Mine cable fault diagnosis system and method based on multi-band leakage current characteristics

Abstract

The present invention belongs to the technical field of fault diagnosis of mining cables, and specifically discloses a mining cable fault diagnosis system and method based on multi-band leakage current characteristics. The method of the present invention is based on the multi-band leakage current characteristics of mining cables, and proposes a flexible dynamic threshold denoising method, which can pre-process the multi-band leakage current signal, and combine the deep convolutional neural network to perform multi-level mining and analysis of the multi-band leakage current characteristics. The method shows high diagnostic accuracy in complex environments and can effectively improve the timeliness and accuracy of mining cable fault diagnosis. In addition, in order to meet the collection requirements for multi-band leakage current, the system of the present invention can separately collect low-frequency leakage current and high-frequency grounding wire leakage current in mining cables, realizing multi-band leakage current collection, and solving the technical problems that the existing zero-sequence current sensor is insensitive to high-frequency response and the high-frequency sensor is insensitive to low-frequency current and there is no dedicated multi-band leakage current collection device.

Description

Technical Field

[0001] The present invention belongs to the technical field of mining cable fault diagnosis, and in particular relates to a mining cable fault diagnosis system and method based on multi-band leakage current characteristics. Background Art

[0002] The complex environment, harsh operating conditions, and frequent load fluctuations of coal mines place extremely high demands on the operation of mining cables, a core component of coal mine power supply systems. High humidity and significant temperature variations in underground coal mines make the insulation of mining cables susceptible to aging, leading to insulation degradation. Under the harsh conditions of high temperature and humidity, and facing long periods of high load operation, mining cables are prone to overheating and insulation aging. Furthermore, the confined space underground in coal mines makes mining cables vulnerable to sudden impacts such as being hit, bumped, and dragged, which can damage the cable insulation and cause grounding or leakage faults. A mining cable failure can cause power outages, production interruptions, and even safety accidents. Therefore, accurate monitoring and fault diagnosis of the operating status of mining cables are crucial for maintaining equipment safety and ensuring stable mine production.

[0003] In recent years, with the development of artificial intelligence and signal processing technologies, mining cable fault diagnosis methods based on feature recognition and machine learning algorithms have gradually become a new means of detecting defects and faults in mining cables. When a fault occurs, the multi-band leakage current signal in the bottom line or shielding layer of the mining cable contains rich characteristic information, which can effectively reflect the internal defects or fault conditions of the mining cable. However, due to the complex and changing environment of the mine, the parameters such as the frequency, wavelength, and intensity of the multi-band leakage current signal of the mining cable will fluctuate accordingly, making it difficult to effectively identify the operating status of the mining cable by traditional judgment based on experience. Therefore, there is an urgent need to propose an intelligent diagnostic technology based on multi-band leakage current signals to achieve accurate identification of mining cable anomalies, provide important guarantees for the safe and stable operation of mine power equipment, and promote the improvement of the intelligent management level of mine power systems. Summary of the Invention

[0004] The purpose of the present invention is to propose a mining cable fault diagnosis method based on multi-band leakage current characteristics. The method mines the multi-band leakage current characteristics of mining cables and proposes a flexible dynamic threshold denoising method, which can pre-process the multi-band leakage current signals collected by the system. At the same time, combined with a deep convolutional neural network, the multi-band leakage current characteristics are mined and analyzed at multiple levels, which is conducive to the accurate identification of mining cable anomalies.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: a mining cable fault diagnosis method based on multi-band leakage current characteristics, comprising the following steps: step 1. performing noise reduction preprocessing on the acquired multi-band leakage current signal of the mining cable; wherein the multi-band leakage current signal of the mining cable is obtained by combining the high-frequency leakage current signal and the low-frequency leakage current signal collected synchronously; wherein the high-frequency leakage current signal and the low-frequency leakage current signal are two homologous data of the same original signal; step 2. performing feature extraction on the multi-band leakage current signal of the mining cable after the preprocessing in step 1, and annotating the extracted multi-band leakage current features of the mining cable to obtain a training data set; step 3. building a mining cable fault diagnosis model based on a convolutional neural network, training the mining cable fault diagnosis model based on the training data set obtained in step 2, and obtaining a trained mining cable fault diagnosis model; performing noise reduction preprocessing on the multi-band leakage current signal of the mining cable to be diagnosed according to the method of step 1, and after the feature extraction in step 2, inputting the signal into the trained mining cable fault diagnosis model to obtain a mining cable fault diagnosis result.

[0006] The purpose of the present invention is to propose a mining cable fault diagnosis system based on multi-band leakage current characteristics, which can separately collect low-frequency leakage current in the cable and high-frequency ground wire leakage current, thereby realizing multi-band leakage current collection.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a mining cable fault diagnosis system based on multi-band leakage current characteristics, including a multi-band leakage current mining cable fault information acquisition device and a host computer; the multi-band leakage current mining cable fault information acquisition device is used to collect multi-band leakage current signals of mining cables and transmit the collected multi-band leakage current signals of mining cables to the host computer; the host computer includes a memory and one or more processors; the memory stores executable code; when the processor executes the executable code, the mining cable fault diagnosis method based on multi-band leakage current characteristics as described above is implemented.

[0008] The multi-band leakage current mining cable fault information acquisition device includes an equipment bracket, a grounding wire guide channel, a multi-band leakage current data acquisition unit for synchronously acquiring multi-band currents, and an acquisition data processing unit; the grounding wire guide channel is arranged on the equipment bracket and has two ports for the mining cable grounding wire to pass through; the acquisition data processing unit is also installed on the equipment bracket; the multi-band leakage current data acquisition unit includes a low-frequency leakage current acquisition unit and a high-frequency leakage current acquisition unit; wherein, the low-frequency leakage current acquisition unit and the high-frequency leakage current acquisition unit are both arranged on the equipment bracket, and the low-frequency leakage current acquisition unit is located above the high-frequency leakage current acquisition unit and the grounding wire guide channel; the multi-band leakage current mining cable fault information acquisition device is arranged at the end position of the mining cable; wherein, the ring of the low-frequency leakage current acquisition unit The collection part is arranged in a circle on the mining cable; the grounding wire led out from the end of the mining cable first passes back in the opposite direction through the gap formed between the mining cable and the annular collection part of the low-frequency leakage current collection unit, and then is led out through the grounding wire guide channel and grounded; the reverse direction here refers to the direction from the end of the mining cable where the multi-band leakage current mining cable fault information collection device is located to the other opposite end of the mining cable; the annular collection part of the high-frequency leakage current collection unit is arranged in a circle on the grounding wire guide channel; the low-frequency leakage current collection unit and the high-frequency leakage current collection unit are both connected to the collection data processing unit, and are respectively used to send the corresponding collected low-frequency leakage signal and high-frequency leakage signal to the collection data processing unit; the collection data processing unit of the multi-band leakage current mining cable fault information collection device is connected to the host computer.

