A method and system for detecting the degree of aging of a cable

Abstract

The application discloses a kind of methods and systems for detecting the aging degree of cable, it is related to cable detection technical field, comprising: determining initial suspected aging space area and determining corresponding initial vibration signal section;Determine phase space reconstruction parameter according to initial vibration signal section, and determine the local divergence feature of trajectory, calculate initial aging sensitive factor;Determine topological correction parameter according to initial aging sensitive factor, and utilize topological correction parameter to carry out reverse correction to electromagnetic signal, obtain corrected electromagnetic signal;Whether the initial suspected aging space area is consistent with initial suspected aging space area is checked using corrected electromagnetic signal, if not consistent, then redetermine updated vibration signal section, and update initial aging sensitive factor;The application realizes the accurate identification and real-time tracking of early aging area of cable by the collaborative analysis and topological correction of electromagnetic and vibration signal, effectively improves the accuracy and reliability of cable aging detection.

Description

Technical Field

[0001] This invention relates to the field of cable testing technology, and specifically to a method and system for detecting the degree of aging of cables. Background Technology

[0002] As a crucial medium for power transmission and communication in power systems and communication networks, the stability of cable operation directly impacts the safety and reliability of power transmission and information communication. Because cables are susceptible to various factors during long-term operation, such as environmental factors, mechanical stress, thermal aging, and partial discharge, they often experience insulation aging, localized damage, and early failures. Therefore, timely and accurate detection and location of cable aging conditions are essential.

[0003] Existing cable aging detection methods generally include electrical characteristic quantity detection and mechanical vibration characteristic detection. Electrical characteristic quantity detection typically utilizes changes in the propagation characteristics of electromagnetic signals in cables, capturing electromagnetic signal anomalies through frequency or time domain analysis to determine the cable's aging state. However, because electromagnetic signals are easily affected by external electromagnetic interference and environmental noise, existing methods often struggle to accurately locate abnormal areas, and the signal misjudgment rate is relatively high. Furthermore, while mechanical vibration characteristic detection possesses some anti-interference capabilities, its sensitivity to subtle changes in the early stages of cable aging is insufficient, making it difficult to detect early aging areas and consequently affecting the accuracy and timeliness of the detection.

[0004] Therefore, how to effectively reduce the risk of misjudgment caused by electromagnetic signal interference while ensuring the sensitivity of early aging detection, and accurately locate the aging area of ​​the cable, has become a technical problem that urgently needs to be solved in the field of cable condition monitoring. Summary of the Invention

[0005] The purpose of this invention is to provide a system and method for detecting the degree of aging of cables, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for detecting the degree of aging of cables, comprising:

[0008] S101: Acquire the electromagnetic and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area.

[0009] S102: Determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor.

[0010] S103: Determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and use the topology correction parameters to perform reverse correction on the electromagnetic signal to obtain the corrected electromagnetic signal.

[0011] S104: Use the corrected electromagnetic signal to check the initial suspected aging space area again. If the checked suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is determined based on the checked suspected aging space area.

[0012] S105: Update the initial aging sensitivity factor based on the updated vibration signal segment.

[0013] Furthermore, determining the initial suspected aging spatial region based on the spectral energy anomaly of the electromagnetic signal includes:

[0014] S101.1.1: Perform variational mode decomposition on the electromagnetic signal to obtain multiple modal components;

[0015] S101.1.2: Perform Hilbert transform on each modal component to obtain the instantaneous amplitude spectrum of each modal component;

[0016] S101.1.3: Calculate the rate of change of energy density of the instantaneous amplitude spectrum window by window along the frequency axis with a preset frequency window width. The calculation formula is as follows:

[0017] ;

[0018] In the formula: For frequency The rate of change of energy density at that location; For frequency The energy density is expressed as the average of the squares of the instantaneous amplitudes within the window; The width of the frequency window is determined based on the signal frequency range and actual resolution requirements.

[0019] S101.1.4: The frequency range in which the rate of change of energy density exceeds a preset threshold within a continuous window is defined as the spectrum energy anomaly range;

[0020] S101.1.5: Bandpass filtering is applied to the abnormal interval, and the initial suspected aging space area on the cable is determined by time-domain reflection method based on the start and end time of the abnormal waveform and the propagation speed of the electromagnetic signal.

[0021] Furthermore, determining the corresponding initial vibration signal segment based on the initial suspected aging space region includes:

[0022] S101.2.1: Determine the corresponding location coordinates based on the initial suspected aging space area's position on the cable;

[0023] S101.2.2: Calculate the vibration signal propagation delay based on the vibration signal propagation speed and the position coordinates;

[0024] S101.2.3: Determine the start and end times of the initial vibration signal segment based on the propagation delay, and extract the initial vibration signal segment.

[0025] Furthermore, determining the phase space reconstruction parameters includes:

[0026] S102.1.1: Determine the optimal embedding dimension using the pseudo nearest neighbor method;

[0027] S102.1.2: Determining the optimal delay time using the autocorrelation function:

[0028] S102.1.3: Determine the optimal embedding dimension and optimal delay time as phase space reconstruction parameters.

