When calculating the energy percentage, the squares of each data point of the component are first squared and then added together to obtain the component energy. Then, the total energy is obtained by adding all the component energies together and dividing the component energy by the total energy to obtain the result. The skewness value, impulse factor, and energy percentage corresponding to each intrinsic mode component are arranged in sequence. These data of all components together constitute the multi-dimensional time-frequency characteristics of the target cable.

[0079] Specifically, when extracting energy distribution features from multi-dimensional time-frequency features, the energy proportion data corresponding to each intrinsic mode component is first located in the multi-dimensional time-frequency features. Then, according to the oscillation mode order of each intrinsic mode component, these energy proportion data are sequentially organized into an ordered set, which is the extracted energy distribution feature of the target cable.

[0080] Furthermore, when calculating the morphological similarity by comparing the extracted energy distribution features with the baseline energy distributions under various typical fault states pre-stored in the preset cable reference model, it is first clarified that each typical fault state in the preset cable reference model corresponds to a set of baseline energy distributions arranged in the same oscillation mode order. During the comparison, a set of baseline energy distributions is selected, and the energy proportion of each component in the energy distribution feature is compared with the energy proportion of the corresponding component of the baseline energy distribution in the oscillation mode order. The difference between the corresponding values ​​of each set is recorded, and the differences of all components are comprehensively summarized and calculated. The result is the morphological similarity of the target cable corresponding to the baseline energy distribution. This process is repeated until the energy distribution features are compared with all baseline energy distributions and the corresponding morphological similarity is obtained.

[0081] Furthermore, when selecting the fault state corresponding to the reference energy distribution with the highest morphological similarity as the fault template of the target cable, all obtained morphological similarities are first compared in terms of numerical value. The morphological similarity with the largest value is then identified, and the reference energy distribution corresponding to the largest morphological similarity is determined. The typical fault state associated with the reference energy distribution is then searched in the preset cable reference model. This typical fault state is the fault template of the target cable.

[0082] Specifically, no. Adaptive weighting for frequency band allocation The source is determined based on the importance of each frequency band in distinguishing cable fault states. Specifically, by analyzing historical cable fault data, the contribution of energy changes in different frequency bands in distinguishing different fault types is statistically analyzed. Frequency bands with higher contribution are assigned higher weights, and frequency bands with lower contribution are assigned lower weights. Each frequency band corresponds to a specific adaptive weight.

[0083] Further, the energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature.

[0084] Further, the energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature.

[0085] Further, the energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature.

[0086] Further, the energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. The energy value of the vectorized energy distribution feature in the first preset frequency band is obtained by vectorizing the energy distribution feature extracted from the multi-dimensional time-frequency feature. ​The value can directly reflect the similarity of both in the energy distribution form, and provide a basis for subsequent selection of fault templates.

[0087] In general, adaptive wavelet threshold denoising can effectively filter out the interference components in the first response signal, ensuring signal purity and providing a high-quality data basis for subsequent feature extraction. On this basis, the denoised signal is decomposed into intrinsic mode components representing different oscillation modes, and the skewness value, pulse factor and energy proportion are combined to form a multi-dimensional time-frequency feature, which can comprehensively capture fault-related signal characteristics, avoid missing fault information caused by single feature dimension limitation, and ensure the integrity and representativeness of the feature.

[0088] In general, the matching stage focuses on the energy distribution characteristics in the multi-dimensional time-frequency feature, and compares the reference energy distribution of the typical fault state in the preset cable reference model one by one, and calculates the shape similarity formula with adaptive weight, which can highlight the influence of key frequency bands on the matching result, reduce the interference of non-key frequency bands, and greatly improve the matching accuracy. It can accurately select the fault template that best matches the target cable fault state, and provide reliable preliminary judgment basis for subsequent determination of fault area and accurate diagnosis.

