Multi-core cable fault test system and test method

Abstract

The invention relates to the technical field of cable fault testing, in particular to a multi-core cable fault testing system and method, and the method comprises the steps: determining a fault generation coefficient of a target core wire testing current signal based on the difference between periods where all adjacent signal wave crests in the target core wire testing current signal are located and a periodic parameter; determining the composition richness of the target core wire test current signal based on the similarity of related data sets between the target core wire and all other core wires under different voltage conditions and the signal amplitude distribution near any maximum value point in the target core wire test current signal; and performing forward fusion on the composition richness and the fault occurrence coefficient, adjusting a preset initial decomposition interval scale, screening part of component signals for reconstruction, and performing fault detection on the reconstructed signals. According to the invention, accurate analysis of the test current signal is realized, and the accuracy of fault diagnosis is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cable fault testing, and in particular to a multi-core cable fault testing system and testing method. Background Art

[0002] With the advancement of modern industry and informatization, cables have become a key component in fields such as communications, energy, transportation, and automation, widely used to transmit power, signals, and data. Due to long-term use, cables may experience faults such as breakage, wear, and poor contact, affecting system safety and stability.

[0003] Existing multi-core cable fault testing methods typically rely on real-time current data to analyze whether abnormal impedance or interference exists within each core to identify multi-core cable faults. However, due to the complex structure of multi-core cables, each cable contains multiple cores, and signal crosstalk between different cores may occur. This can lead to a deviation in the relationship between the test current of the multi-core cable and the actual cable fault, thus affecting the accuracy of fault diagnosis. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a multi-core cable fault testing system and testing method, the technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a multi-core cable fault detection method, the method comprising the following steps: Obtain the test current signal of each core wire in the multi-core cable; Based on the target core wire test current signal and the difference between it and all other core wire test current signals, the adaptive decomposition interval scale of the target core wire test current signal when using robust local mean decomposition for signal decomposition is determined, specifically: A1, determining a fault occurrence coefficient of the target core wire test current signal based on the difference between the periods of all adjacent signal peaks in the target core wire test current signal and the periodicity parameter; A2, obtaining the test current signals of all other cores under the same voltage condition as the target core, recording them as the relevant data set of the target core; determining the signal distortion factor of the test current signal of the target core based on the similarity of the relevant data sets between the target core and all other cores under different voltage conditions; A3, based on the probability of occurrence of the signal amplitude of the maximum points near any maximum point in the target core wire test current signal, the maximum slope value near any maximum point, and the signal amplitude difference and time interval difference between any maximum point and its adjacent minimum point on the right, determine the high-frequency noise factor of any maximum point; A4, combining the average level of the high-frequency noise coefficient and the signal distortion factor of all maximum points to determine the composition richness of the target core wire test current signal; A5, forwardly fuses the component richness and the fault occurrence coefficient to determine the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal; uses the scale adjustment coefficient to adjust the preset initial decomposition interval scale to determine the adaptive decomposition interval scale; After signal decomposition, some component signals are screened for reconstruction, and fault detection is performed on the reconstructed signals.

[0005] Preferably, the fault occurrence coefficient is determined by a result of reverse fusion of the difference between the periods of all adjacent signal peaks and the periodic parameter.

[0006] Preferably, the signal distortion factor is determined by an average level of similarity of relevant data sets between the target core wire and all other core wires under all voltage conditions.

[0007] Preferably, the method for determining the high-frequency noise coefficient is: The noise factor of any maximum point is determined by combining the probability of occurrence of the signal amplitudes of the maximum points near any maximum point and the maximum value of the slope near any maximum point; Calculating an average level of the product of the signal amplitude difference and the time interval difference at all maximum value points; The average level of the multiplication result and the noise coefficient are forward fused to obtain the high-frequency noise coefficient.

[0008] Preferably, the noise coefficient is determined by reverse fusion of the probability of occurrence of the slope maximum and the signal amplitudes of all the maximum points.

[0009] Preferably, the slope maximum is further determined as the maximum absolute value of the slopes of all signals near any maximum point.

[0010] Preferably, the composition richness is determined by the product of the average level of the high-frequency noise coefficients of all maximum points and the signal distortion factor.

[0011] Preferably, the adaptive decomposition interval scale of the target core wire test current signal is recorded as , Where, represents the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal, and Q represents the preset initial decomposition interval scale of the target core wire test current signal.