[0029] Furthermore, determining the local divergence characteristics of the trajectory includes:

[0030] S102.2.1: Reconstruct the trajectory of the initial vibration signal segment using phase space reconstruction parameters;

[0031] S102.2.2: Determine the nearest neighbor of each trajectory point;

[0032] S102.2.3: Calculate the change in distance between the trajectory point and its nearest neighbor over time to determine the local divergence index of the trajectory:

[0033] ;

[0034] In the formula: The local divergence index of the trajectory; is the distance between a trajectory point and its nearest neighbor at time t; N is the total number of trajectory points; For time step;

[0035] S102.2.4: Determine the initial aging sensitivity factor based on the local divergence index.

[0036] Furthermore, methods for determining the nearest neighbor in a trajectory include:

[0037] S102.2.2.1: Calculate the energy envelope variance characteristics within the local window of the vibration signal corresponding to each trajectory point;

[0038] S102.2.2.2: The energy envelope variance feature and the Euclidean distance between trajectory points are weighted and fused, and the trajectory point with the smallest fused distance is selected as the nearest neighbor.

[0039] Further, determining the topology correction parameters and inversely correcting the electromagnetic signal includes:

[0040] S103.1.1: Determine the sensitive frequency band in the electromagnetic signal based on the initial aging sensitivity factor, and construct the topology of the sensitive frequency band and the overall frequency band of the electromagnetic signal respectively;

[0041] S103.1.2: The Betti number of the two topologies is calculated using the continuous homology method, and the difference between the Betti numbers is analyzed to determine the topology correction parameters;

[0042] S103.1.3: Use the topology correction parameters to perform reverse correction on the entire frequency band of the electromagnetic signal to obtain the corrected electromagnetic signal.

[0043] Furthermore, the electromagnetic signal sensitive frequency bands are determined based on the initial aging sensitivity factor, including:

[0044] S103.1.1.1: Determine the initial sensitivity frequency reference range based on the initial aging sensitivity factor;

[0045] S103.1.1.2: Perform wavelet packet decomposition on the electromagnetic signal within the initial sensitive frequency reference range to obtain wavelet packet energy sub-bands at different scales;

[0046] S103.1.1.3: Calculate the multi-scale sample entropy for each energy sub-band to obtain the multi-scale sample entropy value corresponding to each energy sub-band;

[0047] S103.1.1.4: Select the frequency band corresponding to the energy sub-band whose multi-scale sample entropy value exceeds the preset entropy threshold as the sensitive frequency band.

[0048] Furthermore, the initially suspected aging areas were re-verified using the corrected electromagnetic signals, including:

[0049] S104.1.1: Perform empirical mode decomposition on the electromagnetic signal to obtain multiple modal components;

[0050] S104.1.2: Calculate the multi-scale permutation entropy of each modal component, determine the weighting factor based on the permutation entropy, and obtain the fused modal signal by weighted fusion;

[0051] S104.1.3: Determine new spectral energy anomaly segments based on the fused modal signals, and recheck the initial suspected aging spatial regions.

[0052] Secondly, the present invention provides a system for detecting the degree of cable aging, implemented based on the method for detecting the degree of cable aging described above, including:

[0053] The acquisition module is used to acquire electromagnetic and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area.

[0054] The reconstruction module is used to determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor.

[0055] The correction module is used to determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and to perform reverse correction on the electromagnetic signal using the topology correction parameters to obtain the corrected electromagnetic signal.

[0056] The judgment module is used to re-verify the initial suspected aging space area using the corrected electromagnetic signal. If the verified suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is re-determined based on the verified suspected aging space area.

[0057] An update module is used to update the initial aging sensitivity factor based on the updated vibration signal segment.

[0058] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0059] This invention comprehensively utilizes the spectral energy anomaly characteristics of electromagnetic signals and the dynamic characteristics of vibration signals. First, it uses variational mode decomposition and phase space reconstruction techniques to collaboratively determine the early aging region of cables. This overcomes the problem of false detection and missed detection caused by the susceptibility of single-signal methods in the prior art to external electromagnetic interference or environmental vibration. As a result, it can effectively reduce the false judgment rate in the process of identifying aging regions and significantly improve the sensitivity, reliability and accuracy of detecting early aging regions of cables.

[0060] Furthermore, by proposing a sensitive frequency band determination and topology correction method based on topological differences, the sensitive frequency bands related to aging in electromagnetic signals are specifically enhanced, reducing interference from frequency bands unrelated to aging. This achieves effective signal correction and feature extraction, solving the problems of indistinct signal features and severe background noise interference in existing technologies. In addition, by introducing multi-scale sample entropy and continuous homology topology analysis methods, the stability and robustness of sensitive frequency band identification and signal correction are improved, effectively enhancing the accuracy of abnormal region detection and confirmation.

