Pump station circuit breaker fault detection method and system

Abstract

The invention relates to the technical field of power equipment fault diagnosis, in particular to a pump station circuit breaker fault detection method and system, and the method comprises the following steps: S1, collecting a vibration signal lower than 50 Hz in the operation process of a circuit breaker; s2, preprocessing the vibration data acquired in the S1; s3, analyzing the vibration data preprocessed in the S2, and extracting characteristic parameters related to low-frequency vibration of the circuit breaker; forming a feature vector by the extracted feature parameters; s4, training the feature mode training set to generate a circuit breaker fault mode recognition model; and S5, converting the current vibration data into a real-time feature vector, inputting the real-time feature vector into the fault mode recognition model generated in the step S4, judging whether the circuit breaker is abnormal or not, and generating a fault diagnosis report. According to the method, high-precision real-time detection and reliable diagnosis of the circuit breaker fault are realized through precise extraction of the low-frequency vibration signal and feature vector construction in combination with the optimized support vector machine fault mode recognition model.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment fault diagnosis, and in particular to a pump station circuit breaker fault detection method and system. Background Art

[0002] Pump station circuit breakers are important equipment to ensure safe operation of electricity in pump station systems. Their main function is to disconnect and protect circuits to prevent damage to equipment due to short circuits, overloads or other abnormal conditions. However, long-term operation of the mechanical parts of circuit breakers will cause wear, aging or loosening, which may cause mechanical failures or electrical abnormalities. Traditional circuit breaker maintenance methods rely on regular manual inspections or simple electrical parameter monitoring, which makes it difficult to detect early hidden dangers in a timely manner. Especially in critical continuous operation scenarios such as pump stations, traditional methods often have problems such as low efficiency and untimely detection, which can easily lead to the accumulation of hidden faults and ultimately pose a serious threat to the safety of equipment operation.

[0003] Although the existing technology has introduced some technical means based on vibration monitoring or data analysis in the field of circuit breaker fault detection, there are still the following technical problems that need to be solved: First, the monitored signal frequency band is usually concentrated in the high-frequency range, and the low-frequency vibration signal, as an important feature reflecting the failure of the mechanical components of the circuit breaker, is often not fully utilized; second, there is a lack of organic combination between data preprocessing, feature extraction and fault pattern recognition, resulting in insufficient reliability of the detection results; third, the classification accuracy and robustness of the existing fault detection model when processing the mapping relationship between complex vibration features and fault states still need to be improved. Therefore, it is of great technical significance and application value to develop a detection method and system that can comprehensively analyze low-frequency vibration signals and achieve high-precision fault identification. Summary of the invention

[0004] Based on the above objectives, the present invention provides a pump station circuit breaker fault detection method and system.

[0005] A pump station circuit breaker fault detection method comprises the following steps: S1: Install low-frequency vibration sensors at designated locations of the pump station circuit breakers to collect vibration signals below 50 Hz during the operation of the circuit breakers to form initial vibration data; S2: preprocessing the initial vibration data collected by S1, including removing noise, filtering and eliminating abnormal data, to obtain preprocessed vibration data; S3: Analyze the vibration data preprocessed by S2, extract characteristic parameters related to the low-frequency vibration of the circuit breaker, the characteristic parameters include vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics; and construct the extracted characteristic parameters into a characteristic vector; S4: Establish a feature pattern training set using historical circuit breaker operation data and historical fault data, and combine it with the feature vector in S3 to train the feature pattern training set using the support vector machine algorithm to generate a circuit breaker fault pattern recognition model; S5: Convert the current vibration data into a real-time feature vector and input it into the fault pattern recognition model generated by S4 to determine whether the circuit breaker is abnormal; when an abnormal pattern is detected, output the fault recognition result and record the corresponding feature parameters, and generate a fault diagnosis report.