[0009] The present invention has the following advantages: As mentioned above, the present invention describes a mining cable fault diagnosis system and method based on multi-band leakage current characteristics. The method of the present invention is based on the multi-band leakage current characteristics of mining cables, and proposes a flexible dynamic threshold denoising method, which can pre-process the multi-band leakage current signal, and combine the deep convolutional neural network to perform multi-level mining and analysis of the multi-band leakage current characteristics. First, the multi-band leakage current signal collected by the high-frequency leakage sensor is denoised, and then the main frequency and fault frequency of the cable multi-band leakage current under normal and fault conditions are extracted, and feature extraction and deep convolutional neural network parameter setting are performed. This method shows high diagnostic accuracy in complex environments, can effectively improve the timeliness and accuracy of mining cable fault diagnosis, and has good practical application value and promotion prospects. In addition, in order to meet the requirements of the above-mentioned method for collecting multi-band leakage current, the system of the present invention can separately collect low-frequency leakage current and high-frequency grounding wire leakage current in mining cables, thereby realizing multi-band leakage current collection, which effectively solves the technical problems that the existing zero-sequence current sensor is insensitive to high-frequency response and high-frequency sensor is insensitive to low-frequency current, as well as the lack of a dedicated multi-band leakage current collection device. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flowchart of a mining cable fault diagnosis method based on multi-band leakage current characteristics in an embodiment of the present invention.

[0011] Figure 2 This is a structural diagram of the mining cable fault diagnosis model built in an embodiment of the present invention.

[0012] Figure 3 Schematic diagram of the structure of a multi-band leakage current mining cable fault information collection device in an embodiment of the present invention.

[0013] Figure 4 It is a side sectional view of a multi-band leakage current mining cable fault information collection device in an embodiment of the present invention.

[0014] Figure 5 This is an installation structure diagram of a multi-band leakage current mining cable fault information collection device in an embodiment of the present invention.

[0015] Among them, 1-equipment bracket, 2-grounding wire guide channel, 3-acquisition data processing unit, 4-low-frequency leakage current acquisition unit, 5-high-frequency leakage current acquisition unit, 6-mining cable, 7-grounding wire, 8-clamp handle; 9-first shell, 10-first coil, 11-first magnetic core, 12-first output circuit, 13-second shell, 14-second coil, 15-second magnetic core, 16-second output circuit, 17-insulation fixing bracket, 18-adjustable rolling belt. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods: Example 1 In view of the long-term operation of mining cables in complex mine environments, they are easily affected by factors such as moisture, dust, and high load, which leads to the problem of aging of mining cables. This Example 1 proposes a mining cable fault diagnosis method based on multi-band leakage current characteristics. The method is based on multi-band leakage current characteristics for mining, establishes a flexible dynamic threshold value denoising method, and proposes a mining cable fault diagnosis model based on convolutional neural network. The method makes full use of the advantages of the flexible dynamic threshold value algorithm in removing noise interference and retaining signal characteristics. By decomposing and transforming the collected multi-band leakage current signal, the background noise in complex working conditions such as coal mines is removed, and then the time-frequency features after denoising are extracted and input into the convolutional neural network model for feature learning and fault identification. Experiments have proved that the improved model of the present invention can effectively identify the fault state of mining cables under complex working conditions, and realize efficient and accurate diagnosis and evaluation of the operating status of mining cables.

[0017] like Figure 1As shown, a mining cable fault diagnosis method based on multi-band leakage current characteristics includes the following steps: Step 1. Pre-processing the acquired multi-band leakage current signal of the mining cable to reduce noise; wherein the multi-band leakage current signal of the mining cable is obtained by combining the high-frequency leakage current signal and the low-frequency leakage current signal collected synchronously.

[0018] The high-frequency leakage current signal and the low-frequency leakage current signal are two homologous data of the same original signal.

[0019] In the present embodiment, the high-frequency leakage current signal and the low-frequency leakage current signal can generally be used as two signal input models for feature mining and diagnosis in fault diagnosis. Of course, the data fusion principle can also be adopted, that is, the two signals can be fused.

[0020] Specifically, there are at least two ways to fuse the high-frequency leakage current signal and the low-frequency leakage current signal, namely, cutoff frequency fusion and weighted fusion according to the different response characteristics of the magnetic core to each frequency band.

[0021] From at least two fusion methods, one can be selected as the fusion method for the high-frequency leakage current signal and the low-frequency leakage current signal. Of course, the above two fusion methods are only examples, and a specific fusion method can also be used as needed.

[0022] Step 2. Extract features from the multi-band leakage current signal of the mining cable preprocessed in step 1, and annotate the extracted multi-band leakage current features of the mining cable to obtain a training dataset.

[0023] The acoustic signal frequency of a properly operating mining cable is typically concentrated below 1000Hz, primarily manifesting as n-fold components around a 50Hz fundamental frequency. However, when a mining cable fault occurs, the frequency range of the zero-sequence leakage current signal significantly extends, typically to within 4kHz. Noise interference above 4kHz is considered a minor factor and can be almost ignored.

[0024] To more effectively analyze and diagnose different types of mining cable faults, it is necessary to systematically classify noise signals. This classification method can more accurately extract the characteristics of each type of noise, providing a basis for subsequent fault type identification.

[0025] Due to the complexity and time-varying nature of non-stationary signals, directly analyzing and applying them for fault diagnosis is extremely difficult. To preprocess the extracted acoustic signals, the present invention employs a newly improved microwave threshold denoising algorithm to preprocess multi-band leakage current signals. Microwave threshold denoising employs a decomposition transformation method to decompose the signal into different scales. High-frequency coefficients are processed by setting thresholds to remove noise, ultimately reconstructing a valid, noise-free signal.

[0026] Microwave threshold denoising involves three steps: original signal transformation and decomposition, threshold setting and denoising, and signal reconstruction. The following describes the process of implementing noise reduction preprocessing on multi-band leakage current signals using an improved microwave threshold denoising algorithm.

[0027] Step 1.1. Decomposition of original signal.