[0061] Furthermore, by accurately calculating the local divergence characteristics of the trajectory and dynamically updating the aging sensitivity factor, it is possible to track and characterize the minute changes in the local aging state of the cable over time in real time. This solves the problem that traditional static characterization of aging characteristics cannot accurately track the actual changes in the cable's state in real time. Moreover, this invention realizes closed-loop feedback and adaptive updating between electromagnetic and vibration signal characteristics, further improving the overall detection system's response capability to changes in cable aging characteristics. It achieves real-time accurate monitoring and dynamic tracking of early cable aging, providing effective technical support for the safe operation of cables. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0063] Figure 1 This is a flowchart of a method for detecting the degree of aging of cables according to the present invention;

[0064] Figure 2 This is a framework diagram of a system for detecting the degree of aging of cables according to the present invention. Detailed Implementation

[0065] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make the description of this application more complete and comprehensive, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The drawings are merely illustrative illustrations of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0066] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of the exemplary embodiments disclosed in this application. However, those skilled in the art will recognize that the technical solutions disclosed in this application can be practiced with one or more specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the disclosure of this application.

[0067] Example 1

[0068] like Figure 1As shown, this embodiment discloses a method for detecting the degree of aging of cables, including:

[0069] S101: Acquire the electromagnetic and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area.

[0070] It should be understood that electromagnetic signals can be obtained by existing conventional sensing devices such as electromagnetic induction coils, magnetic field sensors or radio frequency sensors installed axially on the surface of the cable; while vibration signals can be obtained by existing conventional vibration sensing devices such as piezoelectric, fiber optic or MEMS vibration sensors installed at specific locations on the outer sheath of the cable. The sampling frequency of the signal is generally not less than 10 kHz to ensure that the signal details are fully recorded.

[0071] In practice, determining the initial suspected aging spatial region based on the spectral energy anomaly of the electromagnetic signal includes:

[0072] S101.1.1: Perform variational mode decomposition on the electromagnetic signal to obtain multiple modal components;

[0073] Specifically, variational mode decomposition is a method for adaptively decomposing a non-stationary signal into several modal components with different frequency bands to achieve effective separation of modal components with different frequencies. Its specific steps include:

[0074] (a) Initialize the preset number of modes K, which is generally determined based on experience, such as 4 to 8;

[0075] (b) Construct an optimization problem with the objective of minimizing the sum of the modal component bandwidths. The constraint is to minimize the error between the original signal and the sum of the modal components. The specific optimization objective function is:

[0076] ;

[0077] In the formula: For the k-th modal component, For the corresponding center frequency, For the Dirac function, The imaginary unit;

[0078] (c) The alternating direction multiplier method (ADMM) is used to iteratively solve the problem until convergence, and finally multiple modal components are obtained. ;

[0079] For example, in actual engineering, it is generally advisable to set the number of modes to 5 to 6;

[0080] S101.1.2: Perform Hilbert transform on each modal component to obtain the instantaneous amplitude spectrum of each modal component;

[0081] Specifically, the Hilbert transform is defined as follows:

[0082] ;

[0083] in, The result is the Hilbert transform. For Cauchy principal value integral operators;

[0084] Furthermore, the instantaneous amplitude spectrum of each modal component is:

[0085] ;

[0086] In the formula: The instantaneous amplitude spectrum of the k-th modal component;

[0087] S101.1.3: Calculate the rate of change of energy density of the instantaneous amplitude spectrum window by window along the frequency axis with a preset frequency window width (e.g., 1Hz to 10Hz). The calculation formula is as follows:

[0088] ;

[0089] In the formula: For frequency The rate of change of energy density at that location; For frequency The energy density is expressed as the average of the squares of the instantaneous amplitudes within the window; The width of the frequency window is determined based on the signal frequency range and actual resolution requirements.

[0090] S101.1.4: The frequency range in which the rate of change of energy density exceeds a preset threshold within a continuous window is defined as the spectrum energy anomaly range;

[0091] Specifically, the threshold for the rate of change of energy density can be set to a number of times (e.g., 2 to 3 times) the statistical mean of the overall rate of change.

[0092] S101.1.5: Bandpass filtering is applied to the abnormal interval, and the start and end times of the abnormal waveform and the propagation speed of the electromagnetic signal are considered (this speed is generally predetermined based on the cable material and type, such as the electromagnetic wave speed in the cable, which is generally within a certain range). Between), the initial suspected aging space region on the cable was determined using the time-domain reflectometry method;

[0093] Specifically, the formula for determining spatial location is:

[0094] ;

[0095] In the formula: Location of the initial suspected aging area on the cable; The speed at which electromagnetic signals propagate in the cable; , These are the start and end times of the abnormal waveform, respectively.