[0089] S3: determining the fault area in the target cable corresponding fault template according to the propagation time and propagation speed of the first response signal;

[0090] In the embodiments of the present application, the determination of the fault area in the target cable corresponding fault template according to the propagation time and propagation speed of the first response signal comprises:

[0091] Identify the significant distortion point caused by the reflection of the fault point from the first response signal, and take the offset of the distortion point relative to the time when the wideband excitation signal is emitted as the propagation time of the first response signal;

[0092] According to the cable type indicated by the fault template, query the preset propagation speed parameter table to obtain the signal propagation speed corresponding to the cable type as the propagation time of the first response signal;

[0093] The product of the propagation time and the propagation speed is taken as the approximate distance between the fault point and the measurement point in the target cable;

[0094] Compare the approximate distance with the pre-defined area distance boundary in the fault template to determine the specific section of the fault point, and output the specific section as the fault area of the target cable.

[0095] Specifically, when the significant distortion points are identified from the first response signal, the response signal of the same type of cable in the normal non-fault state is obtained as a reference waveform, the first response signal is compared with the reference waveform at each time point, and the points with sudden protrusions, depressions or waveform breaks in the first response signal compared with the reference waveform are found out, which are the significant distortion points caused by the fault point reflection. The starting time of the wideband excitation signal is read by using a time recording device, and the time when each significant distortion point appears in the first response signal is also read. The time offset is obtained by subtracting the starting time of the wideband excitation signal from the time when the significant distortion point appears, which is the propagation time of the first response signal.

[0096] Further, the specific type of the target cable indicated from the fault template is extracted, and the preset propagation speed parameter table pre-stores the associated data of different cable types and corresponding signal propagation speeds. The matching entry is found in the parameter table by matching the cable type extracted from the fault template, and the corresponding signal propagation speed value is extracted from the matching entry. The extracted signal propagation speed value is the propagation speed of the first response signal.

[0097] Further, the propagation time value of the first response signal obtained in the first step is taken out, and the propagation speed value of the first response signal obtained in the second step is also taken out. The two values are multiplied to obtain the approximate distance between the fault point and the measurement point in the target cable.

[0098] Further, the pre-defined regional distance boundary is called from the fault template, the approximate distance value obtained in the third step is compared with the regional distance boundary one by one, and it is judged in which distance range the approximate distance value falls. The cable section corresponding to the distance range is the specific section of the fault point. The specific section is output as the result, and the output specific section is the fault region of the target cable.

[0099] In summary, by accurately identifying the significant distortion points caused by the fault point reflection from the first response signal, and using the offset of the time relative to the wideband excitation signal as the propagation time, the influence of non-fault interference signals can be effectively excluded, the accuracy of the propagation time acquisition is ensured, and a reliable basis is provided for subsequent distance calculation.

[0100] In summary, according to the cable type indicated by the fault template, the preset propagation speed parameter table is queried, the signal propagation speed obtained is highly adapted to the characteristics of the target cable, the calculation deviation caused by the fixed propagation speed is avoided, and the calculation accuracy of the approximate distance between the fault point and the measurement point is greatly improved.

[0101] In summary, by comparing the approximate distance with the region distance boundary predefined in the fault template, the specific section where the fault point is located can be accurately locked, the fault region range is determined, the subsequent narrowband excitation signal is accurately applied to the fault region, the resource waste and efficiency loss caused by large-scale detection are avoided, the pertinence and effectiveness of the subsequent diagnosis link are effectively guaranteed, and the accuracy of the overall fault diagnosis is further improved.

[0102] S4: applying a narrowband excitation signal to the fault region of the target cable to obtain an impedance spectrum of the target cable;

[0103] In the embodiment of the present application, a signal generator is used to generate a narrowband excitation signal, the frequency range of the signal generator is set to a fixed narrow frequency interval to meet the narrowband requirement, the output end of the signal generator is connected to the start end interface of the target cable fault region through a shielded cable, a current acquisition device is connected in series in the connection line between the signal generator and the start end of the fault region, and a voltage acquisition device is connected in parallel between the start end and the end of the fault region, so as to ensure that all connection parts are firm and the shielding layer is intact to avoid interference.

[0104] Further, the signal generator is started to make the narrowband excitation signal stably input into the target cable fault region, and the current acquisition device and the voltage acquisition device are started, the current acquisition device acquires the current signal in the fault region line in real time, and the voltage acquisition device acquires the voltage signal between the start end and the end of the fault region in real time, the acquisition process is synchronized with the frequency of the narrowband excitation signal, and it is ensured that each frequency point corresponds to a set of current and voltage data.