[0012] Preferably, the method for screening some component signals is: calculating the mean signal amplitudes of all component signals and sorting them in ascending order, and the first preset number of component signals are the screened some component signals.

[0013] In a second aspect, an embodiment of the present application also provides a multi-core cable fault testing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the multi-core cable fault testing methods described above are implemented.

[0014] This application has at least the following beneficial effects: The present application aims to solve the problem in existing multi-core cable fault testing methods that, due to the complex structure of the multi-core cable and the possible signal crosstalk between different cores, the relationship between the test current and voltage data and the cable fault is deviated, affecting the accuracy of fault diagnosis. To this end, the present application proposes a fault diagnosis method based on real-time test current signals: first, by analyzing the difference between the periods of adjacent signal peaks in the test current signal of each core and the periodic parameters, the fault occurrence coefficient of each core is extracted to measure the periodic characteristic changes in the core test current signal after the fault occurs; then, the distortion of the test current signal of each core in the multi-core cable is further analyzed, and the type of noise caused by the fault is considered, especially the high-frequency noise that is usually accompanied by strong electromagnetic interference. This can not only quantitatively analyze the degree of mutual influence between the cores, but also more accurately determine the location of the high-frequency noise, thereby facilitating cable fault location; finally, combining the fault occurrence coefficient and the composition richness, the adaptive decomposition strength of the core test current signal is extracted, thereby achieving accurate analysis of the test current signal and improving the accuracy of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A flowchart of a multi-core cable fault testing method provided in an embodiment of the present application; Figure 2 A flow chart of a method for determining an adaptive decomposition interval scale provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a multi-core cable fault testing system and testing method proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of a multi-core cable fault testing system and testing method provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] An embodiment of the present application provides a multi-core cable fault testing system and testing method.

[0021] Specifically, a multi-core cable fault test method is provided as follows. Figure 1 , the method comprises the following steps: The first step is to obtain the test current signal of each core wire in the multi-core cable.

[0022] In this embodiment, an independent power supply is provided to each core of a multi-core cable, allowing the voltage of each core to be adjusted individually. Appropriate connectors and joints are selected to connect the output of the independent power supply to the starting end of the core to be tested. A stable DC voltage is supplied to the test core via the cable, thereby measuring the current signal and ensuring a stable and reliable connection. The fixed voltage condition used when testing the current signal is selected from the most commonly used operating voltage conditions in a specific project for analysis and is set by the implementer.

[0023] Next, current sensors are installed in the cable of each core wire and on the surface of the multi-core cable to ensure that each sensor can accurately measure the current signal of the corresponding core wire; In this embodiment, the sampling frequency of the sensor is set to 5kHz to achieve real-time acquisition. To ensure the quality of the acquired signal, the signal is initially denoised through a bandpass filter after acquisition, thereby improving the accuracy of subsequent analysis results.

[0024] At this point, the test current signal of each core wire in the multi-core cable can be obtained through the above method.

[0025] In the second step, based on the target core wire test current signal and the difference between the target core wire test current signal and all other core wire test current signals, the adaptive decomposition interval scale of the target core wire test current signal when using robust local mean decomposition for signal decomposition is determined.

[0026] During normal transmission in a multi-core cable, each core operates normally, current flows steadily along a predetermined path, and the current signal exhibits a regular and stable fluctuation pattern. However, if one or more cores within the cable fail, current transmission is disrupted, causing the signal to become unstable, manifesting as irregular or sudden abnormal fluctuation patterns.

[0027] Accordingly, this application determines the adaptive decomposition interval scale for the target core test current signal when using robust local mean decomposition (RLMD) to decompose the target core test current signal based on the differences between the target core test current signal and the test current signals of all other cores. This is used to decompose the target core test current signal using robust local mean decomposition (RLMD) and extract features for fault detection. This avoids the problem of deviations in the relationship between test current and voltage data and cable faults caused by the complex structure of multi-core cables and potential signal crosstalk between different cores, thereby improving the accuracy of fault detection. Robust local mean decomposition is a well-known technology and will not be described in detail here.

[0028] In this embodiment, the flow chart of the method for determining the scale of the adaptive decomposition interval is shown in the attached figure. Figure 2 As shown, specifically: A1, based on the difference between the periods of all adjacent signal peaks in the target core wire test current signal and the periodicity parameter, determines the fault occurrence coefficient of the target core wire test current signal.

[0029] In this embodiment, the i-th core line is used as the target core line for subsequent analysis.