[0096] In practice, determining the corresponding initial vibration signal segment based on the initial suspected aging space region includes:

[0097] S101.2.1: Determine the corresponding location coordinates based on the initial suspected aging space area's position on the cable;

[0098] Specifically, taking the starting end of the cable as the origin, it can be directly... As corresponding spatial location coordinates;

[0099] S101.2.2: Calculate the vibration signal propagation delay based on the vibration signal propagation speed (generally, the mechanical vibration signal propagation speed in cables is 1000-5000 m / s, and the specific value can be determined according to the cable type) and the aforementioned position coordinates;

[0100] The specific calculation formula is as follows:

[0101] ;

[0102] In the formula: Delay for vibration signal propagation; The speed at which vibration signals propagate in a cable;

[0103] S101.2.3: Determine the start and end times of the initial vibration signal segment based on the propagation delay, and extract the initial vibration signal segment;

[0104] Specifically, if the start and end times of the abnormal electromagnetic signal segment are , The start and end times of the corresponding segment of the vibration signal can be determined by the delay compensation method, that is:

[0105] The start time of the vibration signal segment: ;

[0106] Vibration signal segment termination time: .

[0107] S102: Determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor.

[0108] In implementation, determining the phase space reconstruction parameters includes:

[0109] S102.1.1: Determine the optimal embedding dimension using the pseudo nearest neighbor method;

[0110] Specifically, the optimal embedding dimension is used to determine the appropriate dimension for signal expansion during phase space reconstruction, ensuring that the original signal dynamics are accurately reproduced; the implementation process of the pseudo nearest neighbor method is as follows:

[0111] (a) Construct phase spaces corresponding to different embedding dimensions m (e.g., gradually increasing from 1 to 10) for the initial vibration signal segment;

[0112] (b) In each phase space, for each trajectory point Determine its nearest neighbor. ;

[0113] (c) Calculate the percentage change in the distance between nearest neighbors for each trajectory point in dimensions m and m+1:

[0114] ;

[0115] In the formula: For trajectory points Coordinates in m-dimensional space; For trajectory points The coordinates of the nearest neighbor in m-dimensional space;

[0116] (d) Calculate the proportion of all trajectory points whose distance change ratio exceeds a preset threshold (e.g., 15%). When this proportion first falls below the preset threshold (e.g., 5%), the corresponding dimension m is determined as the optimal embedding dimension.

[0117] It should be noted that the above method can effectively eliminate misjudgments caused by false nearest points. The dimension that best represents the true dynamic characteristics of the vibration signal is selected as the optimal dimension. Usually, the actual dimension is selected between 3 and 6.

[0118] S102.1.2: Determining the optimal delay time using the autocorrelation function:

[0119] Specifically, the delay time is used to determine the time interval between adjacent coordinates in the trajectory point sequence, ensuring that the dynamic relationship between trajectory points is fully reflected. The specific implementation steps are as follows:

[0120] (a) Calculate the normalized autocorrelation function of the initial vibration signal segment. :

[0121] ;

[0122] In the formula: This represents the i-th sampled value of the vibration signal; is the mean of the signal sampling sequence; N is the length of the signal sequence;

[0123] (b) Select the first time that the absolute value of the autocorrelation function is lower than a preset threshold. The optimal delay time is a time t (e.g., 0.1–0.2). :

[0124] ;

[0125] It should be noted that the above autocorrelation function method can effectively avoid the clustering of trajectory points caused by too short a time or the loss of dynamic information caused by too long a time. Generally, based on experience, the delay time ranges from 1 / 4 to 1 / 2 of the signal period.

[0126] S102.1.3: Determine the optimal embedding dimension and optimal delay time as phase space reconstruction parameters;

[0127] In implementation, determining the local divergence characteristics of the trajectory includes:

[0128] S102.2.1: Reconstruct using phase space reconstruction parameters (optimal embedding dimension m and delay time) Initial vibration signal segment trajectory;

[0129] Specifically, assuming the initial vibration signal segment is a one-dimensional sequence Then the i-th trajectory point after phase space trajectory reconstruction Defined as:

[0130] ;

[0131] In the formula: m is the embedding dimension; Indicates vector transpose;

[0132] S102.2.2: Determine the nearest neighbor of each trajectory point;

[0133] Specifically, methods for determining the nearest neighbor in a trajectory include:

[0134] S102.2.2.1: Calculate the energy envelope variance characteristics within the local window of the vibration signal corresponding to each trajectory point (such as the range of 5 to 10 sampling points before and after the trajectory point);

[0135] Specifically, the method for calculating the energy envelope variance characteristic is as follows:

[0136] (a) Obtain the instantaneous envelope E(t) within a local window of the vibration signal using the Hilbert transform;

[0137] (b) Calculate the variance of the instantaneous envelope within the local window:

[0138] ;

[0139] In the formula: W is the length of the local window; This represents the j-th sample value of the instantaneous energy envelope; The mean of the instantaneous energy envelope within the window;

[0140] S102.2.2.2: The energy envelope variance feature and the Euclidean distance between trajectory points are weighted and fused, and the trajectory point with the smallest fused distance is selected as the nearest neighbor.