[0105] Further, the voltage signal value and the current signal value corresponding to each frequency point are calculated, the impedance value of the fault region at the frequency point is obtained by dividing the voltage value at the frequency point by the corresponding current value, all frequency points and their corresponding impedance values are sequentially arranged in order according to the frequency sequence of the narrowband excitation signal to form a data set, and the data set is arranged with frequency as the horizontal axis and impedance value as the vertical axis, so that the obtained graphical data set is the impedance spectrum of the target cable.

[0106] In summary, the narrowband excitation signal is applied to the determined fault region instead of the whole target cable, the excitation energy can be concentrated on the fault related section, the electrical characteristic interference of the non-fault region can be effectively avoided, the impedance spectrum distortion caused by irrelevant signal mixing can be avoided, the impedance spectrum obtained can accurately focus on the electrical state of the fault region, the relevance of the spectrum information and the fault characteristics can be greatly improved, and the spectrum can contain clear fault related resonance frequency points and impedance amplitude values.

[0107] In general, the impedance spectrum is the core data basis for subsequent comparison with the typical fault mode in the fault feature database. High-quality and high-correlation impedance spectrum can provide reliable basis for subsequent identification of resonance peaks and calculation of matching degree, directly guaranteeing the accuracy of fault type determination and fault location determination, and targeted excitation without excitation of the whole domain of the cable, reducing signal loss and detection redundancy, and further improving the efficiency and accuracy of fault diagnosis.

[0108] S5: Comparing the resonance frequency point and the corresponding impedance amplitude value of the impedance spectrum with the typical fault mode in the fault feature database to obtain the fault type and the fault location of the target cable;

[0109] In the embodiment of the present application, the comparison of the resonance frequency point and the corresponding impedance amplitude value of the impedance spectrum with the typical fault mode in the fault feature database to obtain the fault type and the fault location of the target cable comprises:

[0110] Identifying the resonance peak with an amplitude exceeding a preset threshold from the impedance spectrum, and recording the frequency value and the corresponding impedance amplitude value of the resonance peak to obtain the feature set to be analyzed of the resonance peak;

[0111] Calculating the matching degree of the feature set to be analyzed and the reference feature set corresponding to the typical fault mode in the fault feature database;

[0112] According to the matching degree calculation result, the candidate fault mode with the highest matching degree is screened out, wherein the matching degree is calculated as follows:

[0113] ;

[0114] In the formula, is the matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is an adaptive weight coefficient determined by the feature saliency index of the reference feature set at the key resonance frequency point, is the initial matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is the normalized value of the relative error of impedance amplitude of the feature set to be analyzed and the reference feature set at the preset key resonance frequency point;

[0115] Based on the fault location calculation rule corresponding to the candidate fault mode and the resonance frequency point in the feature set to be analyzed, the fault location of the target cable is determined, and the candidate fault mode with the highest matching degree is determined as the fault type.

[0116] In the embodiment of the present application, the final fault position of the target cable is determined based on the resonance frequency points in the feature set to be analyzed and the fault position calculation rule corresponding to the candidate fault mode, and the method comprises the following steps:

[0117] The fault position calculation rule corresponding to the candidate fault mode is called from the pre-stored rule library.

[0118] The resonance frequency points in the feature set to be analyzed are sorted according to the frequency values to obtain a resonance frequency point sequence of the feature set to be analyzed.

[0119] The fault position of the resonance frequency point sequence is derived according to the mapping relationship of the fault position calculation rule.

[0120] Specifically, the impedance spectrum contains the impedance amplitude values corresponding to each frequency point, the preset threshold is a pre-set impedance amplitude standard for judging the resonance peak, the impedance amplitude values in the impedance spectrum are checked frequency by frequency, each impedance amplitude value is compared with the preset threshold, when the impedance amplitude value of a certain frequency point exceeds the preset threshold, the frequency value of the frequency point is the frequency value corresponding to the resonance peak, and the impedance amplitude value corresponding to the frequency point is recorded at the same time, all recorded resonance peak frequency values and corresponding impedance amplitude values are arranged into an ordered data set, and the data set is the feature set to be analyzed of the resonance peak.