[0006] Optionally, the S1 specifically includes: S11: Selecting a vibration signal sensitive area as the installation position of the sensor, wherein the sensitive area is a support frame of the circuit breaker or a fixed connection point of a switch operating mechanism; S12: At the installation position determined in S11, use threaded fasteners to fix the low-frequency vibration sensor to the surface of the circuit breaker, and use silicone to fill the gap around the sensor; S13: Connect a standard signal generator to simulate a vibration signal of 0 Hz to 50 Hz, adjust the sensitivity and output amplitude of the low-frequency vibration sensor, and ensure that the error of the vibration signal output by the low-frequency vibration sensor within the target frequency range is controlled within ±1%; S14: directly connect the vibration signal output by the low-frequency vibration sensor to a high-speed data recording device, and set the sampling frequency to 100 Hz; the collected vibration signal is continuously recorded in the form of amplitude and time series; S15: The vibration signal collected in S14 is saved in a digital format as initial vibration data.

[0007] Optionally, the S2 specifically includes: S21: using a noise removal method based on wavelet transform, the initial vibration data is decomposed into multiple layers of wavelet coefficients, and threshold filtering is performed on the high-frequency coefficient part to remove the background noise caused by environmental interference and retain the low-frequency signal component; S22: Using an FIR bandpass filter with a frequency range of 0.5 Hz to 50 Hz, the vibration data processed by S21 is filtered to remove non-target frequency components with frequencies lower than 0.5 Hz and higher than 50 Hz; S23: Apply a statistical method to perform abnormality detection on the vibration signal processed by S22, calculate the mean and standard deviation of the signal amplitude, determine the data points whose amplitude exceeds the range of two times the standard deviation of the mean as abnormal data, and eliminate the abnormal data; S24: The vibration signal obtained after removing the abnormal data in S23 is smoothed by a sliding average algorithm to eliminate small peaks caused by signal fluctuations.

[0008] Optionally, the S3 specifically includes: S31: Divide the vibration data preprocessed by S2 into segments according to time windows, calculate the amplitude range of the vibration signal in each segment, and calculate the mean and standard deviation of the amplitude range of all time windows as characteristic parameters of vibration amplitude fluctuation; S32: Perform fast Fourier transform on the vibration signal preprocessed by S2, extract the frequency components in the spectrum, and calculate the square sum of the amplitudes of each frequency component below 10 Hz as the characteristic parameter of the low-frequency harmonic intensity; S33: Based on the spectrum analysis of S32, determine the frequency point with the largest amplitude in the vibration signal as the resonance frequency; compare the resonance frequency with the reference resonance frequency when the circuit breaker is working normally, and calculate the frequency offset as the characteristic parameter of the resonance frequency offset; S34: Use Hilbert-Huang transform to perform nonlinear analysis on the vibration signal preprocessed by S2, decompose it to obtain several intrinsic mode functions IMF, and calculate the IMF energy proportion and kurtosis as characteristic parameters of the nonlinear characteristics of the vibration signal; S35: Arrange the characteristic parameters extracted from S31 to S34 in a fixed order, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics, to form a characteristic vector of four dimensions, where each dimension corresponds to a characteristic parameter.

[0009] Optionally, the S35 specifically includes: S351: normalizing all feature parameters extracted in S31 to S34, converting the original value of each feature parameter into a relative value so that its range is unified into a fixed interval; S352: in a fixed order, the normalized characteristic parameters are assigned to the four dimensions of the characteristic vector; the first dimension represents the comprehensive situation of the vibration amplitude fluctuation, which is calculated by combining the mean and standard deviation of the vibration amplitude fluctuation; the second dimension represents the low-frequency harmonic intensity, which is directly assigned by the normalized low-frequency harmonic intensity characteristic parameter; the third dimension represents the resonance frequency offset, which is assigned by the normalized resonance frequency offset characteristic; the fourth dimension is used to express the nonlinear characteristics of the vibration signal, which is determined by weighting the normalized energy proportion and kurtosis parameter; S353: Arrange the characteristic parameters of the above four dimensions in a predetermined order to form a four-dimensional characteristic vector, where each dimension of the characteristic vector represents the vibration amplitude fluctuation, the low-frequency harmonic intensity, the resonance frequency offset and the nonlinear characteristic.