[0141] The fusion calculation formula is as follows:

[0142] ;

[0143] In the formula: Let i and j be the coordinates of the trajectory points; The energy envelope variance feature corresponding to the trajectory point; This is a weighting coefficient, typically ranging from 0.4 to 0.6, to balance the influence of geometric distance and energy characteristics on the determination of nearest neighbors;

[0144] S102.2.3: Calculate the change in distance between the trajectory point and its nearest neighbor over time to determine the local divergence index of the trajectory:

[0145] ;

[0146] In the formula: The local divergence index of the trajectory; is the distance between a trajectory point and its nearest neighbor at time t; N is the total number of trajectory points; For time step;

[0147] It should be noted that: local divergence index A larger value indicates a more significant local change in the trajectory, which is more likely to correspond to an early aging region of the cable.

[0148] S102.2.4: Determine the initial aging sensitivity factor based on the local divergence index;

[0149] Specifically, the initial aging sensitivity factor can be defined as the ratio of the local divergence index to a preset benchmark value (e.g., the statistical mean of the local divergence index in a normal cable area), that is:

[0150] ;

[0151] in, To set the baseline divergence index for the preset normal region, It is an initial aging-sensitive factor.

[0152] S103: Determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and use the topology correction parameters to perform reverse correction on the electromagnetic signal to obtain the corrected electromagnetic signal.

[0153] In implementation, determining the topology correction parameters and performing reverse correction of the electromagnetic signal includes:

[0154] S103.1.1: Determine the sensitive frequency band in the electromagnetic signal based on the initial aging sensitivity factor, and construct the topology of the sensitive frequency band and the overall frequency band of the electromagnetic signal respectively;

[0155] Specifically, the electromagnetic signal sensitive frequency bands determined based on the initial aging sensitivity factor include:

[0156] S103.1.1.1: Determine the initial sensitivity frequency reference range based on the initial aging sensitivity factor;

[0157] Specifically, the reference range of the initial sensitivity frequency is determined using empirical formulas based on the initial aging sensitivity factor. The following formula can be used:

[0158] ;

[0159] In the formula: The center frequency of the cable under normal conditions, determined based on experience; This is an adjustment factor for the sensitive frequency range, selected based on experiments and experience, and is generally between 0.5 and 2.

[0160] S103.1.1.2: Perform wavelet packet decomposition on the electromagnetic signal within the initial sensitive frequency reference range to obtain wavelet packet energy sub-bands at different scales; the specific implementation steps are as follows:

[0161] Select appropriate wavelet basis functions (such as db4 wavelet) to perform multi-scale wavelet packet decomposition on electromagnetic signals within the initial sensitive frequency reference range;

[0162] The signal is finely divided into frequencies according to the decomposition scale (e.g., 3 to 4 levels) to obtain multiple sub-band signals of different scales;

[0163] It should be noted that the scale of wavelet packet decomposition is generally determined based on the frequency characteristics of the cable to be tested. The higher the scale, the finer the frequency division, but the greater the computational load. Usually, a scale of 3 to 5 is selected to meet the detection accuracy requirements.

[0164] S103.1.1.3: Calculate the multi-scale sample entropy for each energy sub-band to obtain the multi-scale sample entropy value corresponding to each energy sub-band; the specific implementation steps are as follows:

[0165] For each energy sub-band signal Multi-scale coarsening processing is performed, and the specific coarsening process is as follows:

[0166] ;

[0167] In the formula: , which is a scale factor (e.g., 1 to 5);

[0168] At each scale Next, calculate the corresponding coarse-grained sequences respectively. The sample entropy is calculated using the following formula:

[0169] ;

[0170] In the formula: m is the embedding dimension, and r is the similarity tolerance, which is 0.15 to 0.25 times the standard deviation of the signal; These represent the proportions of similar vector pairs when the template vector dimensions are m and m+1, respectively.

[0171] Calculate the mean value of the sample entropy of each sub-band at each scale, and use it as the multi-scale sample entropy value.

[0172] S103.1.1.4: Select the frequency bands corresponding to energy sub-bands whose multi-scale sample entropy values ​​exceed a preset entropy threshold as sensitive frequency bands;

[0173] Specifically, an entropy threshold is set based on the average entropy of samples under historical normal cable conditions. For example, take 1.2 to 1.5 times the average entropy of normal samples; frequency bands exceeding the threshold represent areas with significant cable aging characteristics and are determined as the final sensitive frequency bands;

[0174] S103.1.2: The Betti numbers for the two topologies are calculated using the continuous homology method, and the differences between the Betti numbers are analyzed to determine the topology correction parameters; the specific implementation is as follows:

[0175] Phase space reconstruction is performed separately for the sensitive frequency band and the overall frequency band;

[0176] The 0th and 1st order Betti numbers of the phase space trajectories of the two frequency bands were calculated using the continuous cohomology method (the Betti numbers represent the number of connected components and the number of holes, respectively).

[0177] In the specific calculation process, by changing the topology analysis threshold, a continuous homology barcode is obtained, and the change curves of the 0th and 1st order Betti numbers are analyzed.