[0121] Further, a plurality of typical fault modes are pre-stored in the fault feature database, and each typical fault mode corresponds to a unique reference feature set, the reference feature set contains the standard frequency value and the corresponding standard impedance amplitude value of the resonance peak under the fault mode, the feature set to be analyzed is taken out, the frequency value and the impedance amplitude value of each resonance peak in the feature set to be analyzed are compared with the standard frequency value and the standard impedance amplitude value at the corresponding position in the reference feature set of a certain typical fault mode in sequence, the difference degree of each comparison data is recorded, all difference degrees are integrated to obtain the matching degree of the typical fault mode and the feature set to be analyzed, and the process is repeated until the matching degree calculation of the feature set to be analyzed and the reference feature set of all typical fault modes in the fault feature database is completed.

[0122] Further, all the calculated matching degree values are compared in size to find the maximum matching degree, and the typical fault mode corresponding to the maximum matching degree is determined, and the typical fault mode is the candidate fault mode with the highest matching degree.

[0123] Further, the corresponding fault location calculation rule of the candidate fault mode with the highest matching degree is called, which explicitly indicates the association manner between the resonance frequency point and the fault location. The resonance frequency point recorded in the feature set to be analyzed is substituted into the calculation rule, and the fault location of the target cable is determined according to the association manner required by the rule. Meanwhile, the candidate fault mode with the highest matching degree is directly determined as the fault type of the target cable.

[0124] Specifically, the matching degree is the matching degree value between the feature set to be analyzed and the typical fault mode in the fault feature database, which is calculated by the formula and used to represent the overall matching between the two.

[0125] Further, the adaptive weight coefficient is determined according to the feature significance index of the reference feature set at the key resonance frequency point. The feature significance index is obtained by analyzing the importance of the key resonance frequency point in distinguishing different fault modes. The higher the importance of the key resonance frequency point, the higher the feature significance index corresponding to it. The greater the value of determined by this method, and vice versa. The value of ranges from 0 to 1.

[0126] Further, the initial matching degree is obtained by comparing the feature set to be analyzed with the reference feature set of the typical fault mode. When comparing, the overall similarity of the frequency value and the impedance amplitude value of all resonance frequency points is considered. The higher the similarity, the greater the value of

[0127] Further, the normalized value is obtained by first calculating the relative error of the impedance amplitude of the feature set to be analyzed and the reference feature set at the preset key resonance frequency point. The relative error is the difference between the impedance amplitude value of the feature set to be analyzed and the impedance amplitude value of the reference feature set divided by the impedance amplitude value of the reference feature set. Then, the relative errors of all key resonance frequency points are normalized to make the result between 0 and 1. The obtained value is The smaller the relative error, the greater the value of

[0128] Further, the formula is used to obtain the final matching degree by combining the initial matching degree and the normalized value of the impedance amplitude relative error at the key resonance frequency point. The specific calculation process is to multiply the adaptive weight coefficient by the initial matching degree , then subtract the difference of from 1 and multiply it by the normalized value . Add the two product results to obtain the sum, which is the final matching degree​ This value reflects the matching of the overall characteristics and the characteristic matching of the key frequency points at the same time, making the matching degree calculation more comprehensive.

[0129] Specifically, the pre-stored rule library stores the association data of various candidate fault modes and corresponding fault position calculation rules. Each candidate fault mode entry is explicitly labeled with a dedicated fault position calculation rule. The matching entry is found in the rule library that matches the determined candidate fault mode. The extracted calculation rule content is the fault position calculation rule corresponding to the candidate fault mode.

[0130] Further, the specific values of the plurality of resonance frequency points in the to-be-analyzed feature set are compared in size one by one. The resonance frequency point with the smallest value is first found as the first element of the sequence, and the next smallest value is found from the remaining values as the second element. In this way, the resonance frequency points are arranged in order until all resonance frequency points are arranged in order from low to high, forming an ordered frequency point set, which is the resonance frequency point sequence of the to-be-analyzed feature set.