[0010] Optionally, the S4 specifically includes: S41: Collecting historical operation data and historical fault data of the circuit breaker in normal operation and known fault states, wherein the collected data includes original records of vibration signals and corresponding operation condition labels, and the operation condition labels clearly mark normal states and different fault types; S42: pre-process the historical data collected in S41, using the same denoising, filtering and outlier removal methods as S2 to ensure that the quality of the historical data is consistent with the real-time data; S43: The historical data processed by S42 is processed according to the method of extracting characteristic parameters in S3, and parameters such as vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics are extracted to generate a characteristic vector consistent with S35, and the characteristic vector is matched with the operating condition label one by one to form a characteristic pattern training set; S44: Divide the feature pattern training set generated by S43 into a training set and a validation set in a ratio of 8:2, where 80% of the data is used to train the support vector machine model and 20% of the data is used to test the model performance; S45: inputting the feature vector extracted in S43 into a support vector machine algorithm, and training the support vector machine model using the data of the training set and its corresponding operating condition labels, so that the support vector machine model can identify the mapping relationship between the specified feature pattern and the circuit breaker fault state; S46: After completing the model training, the classification accuracy of the support vector machine model is verified through the verification set. If the accuracy meets the preset requirements, the model is saved as a circuit breaker fault mode recognition model.

[0011] Optionally, the S45 specifically includes: S451: define training parameters of the support vector machine model, including kernel function, penalty coefficient and tolerance parameter; S452: extracting feature vectors from the training set, and arranging the feature vectors of all samples into an input data matrix in the order of the samples; at the same time, extracting the operating condition label corresponding to each sample to construct an operating state label vector; S453: Setting a training objective of the support vector machine model so that the model can find a classification hyperplane in the high-dimensional space, which can maximize the interval of the classification boundary and reduce the classification error; S454: During the training process, the kernel function is used to map the input data from the original feature space to the high-dimensional feature space; and in the high-dimensional space, an optimal classification hyperplane is constructed by iteratively optimizing the weights and bias parameters of the model to distinguish different operating states; S455: Save the support vector machine model obtained by iterative optimization, which is used to identify the mapping relationship between the input feature vector and the circuit breaker fault state.

[0012] Optionally, the S46 specifically includes: S461: input the feature vectors in the validation set divided in S44 into the trained support vector machine model one by one, classify each input feature vector according to the classification decision function, and output the predicted operating condition label; S462: Record the actual operating condition label of each sample in the validation set and the prediction result of the support vector machine model to form a correspondence table between the actual label and the predicted label; S463: Perform statistics on the label correspondence table and calculate the ratio of the number of samples correctly classified by the model in the validation set to the total number of samples in the validation set as the classification accuracy of the model. The calculation formula is: ,in, Indicates classification accuracy; Indicates the number of samples classified correctly; Indicates the total number of samples in the validation set; S464: Compare the classification accuracy calculated in S463 with the preset classification accuracy threshold When compared, the classification accuracy Greater than or equal to the classification accuracy threshold , it is determined that the preset requirements are met, and the model that meets the requirements is saved as the circuit breaker fault mode recognition model; otherwise, return to S45 to readjust the model parameters and retrain.

[0013] Optionally, the S5 specifically includes: S51: collecting vibration data in real time and preprocessing it according to the same preprocessing method as in S2; then extracting four characteristic parameters of vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics from the preprocessed real-time vibration data according to the method in S3, and constructing a real-time characteristic vector; S52: input the constructed real-time feature vector into the fault mode recognition model generated by S4, and use its classification decision function to determine whether the current vibration data belongs to the defined fault mode; if the output classification result is a fault mode, mark the circuit breaker as abnormal; S53: recording the characteristic parameters extracted in S51, and clarifying the specific characteristics of the abnormal vibration signal, including the vibration amplitude fluctuation range, low-frequency harmonic intensity value, resonance frequency offset and nonlinear characteristic index; S54: Based on the abnormal characteristic parameters and model classification results recorded in S53, a fault diagnosis report is generated, wherein the report includes specific values ​​of the abnormal characteristic parameters, classification results of the fault mode, and the fault type.