[0178] Determining topology correction parameters based on Betti number differences , is represented as:

[0179] ;

[0180] In the formula: These are the first-order Betti numbers for the overall frequency band and the sensitive frequency band, respectively; To prevent small positive numbers with a denominator of zero;

[0181] It should be noted that a larger difference in Betti numbers indicates a significant difference in topology between the sensitive frequency band and the overall frequency band, affecting the topology correction parameters. Also bigger;

[0182] S103.1.3: The electromagnetic signal is reverse-corrected across the entire frequency band using the aforementioned topology correction parameters to obtain the corrected electromagnetic signal; the specific method is as follows:

[0183] Based on the determined topology correction parameters, the spectral amplitude of the electromagnetic signal is nonlinearly weighted and corrected. The corrected spectral amplitude is... The calculation is as follows:

[0184] ;

[0185] In the formula: The amplitude of the original electromagnetic signal spectrum; The center frequency of the sensitive frequency band; The influence range parameter for frequency band correction is determined based on experimental or empirical data.

[0186] The corrected electromagnetic signal's time-domain waveform is obtained by performing an inverse Fourier transform using the corrected spectral amplitude and the original phase information.

[0187] It should be noted that the above topology correction process can effectively enhance the aging characteristic signal of sensitive frequency bands, improve the contrast between signal characteristics and background interference, and facilitate accurate identification of the aging degree in the future.

[0188] S104: Use the corrected electromagnetic signal to check the initial suspected aging space area again. If the checked suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is determined based on the checked suspected aging space area.

[0189] During implementation, the corrected electromagnetic signals are used to re-verify the initial suspected aging areas, including:

[0190] S104.1.1: Perform empirical mode decomposition on the electromagnetic signal to obtain multiple modal components; the specific implementation steps are as follows:

[0191] For the corrected electromagnetic signal Performing empirical mode decomposition (EMD) includes:

[0192] For the original signal In this process, all local extreme points (including maxima and minima) are identified.

[0193] The upper envelope of the signal is obtained by fitting the maximum and minimum points using cubic spline interpolation. With lower envelope ;

[0194] Calculate the local average envelope of the signal :

[0195] ;

[0196] Subtract the local average envelope from the original signal. The difference signal is obtained:

[0197] ;

[0198] Repeat the above steps until the difference signal satisfies the modal component condition (i.e., the mean approaches zero and the difference between the number of extrema and the number of zero crossings is at most 1), then the first intrinsic mode function (IMF) component is obtained. ;

[0199] Subtract the intrinsic mode function from the original signal. Afterwards, the remaining signals were obtained. Then, using the remaining signal as the new input signal, the above process is repeated to obtain a series of modal components. and remainder ;

[0200] It should be noted that empirical mode decomposition does not require preset basis functions. The decomposition result is multiple modal components at different scales, which can effectively extract the inherent characteristics of nonlinear and non-stationary electromagnetic signals. In general, the number of modes n after decomposition is determined by the spectral characteristics and complexity of the electromagnetic signal, and is usually 3 to 8.

[0201] S104.1.2: Calculate the multi-scale permutation entropy of each modal component, determine the weighting factor based on the permutation entropy, and obtain the fused modal signal by weighted fusion; the specific implementation steps are as follows:

[0202] For each modal component signal Perform multi-scale permutation entropy calculation. The calculation steps for multi-scale permutation entropy are as follows:

[0203] For modal components Perform coarsening processing for different scale factors, see the same section above for details;

[0204] For each coarse-grained sequence The permutation entropy PE is calculated as follows:

[0205] First of all A vector sequence is formed by m consecutive points, and the sequence is divided into different arrangement patterns according to the order of their numerical values.

[0206] Then, the frequency of each permutation pattern was counted. ;

[0207] Finally, calculate the permutation entropy:

[0208] ;

[0209] In the formula: The number of all possible permutation patterns;

[0210] Calculate the average permutation entropy of each modal component across all scales, denoted as . ;

[0211] The weighting factor is determined based on the average permutation entropy of each modal component. :

[0212] ;

[0213] All modal components are weighted and fused using weighting factors to obtain the fused modal signal. :

[0214] ;

[0215] It should be noted that: multi-scale permutation entropy reflects the complexity and randomness of modal component signals. The higher the entropy value, the richer the abnormal information carried by the modal component. Therefore, using permutation entropy for weight allocation can effectively highlight modal components with obvious aging characteristics and enhance the sensitivity and robustness of fused modal signals.

[0216] S104.1.3: Determine new spectral energy anomaly segments based on the fused modal signals, and re-verify the initial suspected aging spatial regions; the specific implementation steps are as follows:

[0217] For fused modal signals Perform a Fourier transform to obtain the spectrum of the fused mode signal;

[0218] Calculate the rate of change of spectral energy density window by window according to step S101.1.3 to determine the new spectral energy anomaly segment;

[0219] Based on the identified new spectral energy anomaly segment, repeat step S101.1.5 (i.e., perform bandpass filtering and time-domain reflection localization) to obtain the verified suspected aging spatial region.