[0131] Further, the fault position calculation rule explicitly defines the mapping relationship between the resonance frequency point and the fault position. This relationship specifies the specific fault position range or specific position value corresponding to different frequency values. Each frequency point in the resonance frequency point sequence is substituted into the mapping relationship, and the corresponding fault position information is found in the mapping relationship according to the value of each frequency point. The fault position information corresponding to all frequency points is comprehensively arranged, and the position result obtained after the arrangement is the fault position corresponding to the resonance frequency point sequence.

[0132] In summary, identifying the resonance peak with an amplitude exceeding the preset threshold from the impedance spectrum and recording its frequency and impedance amplitude value can accurately filter out effective features related to the fault, eliminate irrelevant spectral interference, and ensure that the to-be-analyzed feature set can truly reflect the electrical characteristics of the fault region, laying a high-quality data foundation for subsequent comparison.

[0133] In summary, the matching degree formula with adaptive weight is used in comparison, and the adaptive weight is determined by the significance of the key resonance frequency points in the reference feature set. This can highlight the influence of key features on the matching result, reduce the interference of non-key factors, and significantly improve the matching accuracy of the to-be-analyzed feature set and the typical fault mode, ensuring that the selected candidate fault mode is highly consistent with the actual fault state.

[0134] Overall, according to the position calculation rule corresponding to the candidate fault mode, the fault position is derived combined with the sorted resonance frequency points, so that the position calculation is accurately adapted to the fault type characteristics, and the deviation of the general calculation rule is avoided. Finally, the fault type and position are accurately obtained, which provides the core basis for generating a diagnostic report, directly ensures the pertinence of fault repair, and significantly improves the reliability and practicality of the diagnostic result.

[0135] S6: generating a diagnostic report of the target cable according to the fault type and the fault position.

[0136] In the embodiment of the present application, the basic information of the target cable is first collected, including the model, specification, installation time, and the previously determined fault type and determined fault position are called, and these information is arranged into an information list to ensure that each information is accurately corresponding to the target cable. The fixed structure of the diagnostic report is determined, including the cable basic information module, the fault type description module, the fault position description module, and the detection process description module, each module has a clear content direction, such as the model and specification of the cable basic information module, and the fault type description module describes the fault type and typical performance.

[0137] Further, the contents are filled according to the report structure, the target cable model, specification, and installation time arranged are filled in the cable basic information module; the fault type name and typical characteristics of the fault are filled in the fault type description module; the specific description of the fault position is filled in the fault position description module, including the distance from the measuring point and the cable section where it is located; the key detection steps from applying the excitation signal to determining the fault type and position are described in the detection process description module, to ensure that the content is consistent with the actual detection result of the target cable. The filled report content is checked to check whether the cable basic information is consistent with the actual situation of the target cable, whether the fault type and fault position description are consistent with the previous determination result, and whether the detection process description covers the key steps and is error-free, if there is inconsistency or error, it is immediately corrected until all the contents are accurate.

[0138] Further, the report checked without error is typeset according to the preset format, a uniform font, paragraph spacing, and title style are set, and after the typesetting is completed, it is output by printing or exporting an electronic document, and the output document is the diagnostic report of the target cable.

[0139] Overall, the diagnostic report is generated based on the core data of the accurately determined fault type and fault position, which can systematically and systematically integrate the key results of the previous multi-link diagnosis (such as the fault type obtained by impedance spectrum comparison and the fault position derived by resonance frequency), avoid the understanding obstacles caused by scattered fault information, and enable relevant personnel to intuitively and quickly obtain the core information of the target cable fault, without the need for additional complex diagnosis process and data, which greatly reduces the information extraction cost.

[0140] In general, the report content is based on the accurate diagnosis results of the whole-process verification, ensuring the authenticity and reliability of the information, which can be directly used as the core guidance basis for fault repair, clearly defining the specific direction and target for repair work, avoiding repair deviation or blind troubleshooting caused by ambiguous information, and improving the efficiency of fault repair. In addition, the complete diagnosis report can also be used as a cable fault archive, providing data support for subsequent similar fault analysis and cable long-term operation strategy formulation, further extending the practical value of the diagnosis results, and helping the standardization of cable operation and maintenance management.