[0014] A pump station circuit breaker fault detection system, used to implement the above-mentioned pump station circuit breaker fault detection method, includes the following modules: a vibration signal acquisition module: a low-frequency vibration sensor installed at a designated position of the pump station circuit breaker, used to collect vibration signals below 50 Hz in real time, and transmit the collected vibration signals in digital form to a signal processing module; a signal processing module: connected to the vibration signal acquisition module, used to pre-process the collected vibration signals, the pre-processing includes removing noise, filtering and eliminating abnormal data to generate processed vibration data; Feature extraction module: connected with the signal processing module, used to analyze the pre-processed vibration data, extract the characteristic parameters of the low-frequency vibration of the circuit breaker, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics, and construct the extracted characteristic parameters into a feature vector; Fault pattern recognition module: connected to the feature extraction module, used to receive feature vectors, and train with historical circuit breaker operation data and historical fault data to generate a circuit breaker fault pattern recognition model, which is used to determine whether the current feature vector belongs to the preset fault mode and output the fault detection result; Data recording and diagnosis module: connected to the fault mode recognition module, used to record the fault detection results output by the fault mode recognition module and generate a fault diagnosis report, which includes the specific values ​​of the abnormal characteristic parameters, the classification results of the fault mode and the fault type.

[0015] Beneficial effects of the present invention: The present invention can comprehensively capture the key vibration characteristics of the circuit breaker during operation, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonant frequency offset and nonlinear characteristics, through real-time collection, preprocessing and feature extraction of low-frequency vibration signals. Combined with data standardization processing methods and feature vector construction technology, the vibration signal is transformed from raw data to high-precision feature parameters, ensuring the consistency and reliability of input data. These key technologies make the representation of vibration signals more accurate and lay a data foundation for the accuracy of fault identification.

[0016] The present invention, by adopting support vector machine algorithm to train fault pattern recognition model, combined with classification optimization and kernel function mapping technology, can accurately identify the mapping relationship between low-frequency vibration characteristics of circuit breakers and different fault states. At the same time, the model classification accuracy and error level are strictly verified through the verification set to ensure the robustness and generalization ability of the fault identification model. The overall technical solution effectively improves the real-time and accuracy of fault detection, and provides technical support for the safe operation and precise maintenance of circuit breakers. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a circuit breaker fault detection method according to an embodiment of the present invention; Figure 2 Schematic diagram of a circuit breaker fault detection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0020] like Figure 1 As shown, a pump station circuit breaker fault detection method includes the following steps: S1: Install low-frequency vibration sensors at designated locations of the pump station circuit breakers to collect vibration signals below 50 Hz during the operation of the circuit breakers to form initial vibration data; S2: preprocessing the initial vibration data collected by S1, including removing noise, filtering and eliminating abnormal data, to obtain preprocessed vibration data; S3: Analyze the vibration data preprocessed by S2, extract characteristic parameters related to the low-frequency vibration of the circuit breaker, the characteristic parameters include vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics; and construct the extracted characteristic parameters into a characteristic vector; S4: Establish a feature pattern training set using historical circuit breaker operation data and historical fault data, and combine it with the feature vector in S3 to train the feature pattern training set using the support vector machine algorithm to generate a circuit breaker fault pattern recognition model; S5: Convert the current vibration data into a real-time feature vector and input it into the fault pattern recognition model generated by S4 to determine whether the circuit breaker is abnormal; when an abnormal pattern is detected, output the fault recognition result and record the corresponding feature parameters, and generate a fault diagnosis report.