[0030] First, the periodic parameter T of the target core wire test current signal is obtained. This embodiment uses an autocorrelation function to extract the periodic parameter of the target core wire test current signal. A larger value of T indicates a stronger periodicity of the test current signal, i.e., a more regular signal fluctuation pattern. The autocorrelation function is well known and will not be described in detail. In other embodiments of the present application, a Fourier transform may also be used to extract the periodic parameter.

[0031] In addition, due to factors such as poor core wire contact, local resistance will increase, which will cause the fluctuation pattern of the test current signal to have a certain periodicity, but its signal waveform will be distorted. This situation still means that there may be a core wire fault.

[0032] Accordingly, this embodiment utilizes the signal peak detection technology to obtain all signal peaks in the test current signal; wherein, the signal peak detection technology is a well-known technology and will not be described in detail.

[0033] Preferably, in this embodiment, the difference between the periods of all adjacent signal peaks is reversely fused with the periodic parameter to obtain the fault occurrence coefficient of the target core wire test current signal. Wherein, the period of a signal peak is the time from the signal peak to the next signal peak.

[0034] It can be understood that reverse fusion is a fusion method such as subtraction and division between data. The specific reverse fusion method is determined by the implementer based on the actual situation and is not specifically limited in this application.

[0035] As an implementation method, this embodiment uses dynamic time warping (DTW) to perform DTW matching on the signal amplitudes of any two adjacent signal peaks in the test current signal at the same time sequence, obtain the distance set between all matching points, and calculate the variance of the distance set. , is regarded as the peak distortion coefficient; the larger the value, the greater the waveform difference between the periods of two adjacent signal peaks, that is, the signal waveform is distorted. Among them, dynamic time warping is a well-known technology and will not be described in detail. Using the periodic parameter T and the variance Constructing a specific core test current fault occurrence coefficient The formula is: Where, Represents the fault occurrence coefficient of the target core wire test current signal, Represents the periodic parameters of the target core wire test current signal, Represents the number of all signal peaks in the target core wire test current signal. Representative The signal peak and The peak distortion coefficient between the peaks of the signal.

[0036] In multi-core cable fault testing, circuit faults in the core wires (such as open circuits, short circuits, etc.) can cause abnormal current fluctuations, manifesting as irregular fluctuation patterns. Local resistance changes caused by poor contact usually do not change the periodicity of the current, but will increase the resistance of the current path, thereby causing distortion of the current signal waveform.

[0037] When the core wire test current signal has a certain periodicity, it is still necessary to further analyze the degree of distortion between the signal waveforms to measure the current fault occurrence coefficient of the core wire.

[0038] Due to the complex internal structure of multi-core cables, which usually contain multiple staggered cores, fault testing based on test current signals may cause serious signal crosstalk between the cores, thereby affecting the overall current performance of the cable during the test. Therefore, the fault occurrence coefficient obtained based on the test current signal may not accurately reflect the failure probability of the cable.

[0039] A2, obtain the test current signals of all other core wires under the same voltage conditions as the target core wire, and record them as the relevant data set of the target core wire; based on the similarity of the relevant data sets between the target core wire and all other core wires under different voltage conditions, determine the signal distortion factor of the test current signal of the target core wire.

[0040] In a multi-core cable, even if each core wire operates under the same voltage conditions, due to the distance between the core wires and the influence of signal frequency, the faulty core wire will produce electrical anomalies, causing different signal frequencies, and the electromagnetic interference and mutual influence between the internal core wires are not completely consistent.

[0041] Accordingly, this application assigns different voltage conditions to the target core wire. In this embodiment, Q different voltage conditions are set, with Q being 10, to obtain the test current signal of the target core wire under these different voltage conditions. In addition, the same voltage condition is assigned to all other core wires in the multi-core cable, and the corresponding test current signals are obtained. The specific Q different voltage conditions are set by the implementer.

[0042] In this embodiment, the test current signals of all other core wires under the same voltage condition as the target core wire are obtained, and the test current signals of all other core wires under the corresponding voltage condition are combined into a relevant data set of the target core wire.

[0043] Furthermore, under the same voltage condition, the similarity between the relevant data set of the target core wire and the relevant data set of any other core wire is obtained. The larger the value, the stronger the electromagnetic interference between the target core wire combination.

[0044] Preferably, in this embodiment, the signal distortion factor is determined by an average level of similarity of relevant data sets between the target core wire and all other core wires under all voltage conditions.