[0028] First, the signal x(t) is decomposed into high-frequency coefficients representing high-frequency components and low-frequency coefficients representing low-frequency components.

[0029] (1).

[0030] Where x(t) represents the obtained multi-band leakage current signal of the mining cable.

[0031] W j,k is the high-frequency coefficient of the jth layer and position k, representing the high-frequency part of the signal x(t); V J,k is the low-frequency coefficient at the Jth layer and position k, representing the low-frequency part of the signal x(t); is the basis function, is the magnitude function.

[0032] Step 1.2. Flexible dynamic threshold denoising.

[0033] The high-frequency coefficients decomposed in step 1.1 above are The following processing is performed, as shown in formulas (2) and (3).

[0034] (2).

[0035] (3).

[0036] Formula (2) and Formula (3) are the denoising threshold formula and the optimization threshold formula respectively, and λ is the threshold parameter.

[0037] Traditional threshold functions are prone to introducing fixed bias when processing high-frequency decomposition coefficients, which in turn introduces errors during signal reconstruction, resulting in reduced accuracy and significant distortion. However, simply setting a denoising threshold sets the portion of the decomposition coefficients that exceeds the set value to zero. This leads to unnecessary oscillations in the reconstructed signal and discontinuities at the threshold value, which is not conducive to maintaining signal continuity and stability.

[0038] In order to solve the above problems, the present invention proposes a flexible dynamic denoising threshold function, the formula of which is as follows: (4).

[0039] (5).

[0040] Where x represents the high frequency coefficient , T(x) is the denoising threshold output, The dynamic threshold value is used to determine which coefficients are compressed or retained. A small positive number used to avoid division by zero. It is usually set to 0.01 or other sufficiently small values ​​to ensure computational stability. N represents the selected signal length, and a represents the number of signal data points.

[0041] Find After that, it is substituted into formula (2) as the high-frequency coefficient for signal reconstruction. Through the above steps, the flexible dynamic denoising threshold coefficient can be obtained by formula (4), and the optimized threshold coefficient can be obtained by formula (3).

[0042] Step 1.3. Signal reconstruction.

[0043] The processed decomposition coefficients are used to reconstruct the signal to obtain a denoised signal. During the signal reconstruction process in step 1.3, it is usually necessary to select one of the threshold coefficients based on the signal characteristics and needs.

[0044] Specifically, if the noise amplitude in the signal is large and a pure filtered signal is required, the optimized threshold coefficient obtained by formula (3) is used; if the noise amplitude in the signal is low and the details in the signal need to be retained, the flexible dynamic denoising threshold coefficient obtained by formula (4) is used.

[0045] After denoising with the flexible dynamic threshold value obtained by formula (4) or the optimized threshold value by formula (3), the new coefficients are substituted into formula (1), and the inverse operation of formula (1) is performed to obtain the denoised signal S(i); (6).

[0046] In extracting the multi-band leakage current signal characteristics of mining cables, time-frequency analysis methods have a strong ability to capture non-stationary signals and can effectively monitor mining cable status parameters. Based on the structural characteristics of mining cables, optimized sensor layout, actual operating conditions, and multiple sets of fault data, this paper systematically extracts the following features to characterize the multi-band leakage current characteristics of mining cable status through a comprehensive analysis method combining time and frequency domains.

[0047] Among them, the extracted multi-band leakage current characteristics of mining cables include empirical characteristics corresponding to various types of faults, main frequency characteristics, odd-even ratio, harmonic ratio, spectrum center of gravity, total harmonic distortion, instantaneous frequency variation and energy entropy.

[0048] 1. Empirical characteristics corresponding to major faults.

[0049] The operating conditions of mining cables under different load conditions, environmental noise, and various fault conditions (such as partial discharge and single-phase grounding fault) are marked according to the empirical parameter characteristics corresponding to each type of fault.

[0050] For example, single-phase to ground fault characteristics.

[0051] When a single-phase ground fault occurs, a low-frequency zero-sequence current is generated. Its characteristics are different in different operating line states. For a neutral point ungrounded system, when a single-phase ground fault occurs, the single-phase ground zero-sequence current Phase-to-ground capacitance current during normal system operation 3 times.

[0052] (7).

[0053] The low-frequency zero-sequence current of a power system with a directly grounded neutral point is equal to the short-circuit current.

[0054] For example, locally amplify fault characteristics.

[0055] When partial discharge occurs, the high-frequency partial discharge pulse current can be measured by the pulse current method. The center frequency of the current is about 20k, and the threshold can be set. , when the pulse current value When it is greater than the threshold, it is regarded as a partial discharge current characteristic.

[0056] (8).

[0057] This part is a commonly used fault judgment method. In the present invention, it is only regarded as a feature as the input of the diagnosis model.

[0058] 2. Main frequency characteristics.

[0059] Main frequency F m This indicates the main frequency point where energy is concentrated in the signal spectrum and is the core frequency feature of the multi-band leakage current signal. This feature is used to reveal the main components of the electrical fundamental frequency in the operation of mining cables.

[0060] Under normal circumstances, the main frequency of mining cables is usually equal to the power frequency (50 Hz). When an abnormal deviation occurs, it indicates that the system is in an abnormal state or is subject to external interference. The calculation formula is: (9).

[0061] Where, X f is the signal amplitude at frequency f.

[0062] 3. Odd-even ratio.

[0063] Odd-even ratio P mUsed to measure the energy distribution of odd and even multiples in the signal. During normal operation, odd multiples tend to dominate. When the energy of even multiples increases significantly, the mining cable may have non-metallic grounding or arc grounding discharge problems. The calculation formula is: (10).

[0064] Where n is the upper limit of the multiple within the statistical range.

[0065] 4. Harmonic ratio.

[0066] Harmonic ratio H m Evaluate the relative strength of the fundamental frequency and its harmonic components. A high harmonic ratio often means an increase in the nonlinear characteristics of the system, which can be used to judge the health status of mining cables. The calculation formula is: (11).

[0067] Where, is the amplitude of the nth harmonic, f0 is the fundamental frequency, represents the fundamental amplitude, and N represents the total number of harmonics.

[0068] 5. Spectral center of gravity.

[0069] Spectral center C f Represents the center of gravity of the signal spectrum. It reflects the dynamic characteristics of the system by measuring the deviation of the frequency distribution. The displacement of the center of gravity usually indicates the change of the frequency component during the operation of the system. The calculation formula is: (12).