[0021] S1 specifically includes: S11: Select a vibration signal sensitive area as the installation location of the sensor. The sensitive area is the supporting frame of the circuit breaker or the fixed connection point of the switch operating mechanism to ensure that the low-frequency vibration signal can be effectively transmitted to the sensor; S12: At the installation position determined by S11, the low-frequency vibration sensor is fixed to the circuit breaker surface with threaded fasteners, and the gap around the sensor is filled with silicone to enhance the fixing stability of the sensor and the signal transmission efficiency; S13: Connect a standard signal generator to simulate a vibration signal of 0 Hz to 50 Hz, adjust the sensitivity and output amplitude of the low-frequency vibration sensor, and ensure that the error of the vibration signal output by the low-frequency vibration sensor within the target frequency range is controlled within ±1%; S14: directly connect the vibration signal output by the low-frequency vibration sensor to a high-speed data recording device, and set the sampling frequency to 100 Hz to ensure accurate collection of vibration signals below 50 Hz; the collected vibration signal is continuously recorded in the form of amplitude and time series; S15: Save the vibration signal collected by S14 in digital format as initial vibration data. The initial vibration data includes the amplitude, frequency and timestamp information of the vibration, which provides a basis for the subsequent processing and feature extraction of vibration data. The above steps ensure that the sensor can accurately capture low-frequency vibration signals by clarifying the installation position, adopting a reliable fixing method and calibration technology. The vibration signal is directly collected to a high-speed recording device and stored in a digital format to avoid loss and distortion in signal transmission, providing a high-quality vibration data basis for subsequent steps, and significantly improving the accuracy and reliability of fault detection.

[0022] S2 specifically includes: S21: using a noise removal method based on wavelet transform, the initial vibration data is decomposed into multiple layers of wavelet coefficients, and threshold filtering is performed on the high-frequency coefficient part to remove the background noise caused by environmental interference and retain the low-frequency signal component; S22: Use a FIR (finite impulse response) bandpass filter with a frequency range of 0.5 Hz to 50 Hz to filter the vibration data processed by S21 to remove non-target frequency components with frequencies below 0.5 Hz and above 50 Hz, ensuring that the retained vibration signal is concentrated in the frequency band related to the low-frequency vibration of the circuit breaker; S23: Apply a statistical method to perform anomaly detection on the vibration signal processed by S22, calculate the mean and standard deviation of the signal amplitude, determine the data points whose amplitude exceeds the range of two times the standard deviation of the mean as abnormal data, and remove the abnormal data to ensure the authenticity and continuity of the vibration data; S24: The vibration signal obtained after removing abnormal data in S23 is smoothed using a sliding average algorithm to eliminate small spikes caused by signal fluctuations, further improving the stability and accuracy of the data; the above steps remove environmental noise through wavelet transform to effectively reduce the impact of background interference on low-frequency vibration signals; use a bandpass filter to accurately extract the vibration signal of the target frequency band to enhance the effective components of the signal; by removing abnormal data and smoothing, the continuity and credibility of the vibration data are improved, providing high-quality preprocessing data for subsequent feature extraction, significantly improving the accuracy and robustness of fault detection.