[0045] As an implementation method, the formula for constructing the signal distortion factor R of the specific target core wire test current signal is: Where R represents the signal distortion factor of the target core test current signal, Q represents the number of different voltage conditions, Represents the number of cores in a multi-core cable excluding the target core. Represents the relevant data set of the target core under the qth voltage condition, represents the relevant data set of the nth core under the qth voltage condition, Represents a collection of related data and The Pearson correlation coefficient between them. The Pearson correlation coefficient is a well-known technology and will not be described in detail.

[0046] It should be understood that electromagnetic interference and signal distortion are typically caused by the coupling of electric and magnetic fields between different cores. In multi-core cables, voltage variations lead to current fluctuations, which propagate to adjacent cores and cause signal distortion. Therefore, by calculating the similarity between the data sets related to the target core and other cores, the degree of mutual influence between them can be quantitatively analyzed.

[0047] A3, based on the probability of occurrence of the signal amplitudes of the maximum points near any maximum point in the target core wire test current signal and the maximum slope value near any maximum point, determines the noise factor of any maximum point.

[0048] In actual operation, different types of faults will cause the test current signal in the faulty core wire to generate noise of different frequencies, mainly including low-frequency and high-frequency noise; for example, faults such as broken wires and short circuits will cause sharp fluctuations in current and generate high-frequency noise, while faults such as core wire aging will cause abnormal low-frequency current fluctuations, that is, low-frequency noise.

[0049] It's worth noting that high-frequency noise not only affects the signal quality of the core cable but can also expand the range of electromagnetic interference, causing greater disruption to surrounding equipment and systems. Due to the strong radiation properties of high-frequency noise signals, they travel far, further exacerbating the electromagnetic impact on the environment and affecting equipment operation and measurement accuracy.

[0050] Based on this, the test current signal of the target core under fixed voltage conditions is used as the analysis object. The noise signal caused by the core fault usually appears as a disordered fluctuation pattern in the test current signal. That is, the fault noise signal will show a small time-delayed glitch signal fluctuation pattern at the local signal maximum point.

[0051] This application marks all the maximum and minimum points in the test current signal of the target core wire; starting from any maximum point in the test current signal, a window is constructed. In this embodiment, the window size is set to 10 signal sampling points. The signal points within the window can be used to represent the signal points near the maximum point.

[0052] The probability of the signal amplitude of the maximum point in the window where any maximum point is located appearing in the test current signal of the target core wire is obtained. The smaller the probability of occurrence, the less the target signal sampling point appears in the overall signal, that is, the signal may have abnormal fluctuations; the maximum slope value of all signals in the window where any maximum point is located is obtained. The larger the value, the more severe the signal fluctuation of the target core wire at the maximum point.

[0053] Furthermore, the present application determines the noise coefficient of any maximum point based on the probability of occurrence of the signal amplitude of the maximum point near any maximum point in the target core wire test current signal, and the maximum slope value near any maximum point, which is used to characterize the high-frequency noise intensity at each maximum point, thereby facilitating the identification of high-frequency noise.

[0054] Preferably, the noise coefficient is determined by reverse fusion of the probability of occurrence of the slope maximum and the signal amplitudes of all the maximum points.

[0055] In other embodiments of the present application, the noise factor may be further determined by combining the signal amplitudes of all maximum points near any maximum point.

[0056] As an implementation method, the noise coefficient of the jth maximum point in the test current signal of the specific target core wire is constructed as follows: The formula is: Where, Represents the noise coefficient of the jth maximum point in the test current signal of the target core wire, The maximum value among the absolute values ​​of the slopes of all signals in the window where the jth maximum point in the test current signal of the target core wire is located, Represents the set of all maximum points in the window where the jth maximum point in the test current signal of the target core wire is located, Representative Set The A maximum point, Representative The probability that the signal amplitude of a maximum point appears in the test current signal of the target core wire.

[0057] It should be understood that a core fault area causes current signal instability. After passing through the fault area, the current signal cannot quickly return to normal fluctuations, often manifesting as violent fluctuations and time delay glitches. The increased probability of signal maximum points, more violent fluctuations, and more pronounced time delay glitches indicate that the current signal in this area may be a fault area.

[0058] Furthermore, by analyzing the noise components of the current signal in the fault area, it is found that for high-frequency noise, the larger the electromagnetic influence range, the richer the composition of the regional signal.