[0070] Where, X f is the frequency amplitude in the vibration signal spectrum, Indicates the maximum frequency, Indicates the minimum frequency. The spectrum center of the noise at different measuring points and working conditions of mining cables reflects the degree of concentrated distribution of frequency components. Its overall trend is similar to the frequency complexity.

[0071] 6. Total harmonic distortion.

[0072] Total harmonic distortion (THD) quantitatively evaluates the degree of distortion of harmonic components in a signal relative to the fundamental frequency. It is a key indicator for describing harmonic pollution in power systems and equipment nonlinearity. A high THD value means that the equipment is subject to greater nonlinear effects. The calculation formula is: (13).

[0073] in, represents the amplitude of the nth harmonic, Indicates the fundamental wave amplitude.

[0074] 7. Instantaneous frequency variation.

[0075] The instantaneous frequency variation (IFD) reflects the dynamic characteristics of frequency changes in the signal. It is used to capture possible frequency mutations and instability in the system and is an important feature for identifying sudden faults. The calculation formula is: (14).

[0076] Where, represents the instantaneous phase, and T represents the transient time difference.

[0077] 8. Energy entropy.

[0078] Energy entropy E s It describes the energy distribution complexity of the signal spectrum and measures the effective characteristics of random components and irregular behaviors in the signal. High energy entropy usually reflects that the system is subject to more random interference or a mixture of multi-frequency components. The calculation formula is: (15).

[0079] in, represents the energy of sample i.

[0080] Through the leakage current characteristics, the mine cable fault diagnosis results that can be achieved are the output of the mine cable fault diagnosis model, including no fault, single-phase grounding fault, high-resistance single-phase grounding fault, partial discharge and two-phase short circuit.

[0081] Step 3. Build a mining cable fault diagnosis model based on convolutional neural network, such as Figure 2 As shown, the mining cable fault diagnosis model is trained based on the training data set obtained in step 2 to obtain a trained mining cable fault diagnosis model.

[0082] As a key model for deep learning, convolutional neural networks (CNNs) have demonstrated significant advantages in processing spatially structured data such as images, videos, and speech. Through local connections and parameter sharing, CNNs reduce the large amount of parameter redundancy found in traditional fully connected networks, enabling them to efficiently extract both local and global features from data.

[0083] like Figure 2 As shown in FIG, the convolutional neural network in this embodiment is composed of a convolutional layer, a pooling layer, an activation function, and a fully connected layer, which can gradually abstract the input data and realize automatic learning and classification of complex tasks.

[0084] This method extracts multi-band leakage current characteristics from mining cables and uses them as input. Using deep learning methods from convolutional neural networks, it trains the deep relationship between these input characteristics and fault classification, thereby forming an intelligent agent for multi-band leakage current signal fault diagnosis. When samples are input into the convolutional neural network, the output of each convolution layer is a meaningless code parameter. After fully connected, the softmax function is applied, resulting in the output of the probability distribution of various fault types.

[0085] The main function of a convolutional layer is to locally scan the input data using a set of convolutional filters to extract local features. The essence of the convolution operation is to generate a new feature map by performing element-by-element dot products between the convolutional filters and a local region of the input feature map. Each convolutional filter in a convolutional layer extracts a specific pattern in the input (such as edges or textures). By stacking these filters layer by layer, convolutional layers can extract feature representations of the input from low-level to high-level layers.

[0086] The convolution operation can be expressed in the following mathematical form: (16).

[0087] Where: is the pixel value at the output feature position (i, j) of the convolution layer l+1, is the value input to the lth layer, at position (i+m, j+n), and is used to correspond to different positions of the convolution kernel through the offset of m and n. M represents the total offset of i, and N1 represents the total offset of j. represents the convolution kernel, represents the integration variable.

[0088] To further reduce the spatial dimension of feature maps, reduce computational complexity, and enhance the model's ability to resist interference, CNNs often use pooling layers. Pooling layers downsample local regions, retaining important feature information while discarding unnecessary details.

[0089] The most commonly used pooling method is Max Pooling, which retains local significant features by selecting the maximum value in each pooling window. The mathematical expression of Max Pooling is: (17).

[0090] Where, represents the maximum pooling output, and p and q are the sizes of the pooling window. Pooling not only reduces the number of model parameters and computational complexity, but also provides the model with a certain degree of translation invariance, making it robust to small changes in the input signal.

[0091] Convolution operations are inherently linear. To enhance the model's ability to learn nonlinear features, CNNs typically introduce an activation function after the convolution layer. The most commonly used activation function is ReLU (Rectified Linear Unit), which has the following formula: (18).

[0092] ReLU can effectively avoid the gradient vanishing problem, allowing the network to maintain good learning efficiency even when the training depth is deep.

[0093] After several layers of convolution and pooling, CNNs flatten the extracted features and feed them into fully connected layers (FC layers). A FC layer is similar to a layer in a traditional neural network, where each neuron is connected to all neurons in the previous layer. This layer maps the previously extracted features to the final classification space and performs classification or regression predictions based on the learned weights. The calculation formula for the FC layer is: (19).

[0094] Where, represents the output of the fully connected layer, x j is the input feature, w ij is the weight, b i The final output is usually passed through the softmax function for multi-classification tasks, which converts the output value into a probability distribution: (20).

[0095] Where K is the number of categories, and P(y=i) is the probability that the input data belongs to category i.

[0096] The training process of convolutional neural networks relies on the back propagation algorithm, which optimizes the weights, biases and other parameters of the convolution kernel by calculating the gradient of the loss function. Specifically, the loss function The calculation formula is as follows: (twenty one).

[0097] Where y i is the probability of the true label, is the probability predicted by the model. The mining cable fault diagnosis model gradually updates the parameters through the gradient descent algorithm to minimize the loss function and thus optimize the model performance.

[0098] The training process of the mining cable fault diagnosis model is as follows: In practical applications, the training of the mining cable fault diagnosis model is completed first, and all the feature data of the multi-band leakage current characteristics of the mining cable extracted in step 2 are used as input and input into the convolutional neural network. The convolutional neural network mines the correspondence between each input feature and the deep feature of the fault according to the set fault type.

[0099] By continuously adjusting the internal feature parameters of the convolutional neural network, the mining cable fault diagnosis model training is completed.

[0100] The trained mining cable fault diagnosis model is deployed on the host computer of the mining cable fault diagnosis system based on multi-band leakage current characteristics, and the mining cable fault diagnosis is implemented according to the above method steps.