[0023] S3 specifically includes: S31: Divide the vibration data preprocessed by S2 into segments according to time windows, calculate the amplitude range of the vibration signal in each segment (maximum value minus minimum value), and calculate the mean and standard deviation of the amplitude range of all time windows as characteristic parameters of vibration amplitude fluctuation; The mean value of the vibration amplitude fluctuation is given by: ; The standard deviation of vibration amplitude fluctuation is given by: ,in, and Respectively represent The maximum and minimum values ​​of the vibration signal in the mouth over a period of time; Indicates the total number of time windows; Indicates the mean value of vibration amplitude fluctuation; Indicates the standard deviation of vibration amplitude fluctuations; S32: Perform fast Fourier transform on the vibration signal preprocessed by S2, extract the frequency components in the spectrum, and calculate the square sum of the amplitudes of the frequency components below 10 Hz as the characteristic parameter of the low-frequency harmonic intensity to characterize the low-frequency energy characteristics of the circuit breaker vibration signal; the specific calculation formula is: ,in, Indicates the intensity of low-frequency harmonics; Indicates frequency The corresponding vibration signal amplitude; S33: Based on the spectrum analysis of S32, determine the frequency point with the largest amplitude in the vibration signal as the resonant frequency; compare the resonant frequency with the reference resonant frequency when the circuit breaker is working normally, and calculate the frequency offset as the characteristic parameter of the resonant frequency offset; the calculation formula of the resonant frequency offset is: ,in, Indicates the shift of the resonant frequency; Indicates the current resonance frequency, that is, the frequency point with the largest amplitude in the vibration signal; Indicates the reference resonant frequency; S34: It is proposed to use Hilbert-Huang transform (HHT) to perform nonlinear analysis on the vibration signal preprocessed by S2, decompose it to obtain several intrinsic mode functions IMF, and calculate the IMF energy proportion and kurtosis as the characteristic parameters of the nonlinear characteristics of the vibration signal; the formula of IMF energy proportion is: ; The formula for IMF kurtosis is: ;in, Indicates The energy share of each IMF; Indicates The kurtosis of an IMF; and Respectively represent the start time and end time of the signal; is the signal duration; is the total number of IMF obtained by decomposition; S35: Arrange the characteristic parameters extracted from S31 to S34 in a fixed order, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonant frequency offset and nonlinear characteristics, to form a four-dimensional characteristic vector, where each dimension corresponds to a characteristic parameter as a characteristic expression of the circuit breaker low-frequency vibration signal.

[0024] S35 specifically includes: S351: normalize all characteristic parameters extracted from S31 to S34, convert the original value of each characteristic parameter into a relative value, and unify its range to a fixed interval; during the normalization process, by comparing the minimum and maximum values ​​of the characteristic parameters, map the actual values ​​of the characteristic parameters to the standard range, ensuring that the dimensions of different characteristic parameters are consistent, which is convenient for subsequent operations; S352: assign the normalized characteristic parameters to the four dimensions of the characteristic vector in a fixed order; the first dimension represents the comprehensive situation of the vibration amplitude fluctuation, which is calculated by combining the mean and standard deviation of the vibration amplitude fluctuation; the second dimension represents the low-frequency harmonic intensity, which is directly assigned by the normalized low-frequency harmonic intensity characteristic parameter; the third dimension represents the resonance frequency offset, which is assigned by the normalized resonance frequency offset feature; the fourth dimension is used to express the nonlinear characteristics of the vibration signal, which is determined by weighting the normalized energy proportion and kurtosis parameter; S353: Arrange the characteristic parameters of the above four dimensions in a predetermined order to form a four-dimensional characteristic vector, where each dimension of the characteristic vector represents vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics; and perform consistency check on the constructed characteristic vector to verify whether the normalized range of the characteristic parameters meets the standard and whether the parameters of each dimension are correctly mapped to the corresponding characteristic category, so as to ensure that the characteristic vector is accurate; The calculation steps to form the feature vector of four dimensions are as follows: First, each feature parameter is normalized as follows: ,in, Indicates the original value of the characteristic parameter; and are the minimum and maximum values ​​of the feature parameters in the sample data set, respectively; represents the normalized eigenvalue; Then, the normalized feature parameters are assigned to the four dimensions of the feature vector in a specific order, including: the first dimension is , represents the mean value of the vibration amplitude fluctuation and standard deviation The average expression is: The second dimension is , represents the low-frequency harmonic intensity, and the expression is: ;in, is the normalized low-frequency harmonic intensity; The third dimension is , which represents the resonant frequency shift , the expression is: ;in, is the normalized frequency offset; The fourth dimension is , which represents a comprehensive index of nonlinear characteristics, by calculating the energy ratio Kurtosis The weighted average of is obtained, the formula is: ,in, and is the weight of energy proportion and kurtosis, satisfying ; is the normalized energy proportion; is the normalized kurtosis; Next, the four feature dimensions are assigned in order to form a four-dimensional feature vector , expressed as: ; Finally, the constructed feature vector Verify that there are no omissions or redundancies between feature dimensions; verification includes checking whether the normalized range meets , and whether the features of each dimension are correctly mapped to the corresponding parameters.