[0059] Therefore, the present application calculates the signal amplitude difference between each maximum point in the test current signal of the target core wire and its adjacent minimum point on the right. The larger the value, the more violent the fluctuation around the signal, which usually reflects the presence of high-frequency components; calculates the time interval difference between each maximum point in the test current signal of the target core wire and its adjacent minimum point on the right. The smaller the value, the faster the signal fluctuation around the signal. Combined with the signal amplitude difference, it can effectively measure the high-frequency characteristics of the signal at the target signal point.

[0060] In addition, it is worth noting that if there is no minimum point on the right side of the last maximum point, the last maximum point will not be analyzed.

[0061] Preferably, in this embodiment, the average level of the multiplication results of the signal amplitude difference and the time interval difference of all maximum points is calculated, and the average level of the multiplication results and the noise coefficient are forward fused to obtain the high-frequency noise coefficient.

[0062] It can be understood that forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0063] As an implementation method, the high-frequency noise coefficient of the jth maximum point in the test current signal of the specific target core wire is constructed The formula is: Where, Represents the high-frequency noise factor of the jth maximum point in the test current signal of the target core wire, Represents the noise coefficient of the jth maximum point in the test current signal of the target core wire, Representative The signal amplitude difference between a maximum point and its adjacent minimum point on the right, Representative The time interval between a maximum point and its adjacent minimum point on the right.

[0064] It should be understood that the location where high-frequency noise occurs can be determined more accurately based on the high-frequency noise coefficient, thereby facilitating cable fault location.

[0065] A4, combining the average level of the high-frequency noise coefficients of all maximum points and the signal distortion factor, determines the composition richness of the target core wire test current signal.

[0066] When a core fault generates more high-frequency noise, its electromagnetic impact range will expand. At the same time, due to the decrease in electromagnetic compatibility, the core wire is more susceptible to other electromagnetic interference in the surrounding environment, resulting in increased signal complexity and richness.

[0067] Furthermore, the composition richness of the target core wire test current signal is determined by combining the average level of the high-frequency noise coefficients and the signal distortion factor of all maximum points.

[0068] Preferably, the composition richness is determined by the product of the average level of the high-frequency noise coefficients of all maximum points and the signal distortion factor.

[0069] In other embodiments of the present application, the composition richness may also be determined by other forward fusion methods of the average level of the high-frequency noise coefficients of all maximum points and the signal distortion factor.

[0070] It should be understood that the greater the average level of the high-frequency noise coefficient and the signal distortion factor of all maximum points in the test current signal of the target core wire, the more likely it is that the target core wire has high-frequency noise, and the more likely it is that electromagnetic interference and signal distortion will occur, and the greater the possibility of a fault at the location of the core wire, that is, the richer the composition of the core wire test current signal.

[0071] A5, forwardly fuses the component richness and the fault occurrence coefficient to determine the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal; uses the scale adjustment coefficient to adjust the initial decomposition interval scale to determine the adaptive decomposition interval scale.

[0072] The signal decomposition and denoising scale adjustment coefficient of the target core wire test current signal is obtained according to the composition richness of the target core wire test current signal and the fault occurrence coefficient.

[0073] Preferably, the component richness and the fault occurrence coefficient are forward fused to obtain the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal.

[0074] As an implementation method, the formula for constructing a specific adaptive decomposition interval scale adjustment coefficient is: Where, Represents the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal, is a normalized function, F represents the composition richness of the target core wire test current signal, and W represents the fault occurrence coefficient of the target core wire test current signal.

[0075] In traditional RLMD signal decomposition algorithms, the local mean within a sliding window of each data point in the test current signal is calculated. The initial decomposition interval scale, Q, is obtained based on the change in the local mean within the test current signal. In this embodiment, the sliding window size is set to 20 signal points. The method for calculating the initial decomposition interval scale is well known and will not be further described.

[0076] Preferably, in this embodiment, the formula for constructing a specific adaptive decomposition interval scale is: Where, Represents the adaptive decomposition interval scale of the target core test current signal, represents the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal, and Q represents the preset initial decomposition interval scale of the target core wire test current signal.

[0077] The third step is to screen some component signals for reconstruction after signal decomposition and perform fault detection on the reconstructed signals.

[0078] The RLMD signal decomposition algorithm is used to obtain the original test current signal of the target core wire and generate component signals with various frequency and amplitude characteristics. These components are arranged from high to low in importance, and each component represents the original signal at a specific frequency and amplitude.

[0079] By selecting component signals with smaller signal amplitudes for reconstruction, a stable and reliable real current signal can be obtained.