[0101] Specifically, the multi-band leakage current signal of the mining cable to be diagnosed is subjected to noise reduction preprocessing according to step 1, and after feature extraction in step 2, it is input into the trained model to obtain the mining cable fault diagnosis result.

[0102] The method of the present invention outputs the above-mentioned mining cable fault diagnosis model, and ultimately can realize accurate diagnosis and identification of mining cable faults by detecting multi-band leakage current signals when an abnormality occurs in the mining cable.

[0103] Example 2 This Example 2 describes a mining cable fault diagnosis system based on multi-band leakage current characteristics. The system includes a multi-band leakage current mining cable fault information acquisition device and a host computer. The host computer is generally composed of a high-performance industrial-grade workstation, which is primarily used for training fault diagnosis algorithms and actual diagnostic calculations. The multi-band leakage current mining cable fault information acquisition device is a specially designed information acquisition device for the diagnostic method described in the present invention.

[0104] Specifically, the multi-band leakage current mining cable fault information collection device is used to collect multi-band leakage current signals of mining cables and transmit the collected multi-band leakage current signals of mining cables to a host computer.

[0105] The host computer receives the data and implements mining cable fault identification and diagnosis based on multi-band leakage current feature mining through the program.

[0106] In this embodiment, the host computer includes a memory and one or more processors.

[0107] The memory stores executable code; when the processor executes the executable code, the mining cable fault diagnosis method based on multi-band leakage current characteristics as described in the above embodiment 1 is implemented.

[0108] In addition, in response to the problems that existing zero-sequence current sensors are insensitive to high-frequency responses and high-frequency sensors are insensitive to low-frequency currents, and there is no dedicated multi-band leakage current collection device, the present invention provides a multi-band leakage current mining cable fault information collection device, which can separately collect low-frequency leakage currents in cables and high-frequency grounding wire leakage currents, thereby realizing multi-band leakage current collection.

[0109] like Figure 3As shown, the present invention designs a new multi-band leakage current mining cable fault information acquisition device based on the operating characteristics of mining cables. When a mining cable fails, its leakage current signal contains current responses in multiple frequency bands due to different fault types. In actual engineering acquisition, due to the characteristics of the sensor material, the sensor coils composed of different materials respond differently to the frequency range. A sensor composed of only one material cannot meet the demand for obtaining multi-band signals. In addition, mining cables are generally three-core cables. There is a difference between the fault leakage current composed of the algebraic sum of the three-phase currents and the leakage current flowing through the shielding layer grounding wire. This difference can be used to realize partial fault diagnosis and fault location of mining cables.

[0110] Therefore, in order to capture all signal information of the leakage current generated by the fault, the present invention designs a dedicated multi-band leakage current data acquisition unit, which can simultaneously and synchronously acquire multi-band currents.

[0111] Specifically, the multi-band leakage current mining cable fault information collection device includes an equipment bracket 1, a grounding wire guide channel 2, a multi-band leakage current data collection unit for synchronously collecting multi-band currents, and a collection data processing unit 3.

[0112] The equipment bracket 1 is a rectangular parallelepiped structure. The grounding wire guide channel 2 is arranged on the equipment bracket 1 and has two ports for the mining cable grounding wire to pass through. The grounding wire guide channel 2 can be built into the equipment bracket 1, for example.

[0113] One end of the grounding wire guide channel 2 passes through the upper surface of the equipment bracket 1 (that is, one end is open facing upward), and the other end (for example, can be defined as the front end) extends to the front end surface of the equipment bracket 1 (one end is open ahead). Therefore, the grounding wire guide channel 2 can be designed as a tubular structure with a corner (that is, it is composed of two straight pipes and a curved pipe at one end) to facilitate the lead-out of the grounding wire.

[0114] The data acquisition processing unit 3 is also installed on the equipment bracket 1 , and is, for example, arranged at the rear end bottom of the equipment bracket 1 .

[0115] The multi-band leakage current data acquisition unit includes a low-frequency leakage current acquisition unit 4 and a high-frequency leakage current acquisition unit 5 .

[0116] The low-frequency leakage current collection unit 4 and the high-frequency leakage current collection unit 5 are both arranged on the equipment bracket 1 , and the low-frequency leakage current collection unit 4 is located above the high-frequency leakage current collection unit 5 and the grounding wire guide channel 2 .

[0117] The multi-band leakage current mining cable fault information collection device is set at the end of the mining cable, such as Figure 3 As shown; wherein, the annular collection part of the low-frequency leakage current collection unit 4 is arranged around the mining cable 6.

[0118] The frequency response of the low-frequency leakage current acquisition unit 4 is 30-1500 Hz, and it is mainly used for collecting information of power frequency and low-order harmonics.

[0119] The grounding wire 7 led out from the end of the mining cable first passes back in the opposite direction through the gap formed between the mining cable 6 and the annular collection part of the low-frequency leakage current collection unit 4, then leads out through the grounding wire guide channel 2 and is grounded.

[0120] The reverse direction here refers to the direction from the end of the mining cable where the multi-band leakage current mining cable fault information collection device is located to the other opposite end of the mining cable (i.e., the direction from the front end of the equipment bracket 1 to the rear end).

[0121] The annular collecting portion of the high-frequency leakage current collecting unit 5 is arranged around the grounding wire guiding channel 2 .

[0122] The low-frequency leakage current acquisition unit 4 and the high-frequency leakage current acquisition unit 5 are both connected to the acquisition data processing unit 3 and are used to send the corresponding low-frequency leakage signal and high-frequency leakage signal to the acquisition data processing unit 3 respectively.

[0123] The data collection processing unit 3 of the multi-band leakage current mining cable fault information collection device is connected to the host computer.

[0124] like Figure 2 As shown, the low-frequency leakage current collection unit is designed as a clamp-shaped structure, that is, the upper annular collection part is designed as an openable structure, and the lower part of the low-frequency leakage current collection unit is designed with a clamp-shaped handle 8 for controlling the opening and closing of the openable structure.

[0125] The clamp-shaped structure design allows the low-frequency leakage current acquisition unit 4 to be directly clamped on the mining cable 6 without removing the mining cable from the system for installation, thereby simplifying the installation of the low-frequency leakage current acquisition unit 4 .

[0126] like Figure 2 As shown, the annular collection part of the low-frequency leakage current collection unit 4 includes a first housing 9, a first coil 10, a first magnetic core 11 and a first output circuit 12. The first housing 9 is made of insulating material, and the first magnetic core 11 is located inside the first housing.