[0141] Embodiment Two

[0142] As shown in the figure, the embodiment also provides a functional module diagram of a cable fault diagnosis system. Figure 2

[0143] The cable fault diagnosis system 100 described in the embodiment can be installed in a terminal. According to the functions to be implemented, the cable fault diagnosis system 100 can include a wideband excitation module 101, a fault template matching module 102, a fault area determination module 103, a narrowband excitation module 104, an impedance spectrum comparison module 105, and a diagnosis report generation module 106. The modules described in the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by a terminal processor and can complete a fixed function, which are stored in the memory of the terminal.

[0144] In the embodiment, the functions of each module / unit are as follows:

[0145] The wideband excitation module 101 is configured to apply a wideband excitation signal to a target cable to excite the target cable to generate a first response signal.

[0146] The fault template matching module 102 is configured to extract multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and match a fault template of a preset cable reference model according to an energy distribution feature in the multi-dimensional time-frequency features.

[0147] The fault area determination module 103 is configured to determine a fault area in the corresponding fault template of the target cable according to the propagation time and propagation speed of the first response signal.

[0148] The narrowband excitation module 104 is configured to apply a narrowband excitation signal to the fault area of the target cable to obtain an impedance spectrum of the target cable.

[0149] The impedance spectrum comparison module 105 is configured to compare the resonance frequency point and the corresponding impedance amplitude value of the impedance spectrum with a typical fault mode in a fault feature database to obtain a fault type and a fault location of the target cable.

[0150] ​The diagnostic report generation module 106 is configured to generate a diagnostic report of the target cable according to the fault type and the fault location.

[0151] In detail, each module in the cable fault diagnosis system 100 in the embodiment of the present application adopts the same technical means as the cable fault diagnosis method in Embodiment One and Embodiment Two when in use, and can produce the same technical effects, which will not be described here again.

[0152] Embodiment Three

[0153] As shown in Figure 3 The present embodiment further provides a computer terminal, which can include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and can further include a computer program stored in the memory 11 and executable on the processor 10, such as a cable fault diagnosis program.

[0154] In some embodiments, the processor 10 can be composed of integrated circuits, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more combinations of central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the terminal, which connects all components of the terminal through various interfaces and lines, executes programs or modules stored in the memory 11 (for example, executes a cable fault diagnosis program), and calls data stored in the memory 11 to perform various functions of the terminal and process data.

[0155] The memory 11 includes at least one type of medium, including flash memory, mobile hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the terminal, for example, a mobile hard disk of the terminal. In other embodiments, the memory 11 can also be an external storage device of the terminal, for example, a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 11 can include both an internal storage unit and an external storage device of the terminal. The memory 11 can be used not only to store application software and various data installed on the terminal, such as the code of a cable fault diagnosis program, but also to temporarily store data that has been output or will be output.

[0156] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize the connection communication between the memory 11, the at least one processor 10, etc.

[0157] The communication interface 13 is configured to realize the communication between the terminal and other terminals, and includes a network interface and a user interface. Optionally, the network interface can include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is usually configured to establish a communication connection between the terminal and other terminals. The user interface can be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface can also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch screen, etc. The display can also be appropriately referred to as a display screen or a display unit, and is configured to display the information processed in the terminal and to display a visualized user interface.

[0158] Only the terminal with components is shown in the figure, and those skilled in the art can understand that the structure shown in the figure does not constitute a limitation on the terminal, and the terminal can include fewer or more components than those shown in the figure, or some components can be combined, or different component arrangements can be used.

[0159] For example, although not shown, the terminal can further include a power supply (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, so as to realize functions such as charge management, discharge management, and power consumption management through the power management system. The power supply can also include one or more direct current or alternating current power supplies, a recharging system, a power supply fault detection circuit, a power supply converter or inverter, a power supply status indicator, etc. The terminal can also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which are not described here again.

[0160] It should be understood that the embodiments are only for illustration, and the scope of the patent application is not limited by the structure.