[0220] It should be noted that the consistency of suspected areas can be judged by whether the difference between the two determinations of the spatial position of the area is within a preset range. If the difference is less than the preset spatial position error threshold (e.g., 10 cm), it is considered consistent. In this case, there is no need to redetermine the vibration signal segment to save computing resources.

[0221] It should be further explained that when there is a discrepancy, the position coordinates and vibration signal propagation delay are recalculated based on the verified location of the suspected aging space area according to the method described in steps S101.2.1 to S101.2.3, and the vibration signal at the corresponding location is re-extracted to obtain an updated vibration signal segment, so as to further improve the accuracy of the matching between the vibration signal segment and the suspected aging space area.

[0222] S105: Update the initial aging sensitivity factor based on the updated vibration signal segment;

[0223] In this embodiment, updating the initial aging sensitivity factor based on the updated vibration signal segment specifically includes:

[0224] The phase space trajectory of the updated vibration signal segment is reconstructed, and the local divergence index of the trajectory is determined.

[0225] It should be noted that the method for reconstructing the phase space trajectory here is the same as that used in step S102 above, that is, firstly, the optimal embedding dimension and the optimal delay time are determined, and then the trajectory is reconstructed using the determined phase space reconstruction parameters. For the specific process, please refer to the specific implementation of step S102 above, and it will not be described again here.

[0226] Based on the updated phase space trajectory of the vibration signal segment, determine the nearest neighbor point of each trajectory point in the trajectory;

[0227] It should be noted that the method for determining the nearest neighbor of a trajectory point is the same as that in step S102.2.2 above. That is, by calculating the energy envelope variance feature of the local vibration signal of each trajectory point, and then weighting and fusing this variance feature with the Euclidean distance between the trajectory points, the trajectory point with the smallest fused distance is selected as its nearest neighbor. The specific calculation method is also the same as described in S102.2.2 above, and will not be repeated here.

[0228] Based on the change in distance between the trajectory points and their corresponding nearest neighbors over time, calculate the updated trajectory local divergence index. :

[0229] ;

[0230] in, Let be the Euclidean distance between the i-th trajectory point and its corresponding nearest neighbor at time t;

[0231] With the updated trajectory local divergence index Replace the original initial aging-sensitive factor to complete the update of the initial aging-sensitive factor;

[0232] It should be noted that, because the updated vibration signal segment is more accurately located than the initial vibration signal segment and can more accurately reflect the actual aging state of the cable, the local divergence index determined based on this updated segment is... It can more accurately characterize aging features, thereby obtaining more precise aging-sensitive factors;

[0233] Through the above update process, this method can accurately track the changing trends of local features within the cable aging area, effectively improving the stability and accuracy of cable aging detection.

[0234] Example 2

[0235] like Figure 2 As shown in the example, the parts not detailed in this embodiment are as shown in Example 1. This embodiment discloses a system for detecting the degree of cable aging, including:

[0236] The acquisition module 201 is used to acquire electromagnetic signals and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area.

[0237] The reconstruction module 202 is used to determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor.

[0238] The correction module 203 is used to determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and to perform reverse correction on the electromagnetic signal using the topology correction parameters to obtain the corrected electromagnetic signal.

[0239] The judgment module 204 is used to re-verify the initial suspected aging space area using the corrected electromagnetic signal. If the verified suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is re-determined based on the verified suspected aging space area.

[0240] The update module 205 is used to update the initial aging sensitivity factor based on the updated vibration signal segment.

[0241] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0242] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0243] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the degree of aging of cables, characterized in that, include: S101: Acquire the electromagnetic and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area. The step of determining the initial suspected aging space region based on the spectral energy anomaly of the electromagnetic signal includes: S101.1.1: Perform variational mode decomposition on the electromagnetic signal to obtain multiple modal components; S101.1.2: Perform Hilbert transform on each modal component to obtain the instantaneous amplitude spectrum of each modal component; S101.1.3: Calculate the rate of change of energy density of the instantaneous amplitude spectrum window by window along the frequency axis with a preset frequency window width. The calculation formula is as follows: ； In the formula: For frequency The rate of change of energy density at that location; For frequency The energy density is expressed as the average of the squares of the instantaneous amplitudes within the window; The width of the frequency window is determined based on the signal frequency range and actual resolution requirements. S101.1.4: The frequency range in which the rate of change of energy density exceeds a preset threshold within a continuous window is defined as the spectrum energy anomaly range; S101.1.5: Bandpass filtering is applied to the abnormal interval, and the initial suspected aging space area on the cable is determined by time-domain reflection method based on the start and end time of the abnormal waveform and the propagation speed of the electromagnetic signal. The step of determining the corresponding initial vibration signal segment based on the initial suspected aging space region includes: S101.2.1: Determine the corresponding location coordinates based on the initial suspected aging space area's position on the cable; S101.2.2: Calculate the vibration signal propagation delay based on the vibration signal propagation speed and the position coordinates; S101.2.3: Determine the start and end times of the initial vibration signal segment based on the propagation delay, and extract the initial vibration signal segment; S102: Determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor. S103: Determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and use the topology correction parameters to perform reverse correction on the electromagnetic signal to obtain the corrected electromagnetic signal. S104: Use the corrected electromagnetic signal to check the initial suspected aging space area again. If the checked suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is determined based on the checked suspected aging space area. S105: Update the initial aging sensitivity factor based on the updated vibration signal segment.