[0127] The first shell 9 and the first magnetic core 11 are both annular in overall outline. To match the clamp structure of the low-frequency leakage current acquisition unit, the first shell 9 and the first magnetic core 11 are both designed with a semicircular ring structure, and form an annular structure after being assembled.

[0128] The first magnetic core 11 is made of ferrite, silicon steel sheet or alloy material with good low-frequency response effect.

[0129] A first coil 10 is wound around the first magnetic core 11 ; the first coil is connected to a first output circuit 12 ; the first output circuit is used to output the induced low-frequency leakage current at a regulated voltage, and send the output to the acquisition data processing unit 3 through a signal line.

[0130] The process of collecting low-frequency leakage current by the low-frequency leakage current collection unit is as follows: the first magnetic core 11 is inserted into the entire mining cable 6, and the three phases A, B, and C of the mining cable and the ground wire are regarded as four lines. The currents in each line, namely the three-phase current of the cable and the ground wire current, all induce an alternating magnetic field on the magnetic core through the principle of electromagnetic induction.

[0131] Since the grounding wire 7 is passed back in the opposite direction during installation, the magnetic fields generated by the currents of the grounding wire 7 cancel each other out. Therefore, the first magnetic ring 11 only has the induced magnetic field of the three-phase current, that is, the magnetic field induced by the algebraic sum of the three-phase current.

[0132] Under the action of the alternating magnetic field, the first coil 10 wound on the first magnetic ring induces a low-frequency current; the first coil is connected to the first output circuit, and the low-frequency current is stabilized by the first output circuit and then sent to the acquisition data processing unit 3.

[0133] The frequency response of the high-frequency leakage current acquisition unit 5 is 0.5kHz~50kHz. The high-frequency leakage current acquisition unit 5 is mainly used for medium and high frequency response. Its structural form is a rectangular parallelepiped with a through hole for the ground wire guide channel 2 provided therein.

[0134] The annular collection part of the high-frequency leakage current collection unit includes a second shell 13, a second coil 14, a second magnetic core 15 and a second output circuit 16; wherein the second shell is made of insulating material, and the second magnetic core 15 is located inside the second shell.

[0135] The second shell 13 and the second magnetic core 15 are both annular in overall outline.

[0136] The second magnetic core 15 is made of, for example, Permalloy, which has a good high-frequency response effect.

[0137] A second coil 14 is wound around the second magnetic core 15 ; the second coil 14 is connected to a second output circuit 16 ; the second output circuit is used to output the induced high-frequency leakage current at a stable voltage and send it to the acquisition data processing unit 3 through a signal line.

[0138] The process of collecting high-frequency leakage current by the high-frequency leakage current collection unit is as follows: the second magnetic core 15 inserts the ground wire 7, and the ground wire current induces an alternating magnetic field on the magnetic core through the principle of electromagnetic induction. Since the magnetic ring is sensitive to high-frequency signals; under the action of the alternating magnetic field, the second coil wound on the second magnetic ring induces a high-frequency current.

[0139] The second coil is connected to the output circuit, and the high-frequency current is sent to the acquisition data processing unit 3 after being stabilized by the second output circuit.

[0140] By means of the low-frequency leakage current acquisition unit 4 and the high-frequency leakage current acquisition unit 5 , low-frequency leakage current signals and medium- and high-frequency leakage current signals can be acquired at the cable end simultaneously without using a variety of sensors and acquisition devices.

[0141] The data acquisition and processing unit 3 primarily consists of a housing, bracket, main control board, power port, output terminals, and antenna. The main control board includes dual-channel input terminals, a conditioning circuit, an analog-to-digital conversion chip, a CPU, output terminals, a memory chip, and communication circuitry. It provides dual-channel synchronous data acquisition and input, Beidou synchronous timing, data preprocessing, data storage, and output capabilities.

[0142] The rear of the housing houses an antenna, which connects to the main control board's output port for satellite timing synchronization and 4G data transmission. The output terminal connects to the main control board's output port, typically using a network cable or fiber optic interface, for data transmission with the host computer.

[0143] The data acquisition and processing unit's functional implementation is as follows: The high-frequency and low-frequency leakage current signals collected and output by the multi-band leakage current data acquisition unit flow through signal lines into the dual-channel synchronous acquisition input terminals of the main control board. The high-frequency and low-frequency signals pass through two channels, are conditioned and filtered by the conditioning circuit, and then enter the main control board's dual-channel analog-to-digital conversion chip. Both signals are synchronously converted into digital quantities and stored in memory. The main control board's CPU controls these signals and transmits them to the communication circuit for transmission to the host computer.

[0144] In addition, the multi-band leakage current mining cable fault information collection device further includes an insulating fixing bracket 17, wherein the insulating fixing bracket is arranged on the equipment bracket 1 and is installed and fixed to the mining cable using an adjustable tie.

[0145] The insulating fixing bracket 17 and the low-frequency leakage current collection unit 4 are located on opposite sides of the equipment bracket 1 , respectively. The low-frequency leakage current collection unit 4 is located on the front side of the equipment bracket 1 , and the insulating fixing bracket 17 is located on the rear side of the equipment bracket 1 .

[0146] The present invention can fix the collection device on the mining cable 6 through the insulating fixing bracket 17. Figure 3 The installation structure of the multi-band leakage current mining cable fault information collection device is shown.

[0147] Typically, each mining cable line uses a dual-end installation method, with a data acquisition device installed at each end of the mining cable 6. Both devices simultaneously collect current signals. The installation method is similar to that of a conventional zero-sequence current transformer, specifically as follows: 1. Clamp the low-frequency leakage current acquisition unit 4 onto the mining cable using its clamp-type design. Simultaneously, route the mining cable's ground wire back through the acquisition portion of the low-frequency leakage current acquisition unit 4.

[0148] The purpose of the low-frequency acquisition part is to collect the algebraic sum of the three-phase currents. When the clamp-on sensor is clamped on the cable, it contains not only the three-phase currents but also the ground wire current. Therefore, it is necessary to lead the ground wire out from the end of the cable and reversely pass it back to the low-frequency acquisition part. This can offset the positive and negative ground wire currents, ensuring that the low-frequency acquisition part only collects the algebraic sum of the three-phase currents.

[0149] 2. Insert the ground wire into the ground wire routing channel of the device, pass it out from the front end, and then ground the ground wire.