[0161] The cable fault diagnosis program stored in the memory 11 in the terminal is a combination of a plurality of instructions, which, when executed in the processor 10, can achieve:

[0162] S1: a broadband excitation signal is applied to a target cable to excite the target cable to generate a first response signal;

[0163] S2: multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising are extracted, and a fault template of a preset cable reference model is matched according to an energy distribution feature in the multi-dimensional time-frequency features;

[0164] S3: a fault region in the corresponding fault template of the target cable is determined according to a propagation time and a propagation speed of the first response signal;

[0165] S4: a narrowband excitation signal is applied to the fault region of the target cable to obtain an impedance spectrum of the target cable;

[0166] S5: a fault type and a fault position of the target cable are obtained by comparing a resonance frequency point and a corresponding impedance amplitude value of the impedance spectrum with a typical fault mode in a fault feature database;

[0167] S6: a diagnosis report of the target cable is generated according to the fault type and the fault position.

[0168] Specifically, the specific implementation method of the processor 10 on the above instructions can refer to the description of the related steps in the corresponding embodiment of the accompanying drawings, which will not be described here.

[0169] Further, the modules / units integrated in the terminal, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium can include any entity or system that can carry the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0170] In several embodiments provided in the present application, it should be understood that the disclosed terminal, system and method can be implemented in other ways. For example, the above-described system embodiments are only schematic, for example, the division of the modules is only a logical function division, and there can be another division manner in actual implementation.

[0171] The modules described as separate components may or may not be physically separate, and the components displayed as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0172] In addition, each functional module in various embodiments of the application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0173] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0174] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (Artificial Intelligence, AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, obtain knowledge and use knowledge to obtain the best results. Theory, method, technology and application system.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method of diagnosing a cable fault, characterized by, The method comprises: S1: a broadband excitation signal is applied to a target cable to excite the target cable to generate a first response signal; S2: multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising are extracted, and energy distribution features in the multi-dimensional time-frequency features are matched with a fault template of a preset cable reference model, comprising: decomposing the first response signal after adaptive wavelet threshold denoising into intrinsic modal components representing different oscillation modes; combining skewness values, pulse factors and energy proportions of the intrinsic modal components to obtain multi-dimensional time-frequency features of the target cable; extracting the energy distribution features from the multi-dimensional time-frequency features; comparing the extracted energy distribution features with reference energy distributions in the preset cable reference model under multiple typical fault states one by one, and calculating a shape similarity of the target cable, wherein a calculation formula of the shape similarity is as follows: ； wherein, is the shape similarity, is the adaptive weight of the band allocation, is the energy value of the vectorized energy distribution feature on the band, is the energy value of the vectorized reference feature on the band, is the total number of preset bands, is the ordinal number of the preset band. selecting a fault state corresponding to a reference energy distribution with the highest shape similarity as a fault template of the target cable; S3: determining a fault region in the fault template corresponding to the target cable according to a propagation time and a propagation speed of the first response signal; S4: applying a narrowband excitation signal to the fault region of the target cable to obtain an impedance spectrum of the target cable; S5: comparing resonance frequency points and corresponding impedance amplitude values of the impedance spectrum with typical fault modes in a fault feature database to obtain a fault type and a fault position of the target cable; S6: generating a diagnosis report of the target cable according to the fault type and the fault position.

2. A method of diagnosing a cable fault as claimed in claim 1, characterized in that, The broadband excitation signal applied to the target cable to excite the target cable to generate the first response signal comprises: selecting an initial frequency band range and a signal form corresponding to the target cable from a preset excitation parameter library; generating an initial broadband excitation signal of the target cable according to the initial frequency band range and the signal form; applying the initial broadband excitation signal to a start end of the target cable, and collecting an initial response of the target cable; adaptively adjusting an amplitude value of the initial broadband excitation signal according to a signal quality of the initial response to obtain the broadband excitation signal of the target cable.

3. A method of diagnosing a cable fault as claimed in claim 1, wherein, The determination of the fault region in the fault template corresponding to the target cable according to the propagation time and the propagation speed of the first response signal comprises: identifying a significant distortion point caused by a fault point reflection from the first response signal, and taking an offset of the distortion point relative to a time when the broadband excitation signal is emitted as the propagation time of the first response signal; querying a preset propagation speed parameter table according to a cable type indicated by the fault template to obtain a signal propagation speed corresponding to the cable type as the propagation time of the first response signal; taking a product of the propagation time and the propagation speed as an approximate distance between a fault point and a measurement point in the target cable; The approximate distance is compared with a region distance boundary predefined in the fault template to determine a specific section of the fault point, and a specific section is output as a fault region of the target cable.