[0080] Specifically, in this embodiment, the method for screening some components is: calculate the signal amplitude mean of all component signals and sort them from small to large, and select the first three component signals as the screened partial component signals. In other embodiments, the number of screened component signals can be selected according to actual conditions.

[0081] Finally, after the test current signal of each core wire in the multi-core cable is processed as described above, the signal characteristics are analyzed using traditional feature extraction methods (such as amplitude, frequency, phase, etc.).

[0082] At the same time, training is performed using a large amount of historical data, including test current signals under normal and fault conditions. The data source can be actual operating data, simulation data, or experimental data. Before training, the test current signals are usually preprocessed by denoising and normalization.

[0083] A convolutional neural network (CNN) model is used for training. After training, the model can accurately identify fault modes in real-time test current signals and precisely locate the fault location and nature. The CNN model training process is well-known and will not be further described.

[0084] Based on the same inventive concept as the above method, an embodiment of the present application also provides a multi-core cable fault testing system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of a multi-core cable fault testing method described in any one of the above methods are implemented.

[0085] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0086] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article 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 article or device. In the absence of further restrictions, the phrase "including a ..." defines an element, does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0087] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not invented herein.

[0088] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A multi-core cable fault testing method, characterized in that: The method comprises the following steps: Obtaining the test current signal of each core wire in a multi-core cable; Based on the target core wire test current signal and the difference between it and all other core wire test current signals, the adaptive decomposition interval scale of the target core wire test current signal when the robust local mean decomposition is used for signal decomposition is determined, specifically: A1, determining a fault occurrence coefficient of a target core wire test current signal based on the difference between the periods where all adjacent signal peaks in the target core wire test current signal are located and the periodicity parameter; A2, obtaining the test current signals of all other core wires under the same voltage condition as the target core wire, and recording them as the relevant data set of the target core wire; based on the similarity of the relevant data sets between the target core wire and all other core wires under different voltage conditions, determining the signal distortion factor of the test current signal of the target core wire; A3, based on the probability of occurrence of the signal amplitude of the maximum point near any maximum point in the target core wire test current signal, the maximum slope value near any maximum point, and the signal amplitude difference and time interval difference between any maximum point and its adjacent minimum point on the right, determine the high-frequency noise coefficient of any maximum point; A4, combining the average level of the high-frequency noise coefficient of all maximum points and the signal distortion factor, determines the composition richness of the target core wire test current signal; A5, forwardly fuse the composition richness and the fault occurrence coefficient to determine the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal; use the scale adjustment coefficient to adjust the preset initial decomposition interval scale to determine the adaptive decomposition interval scale; After signal decomposition, some component signals are screened for reconstruction, and fault detection is performed on the reconstructed signals.

2. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The fault occurrence coefficient is determined by the result of reverse fusion of the difference between the periods where all adjacent signal peaks are located and the periodicity parameter.

3. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The signal distortion factor is determined by an average level of similarity of the relevant data sets between the target core and all other cores under all voltage conditions.

4. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The method for determining the high frequency noise coefficient is: The noise coefficient of any maximum point is determined by combining the probability of occurrence of the signal amplitudes of the maximum points near any maximum point and the maximum slope value near any maximum point; Calculate the average level of the product of the signal amplitude difference and the time interval difference of all maximum value points; The average level of the multiplication result and the noise coefficient are forward fused to obtain the high-frequency noise coefficient.

5. A multi-core cable fault testing method as claimed in claim 4, characterized in that: The noise coefficient is determined by reverse fusion of the probability of occurrence of the slope maximum and the signal amplitudes of all the maximum value points.

6. A multi-core cable fault testing method as claimed in claim 4, characterized in that: The slope maximum is further determined as the maximum value of the absolute values ​​of the slopes of all signals near any of the maximum value points.

7. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The composition richness is determined by the product of the average level of the high-frequency noise coefficients of all maximum points and the signal distortion factor.

8. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The adaptive decomposition interval scale of the target core wire test current signal is recorded as , ; In the formula, represents the scale adjustment coefficient of the adaptive decomposition interval of the target core wire test current signal, and Q represents the preset initial decomposition interval scale of the target core wire test current signal.

9. A multi-core cable fault testing method as claimed in claim 1, characterized in that: The screening method of the partial component signals is: calculating the signal amplitude mean values ​​of all component signals and sorting them from small to large, and the first preset number of component signals are the screened partial component signals.

10. A multi-core cable fault testing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a multi-core cable fault testing method as described in any one of claims 1-9 are implemented.

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