[0150] In order to obtain high-frequency characteristics, the high-frequency collection part collects the high-frequency leakage current of the ground wire, so it needs to be collected separately. Therefore, the present invention passes the ground wire 7 through the collection magnetic core (ie, the second magnetic core 15) and then grounded.

[0151] 3. Wrap the adjustable rolled band 18 of the device's insulating fixing bracket 17 around the mining cable 6 and secure it to secure the device.

[0152] When in use, the multi-band leakage current mining cable fault information collection device is powered on and started. The device's main control board has a built-in program that automatically realizes functions such as synchronous timing, synchronous data collection, and data transmission.

[0153] After being collected by the multi-band leakage current mining cable fault information acquisition device, two synchronously acquired signals are obtained: a high-frequency leakage current signal and a low-frequency leakage current signal. These two signals can generally be used as two signal inputs for feature mining and diagnosis in the fault diagnosis described below. Of course, the two signals can also be combined using data fusion principles.

[0154] There are at least two ways to fuse the high-frequency leakage current signal and the low-frequency leakage current signal, namely cutoff frequency fusion and weighted fusion based on the different response characteristics of the magnetic core to each frequency band; from the at least two fusion methods, one can be selected as the fusion method for the high-frequency leakage current signal and the low-frequency leakage current signal.

[0155] Specifically, the cutoff frequency fusion process is as follows: Because the response frequency of the above-mentioned low-frequency signal acquisition module is 30~1500Hz, the higher the frequency in the signal spectrum, the more distortion it will produce. The response frequency range of the above-mentioned medium and high-frequency signal acquisition module is 0.5kHz~50kHz, and the lower the frequency in the signal spectrum, the more distortion it will produce.

[0156] Select 1k as the cutoff frequency, and only extract the signal collected by the low-frequency acquisition module below 1kHz (inclusive) and define it as x ’ (t), and extract the part x''(t) above 1kHz of the signal collected by the high-frequency acquisition module, and fuse the signal x(t) = x ’ (t)+ x''(t).

[0157] The process of weighted fusion based on the different response characteristics of the magnetic core to each frequency band is as follows: suppose the signal of a certain frequency band of the real signal is f(t), and the signals collected by the low-frequency acquisition module and the high-frequency acquisition module in this frequency band are f'(t) and f''(t) respectively; since the high-frequency module and the low-frequency module have different response characteristics to the signal in this frequency band, weights α and β can be assigned respectively according to their characteristics; α+β=1; then the collected signal of this frequency band f(t)=α*f'(t)+β*f''(t).

[0158] Of course, the above two fusion methods are only examples, and you can choose the fusion method according to your needs.

[0159] The method of the present invention treats high-frequency leakage current signals and low-frequency leakage current signals as two homologous data of the same original signal, and extracts their respective features as input of the diagnostic model, which is equivalent to the same signal source. Since the present invention obtains two groups of parameter samples at the same time, each group of samples can be used for feature mining and diagnosis. Therefore, the number of training and diagnosis samples increases, and the accuracy of feature mining is improved. This method enriches the fault data input samples and improves the accuracy of model feature mining.

[0160] The multi-band leakage current mining cable fault information acquisition device of the present invention can separately collect low-frequency leakage current and high-frequency grounding wire leakage current in mining cables, realizing multi-band leakage current acquisition, solving the technical problems that the existing zero-sequence current sensor is insensitive to high-frequency response and the high-frequency sensor is insensitive to low-frequency current, as well as the lack of a dedicated multi-band leakage current acquisition device.

[0161] Of course, the above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above-mentioned embodiments. It should be noted that all equivalent substitutions and obvious deformation forms made by any technician familiar with this field under the guidance of this specification fall within the substantive scope of this specification and should be protected by the present invention.

Claims

1. A mining cable fault diagnosis system based on multi-band leakage current characteristics, comprising a multi-band leakage current mining cable fault information acquisition device and a host computer; the multi-band leakage current mining cable fault information acquisition device is used to collect multi-band leakage current signals of mining cables and transmit the collected multi-band leakage current signals of mining cables to the host computer; The multi-band leakage current mining cable fault information acquisition device comprises an equipment bracket, a grounding wire guide channel, a multi-band leakage current data acquisition unit for synchronously acquiring multi-band currents, and an acquisition data processing unit; The grounding wire guide channel is provided on the equipment support and has two ports for the mine cable grounding wire to pass through; The data acquisition processing unit is also installed on the equipment bracket; The multi-band leakage current data acquisition unit includes a low-frequency leakage current acquisition unit and a high-frequency leakage current acquisition unit; in, The low-frequency leakage current collection unit and the high-frequency leakage current collection unit are both arranged on the equipment bracket, and the low-frequency leakage current collection unit is located above the high-frequency leakage current collection unit and the grounding wire guide channel; The multi-band leakage current mining cable fault information collection device is arranged at the end of the mining cable; wherein the annular collection part of the low-frequency leakage current collection unit is arranged around the mining cable; The grounding wire led out from the end of the mining cable first passes back in the opposite direction through the gap formed between the mining cable and the ring-shaped collection part of the low-frequency leakage current collection unit, then leads out through the grounding wire guide channel and is grounded; The reverse direction here refers to the direction from the end of the mining cable where the multi-band leakage current mining cable fault information acquisition device is located to the other opposite end of the mining cable; The annular collection part of the high-frequency leakage current collection unit is arranged around the grounding wire guide channel; The low-frequency leakage current acquisition unit and the high-frequency leakage current acquisition unit are both connected to the acquisition data processing unit, and are used to send the corresponding low-frequency leakage current signal and high-frequency leakage current signal to the acquisition data processing unit respectively; The data collection processing unit of the multi-band leakage current mining cable fault information collection device is connected to the host computer; The host computer includes a memory and one or more processors; wherein the memory stores executable code; when the processor executes the executable code, the following method processing steps are performed: Step 1. Perform noise reduction preprocessing on the acquired multi-band leakage current signal of the mining cable; wherein the multi-band leakage current signal of the mining cable is obtained by combining the high-frequency leakage current signal and the low-frequency leakage current signal collected synchronously; The high-frequency leakage current signal and the low-frequency leakage current signal are two homologous data of the same original signal; Step 2. Extract features from the multi-band leakage current signal of the mining cable preprocessed in step 1, and label the extracted multi-band leakage current features of the mining cable to obtain a training data set; Step 3. Build a mining cable fault diagnosis model based on a convolutional neural network, and train the mining cable fault diagnosis model based on the training data set obtained in step 2 to obtain a trained mining cable fault diagnosis model; The multi-band leakage current signal of the mining cable to be diagnosed is subjected to noise reduction preprocessing according to the method of step 1, and after feature extraction in step 2, it is input into the trained mining cable fault diagnosis model to obtain the mining cable fault diagnosis result.

2. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: In the step 1, an improved microwave threshold denoising algorithm is used to implement noise reduction preprocessing of the multi-band leakage current signal; The specific process is as follows: Step 1.

1. Decomposition of original signal; First, the signal x(t) is decomposed into high-frequency coefficients representing high-frequency components and low-frequency coefficients representing low-frequency components; Where x(t) represents the obtained multi-band leakage current signal of mining cable; W j,k is the high-frequency coefficient of the jth layer and position k, representing the high-frequency part of the signal x(t); V J,k is the low-frequency coefficient at the Jth layer and position k, representing the low-frequency part of the signal x(t); j,k (t) is the basis function, is the magnitude function; Step 1.

2. Flexible dynamic threshold denoising; The high-frequency coefficient W obtained by decomposition in step 1.1 above j,k The following processing is performed, as shown in formulas (2) and (3); Formula (2) and Formula (3) are the denoising threshold formula and the optimization threshold formula respectively, and λ is the threshold parameter; Next, a flexible dynamic denoising threshold function is proposed, and the formula is as follows: Where x represents the high frequency coefficient W j,k , T(x) is the denoising threshold output, λ is the set dynamic threshold, ∈ is a positive number used to avoid division by zero, N represents the selected signal length, and a represents the number of signal data points; After T(x) is obtained, it is substituted into formula (2) as the high-frequency coefficient for signal reconstruction; Step 1.

3. Signal reconstruction; The processed decomposition coefficients are used to reconstruct the signal to obtain the denoised signal. The specific method is as follows: if the noise amplitude in the signal is large and a pure filtered signal is required, the optimized threshold coefficient obtained by formula (3) is used; if the noise amplitude in the signal is low and the details in the signal need to be retained, the flexible dynamic denoising threshold coefficient obtained by formula (4) is used. After denoising using the flexible dynamic threshold value obtained by formula (4) or the optimized threshold value by formula (3), the new coefficients are substituted into formula (1), and the inverse operation of formula (1) is performed to obtain the denoised signal S(i); 3. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: In step 2, the extracted multi-band leakage current characteristics of the mining cable include empirical characteristics corresponding to various types of faults, main frequency characteristics, odd-even ratio, harmonic ratio, spectrum center of gravity, total harmonic distortion, instantaneous frequency variation and energy entropy.

4. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: In step 3, the mining cable fault diagnosis result, i.e., the output of the mining cable fault diagnosis model, is divided into five types, i.e., no fault, single-phase grounding fault, high-resistance single-phase grounding fault, partial discharge, and two-phase short circuit.

5. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: In step 3, the training process of the mining cable fault diagnosis model is as follows: All the feature data of the multi-band leakage current characteristics of mining cables extracted in step 2 are used as input and input into the convolutional neural network. The convolutional neural network mines the corresponding relationship between each input feature and the deep feature of the fault according to the set fault type; By continuously adjusting the internal feature parameters of the convolutional neural network, the mining cable fault diagnosis model training is completed.

6. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: The low-frequency leakage current collection unit is designed as a clamp-shaped structure, that is, the annular collection part on the upper part is designed as an openable structure, and the lower part of the low-frequency leakage current collection unit is designed with a clamp-shaped handle for controlling the opening and closing of the openable structure; The multi-band leakage current mining cable fault information collection device further includes an insulating fixing bracket, wherein the insulating fixing bracket is arranged on the equipment bracket and is fixed to the mining cable using an adjustable tie; The insulating fixing bracket and the low-frequency leakage current acquisition unit are respectively located on opposite sides of the equipment bracket.

7. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: The annular collection part of the low-frequency leakage current collection unit includes a first shell, a first coil, a first magnetic core and a first output circuit; wherein the first shell is made of insulating material, and the first magnetic core is located inside the first shell; The overall outlines of the first shell and the first magnetic core are both ring-shaped; The first magnetic core is made of ferrite, silicon steel sheet or alloy material with good low-frequency response effect; A first coil is wound around the first magnetic core; wherein the first coil is connected to a first output circuit; the first output circuit is used to output a regulated voltage of the induced low-frequency leakage current and send the regulated voltage to the acquisition data processing unit through a signal line; The process of collecting low-frequency leakage current by the low-frequency leakage current acquisition unit is as follows: The first magnetic core is used to enclose the entire mining cable. The three phases A, B, and C of the mining cable and the ground wire are regarded as four lines. The currents in each line, namely the three-phase currents of the cable and the ground wire current, induce an alternating magnetic field on the magnetic core through the principle of electromagnetic induction. Since the ground wire is reversed during installation, the magnetic fields generated by the ground wire currents cancel each other out. Therefore, the first magnetic ring only contains the induced magnetic field of the three-phase currents, that is, the magnetic field induced by the algebraic sum of the three-phase currents. Under the action of the alternating magnetic field, the first coil wound on the first magnetic ring induces a low-frequency current; wherein, the first coil is connected to the first output circuit, and the low-frequency current is stabilized by the first output circuit and then sent to the acquisition data processing unit.

8. The mining cable fault diagnosis system based on multi-band leakage current characteristics according to claim 1 is characterized in that: The annular collection part of the high-frequency leakage current collection unit includes a second shell, a second coil, a second magnetic core and a second output circuit; wherein the second shell is made of insulating material, and the second magnetic core is located inside the second shell; The overall outlines of the second shell and the second magnetic core are both ring-shaped; The second magnetic core is made of Permalloy material which has good high frequency response effect; A second coil is wound around the second magnetic core; wherein the second coil is connected to a second output circuit; the second output circuit is used to output a regulated voltage of the induced high-frequency leakage current and send the regulated voltage to the data acquisition processing unit through a signal line; The process of collecting high-frequency leakage current by the high-frequency leakage current acquisition unit is as follows: The second magnetic core inserts the ground wire. The ground wire current induces an alternating magnetic field on the magnetic core through the principle of electromagnetic induction. Since the magnetic ring is sensitive to high-frequency signals, the second coil wound on the second magnetic ring induces a high-frequency current under the action of the alternating magnetic field. The second coil is connected to the output circuit, and the high-frequency current is sent to the acquisition data processing unit after being stabilized by the second output circuit.

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