2. The method for detecting the degree of aging of cables according to claim 1, characterized in that, The determination of phase space reconstruction parameters includes: S102.1.1: Determine the optimal embedding dimension using the pseudo nearest neighbor method; S102.1.2: Determining the optimal delay time using the autocorrelation function: S102.1.3: Determine the optimal embedding dimension and optimal delay time as phase space reconstruction parameters.

3. The method for detecting the degree of cable aging according to claim 2, characterized in that, The local divergence characteristics of the determined trajectory include: S102.2.1: Reconstruct the trajectory of the initial vibration signal segment using phase space reconstruction parameters; S102.2.2: Determine the nearest neighbor of each trajectory point; S102.2.3: Calculate the change in distance between the trajectory point and its nearest neighbor over time to determine the local divergence index of the trajectory: ； In the formula: The local divergence index of the trajectory; is the distance between a trajectory point and its nearest neighbor at time t; N is the total number of trajectory points; For time step; S102.2.4: Determine the initial aging sensitivity factor based on the local divergence index.

4. The method for detecting the degree of aging of cables according to claim 3, characterized in that, Methods for determining the nearest neighbor in a trajectory include: S102.2.2.1: Calculate the energy envelope variance characteristics within the local window of the vibration signal corresponding to each trajectory point; S102.2.2.2: The energy envelope variance feature and the Euclidean distance between trajectory points are weighted and fused, and the trajectory point with the smallest fused distance is selected as the nearest neighbor.

5. The method for detecting the degree of aging of cables according to claim 4, characterized in that, Determining the topology correction parameters and performing reverse correction of the electromagnetic signal includes: S103.1.1: Determine the sensitive frequency band in the electromagnetic signal based on the initial aging sensitivity factor, and construct the topology of the sensitive frequency band and the overall frequency band of the electromagnetic signal respectively; S103.1.2: The Betti number of the two topologies is calculated using the continuous homology method, and the difference between the Betti numbers is analyzed to determine the topology correction parameters; S103.1.3: Use the topology correction parameters to perform reverse correction on the entire frequency band of the electromagnetic signal to obtain the corrected electromagnetic signal.

6. The method for detecting the degree of aging of cables according to claim 5, characterized in that, Based on the initial aging sensitivity factor, the electromagnetic signal sensitive frequency bands include: S103.1.1.1: Determine the initial sensitivity frequency reference range based on the initial aging sensitivity factor; S103.1.1.2: Perform wavelet packet decomposition on the electromagnetic signal within the initial sensitive frequency reference range to obtain wavelet packet energy sub-bands at different scales; S103.1.1.3: Calculate the multi-scale sample entropy for each energy sub-band to obtain the multi-scale sample entropy value corresponding to each energy sub-band; S103.1.1.4: Select the frequency band corresponding to the energy sub-band whose multi-scale sample entropy value exceeds the preset entropy threshold as the sensitive frequency band.

7. The method for detecting the degree of aging of cables according to claim 6, characterized in that, The initial suspected aging areas were rechecked using the corrected electromagnetic signals, including: S104.1.1: Perform empirical mode decomposition on the electromagnetic signal to obtain multiple modal components; S104.1.2: Calculate the multi-scale permutation entropy of each modal component, determine the weighting factor based on the permutation entropy, and obtain the fused modal signal by weighted fusion; S104.1.3: Determine new spectral energy anomaly segments based on the fused modal signals, and recheck the initial suspected aging spatial regions.

8. A system for detecting the degree of aging of cables, implemented based on the method for detecting the degree of aging of cables according to any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire electromagnetic and vibration signals of the cable, determine the initial suspected aging space area based on the abnormal spectrum energy of the electromagnetic signal, and determine the corresponding initial vibration signal segment based on the initial suspected aging space area. The reconstruction module is used to determine the phase space reconstruction parameters based on the initial vibration signal segment, reconstruct the phase space trajectory of the initial vibration signal segment and determine the local divergence characteristics of the trajectory, and calculate the initial aging sensitivity factor. The correction module is used to determine the topology correction parameters of the electromagnetic signal based on the initial aging sensitivity factor, and to perform reverse correction on the electromagnetic signal using the topology correction parameters to obtain the corrected electromagnetic signal. The judgment module is used to re-verify the initial suspected aging space area using the corrected electromagnetic signal. If the verified suspected aging space area is consistent with the initial suspected aging space area, the initial vibration signal segment is directly used as the final vibration signal segment; if they are inconsistent, the updated vibration signal segment is re-determined based on the verified suspected aging space area. An update module is used to update the initial aging sensitivity factor based on the updated vibration signal segment.

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