4. A method of diagnosing a fault in a cable as claimed in claim 3, wherein, The resonance frequency point and the corresponding impedance amplitude value based on the impedance spectrum are compared with typical fault modes in a fault feature database to obtain a fault type and a fault position of the target cable, including: A resonance peak with an amplitude exceeding a preset threshold is identified from the impedance spectrum, and a frequency value and a corresponding impedance amplitude value of the resonance peak are recorded to obtain a feature set to be analyzed of the resonance peak; The feature set to be analyzed is matched with a reference feature set corresponding to a typical fault mode in the fault feature database to calculate a matching degree; According to the matching degree calculation result, a candidate fault mode with the highest matching degree is screened out, and the matching degree is calculated as follows: ； In the formula, is the initial matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is the adaptive weight coefficient determined by the feature saliency index of the reference feature set at the key resonance frequency point, is the initial matching degree of the feature set to be analyzed and the typical fault mode in the fault feature database, is the normalized value of the relative error of the impedance amplitude of the feature set to be analyzed and the reference feature set at the preset key resonance frequency point. Based on the fault position calculation rule corresponding to the candidate fault mode and the resonance frequency point in the feature set to be analyzed, the fault position of the target cable is determined, and the candidate fault mode with the highest matching degree is determined as the fault type.

5. A method of diagnosing a fault in a cable as claimed in claim 4, wherein, Based on the fault position calculation rule corresponding to the candidate fault mode and the resonance frequency point in the feature set to be analyzed, the final fault position of the target cable is determined, including: The fault position calculation rule corresponding to the candidate fault mode is called from a pre-stored rule library; The resonance frequency points in the feature set to be analyzed are sorted according to the frequency values to obtain a resonance frequency point sequence of the feature set to be analyzed; According to the mapping relationship of the fault position calculation rule, the fault position of the resonance frequency point sequence is derived.

6. A cable fault diagnostic system characterized by comprising: It includes: A wideband excitation module for applying a wideband excitation signal to a target cable to excite the target cable to generate a first response signal; A fault template matching module for extracting a multi-dimensional time-frequency feature of the first response signal after adaptive wavelet threshold denoising, and matching a fault template of a preset cable reference model according to an energy distribution feature in the multi-dimensional time-frequency feature, including: Decomposing the first response signal after adaptive wavelet threshold denoising into intrinsic mode components representing different oscillation modes; Combining skewness values, pulse factors and energy proportions of the intrinsic mode components to obtain a multi-dimensional time-frequency feature of the target cable; Extracting the energy distribution feature from the multi-dimensional time-frequency feature; Comparing the extracted energy distribution feature with reference energy distributions in a plurality of typical fault states pre-stored in the preset cable reference model one by one to calculate a shape similarity of the target cable, wherein the shape similarity is calculated according to the following formula: ； wherein, is the shape similarity, is the adaptive weight for the band allocation, is the energy value of the vectorized energy distribution feature on the band, is the energy value of the vectorized reference feature on the band, is the total number of preset bands, is the ordinal number of the preset band. Selecting a fault state corresponding to a reference energy distribution with the highest shape similarity as a fault template of the target cable; A fault region determination module for determining a fault region in the corresponding fault template of the target cable according to a propagation time and a propagation speed of the first response signal; A narrowband excitation module for applying a narrowband excitation signal to the fault region of the target cable to obtain an impedance spectrum of the target cable; An impedance spectrum comparison module is configured to compare the resonance frequency point and the corresponding impedance amplitude value of the impedance spectrum with typical fault modes in a fault feature database, to obtain a fault type and a fault position of the target cable. A diagnostic report generation module is configured to generate a diagnostic report of the target cable according to the fault type and the fault position.

7. A terminal, characterized by comprising: Steps of implementing the method of any one of claims 1 to 5.

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