[0025] The circuit breaker fault mode recognition model generated in S4 specifically includes: S41: collecting historical operation data and historical fault data of the circuit breaker in normal operation and known fault states, wherein the collected data includes original records of vibration signals and corresponding operation condition labels, and the operation condition labels clearly mark normal states and different fault types; S42: pre-process the historical data collected in S41, using the same denoising, filtering and outlier removal methods as S2 to ensure that the quality of the historical data is consistent with the real-time data and that the data can be used for subsequent feature extraction and model training; S43: The historical data processed by S42 is processed according to the method of extracting characteristic parameters in S3, and parameters such as vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics are extracted to generate a characteristic vector consistent with S35, and the characteristic vector is matched with the operating condition label one by one to form a characteristic pattern training set; S44: Divide the feature pattern training set generated by S43 into a training set and a validation set in a ratio of 8:2, where 80% of the data is used to train the support vector machine model and 20% of the data is used to test the model performance; S45: inputting the feature vector extracted in S43 into a support vector machine algorithm, and training the support vector machine model using the data of the training set and its corresponding operating condition labels, so that the support vector machine model can identify the mapping relationship between the specified feature pattern and the circuit breaker fault state; S46: After completing the model training, the classification accuracy of the support vector machine model is verified through the verification set. If the accuracy meets the preset requirements, the model is saved as a circuit breaker fault pattern recognition model for subsequent real-time data fault detection.

[0026] S45 specifically includes: S451: Define the training parameters of the support vector machine model, including kernel function, penalty coefficient and tolerance parameter; the kernel function is used to map the input feature vector to a high-dimensional space; the penalty coefficient is used to balance the model's tolerance to training errors and model complexity; the tolerance parameter determines the convergence accuracy of the model during optimization; S452: extract feature vectors from the training set, and arrange the feature vectors of all samples into an input data matrix in the order of the samples; at the same time, extract the operating condition label corresponding to each sample to construct an operating state label vector; wherein the input data matrix represents the feature information of the sample, and the operating state label vector represents the operating state to which each sample belongs; S453: Setting the training objective of the support vector machine model so that the model can find a classification hyperplane in the high-dimensional space, which can maximize the interval of the classification boundary while reducing the classification error; to this end, the training process simultaneously considers the distribution of input features and the classification requirements of sample labels; S454: During the training process, the kernel function is used to map the input data from the original feature space to the high-dimensional feature space; and in the high-dimensional space, an optimal classification hyperplane is constructed by iteratively optimizing the weights and bias parameters of the model to distinguish different operating states; S455: Save the support vector machine model parameters obtained by iterative optimization, including model weights, biases, and support vector sets, for identifying the mapping relationship between the input feature vector and the circuit breaker fault state.

[0027] The computational steps for training the circuit breaker fault pattern recognition model are as follows: Construct the input feature matrix and label vector: Arrange the feature vectors in the training set according to the samples to form the input feature matrix, which is defined as , where each eigenvector Contains four feature dimensions; at the same time, the operating condition label of each sample in the training set is extracted to form a label vector ,in is the corresponding running status label, which can be positive, negative or multi-class classification; Optimizing the Support Vector Machine Objective Function: Using the Input Feature Matrix and label vector , the model is trained according to the following optimization objective function: ; Constraints: ; ; ,in, is the weight vector of the classification hyperplane, is the bias, is the feature vector after kernel function mapping, is the slack variable, is the penalty coefficient; Kernel function mapping and classification hyperplane construction: Using kernel function Map the input feature vector to a high-dimensional feature space, and the kernel function is defined as: ; Calculate the classification hyperplane parameters based on the optimization results and , forming a classification decision function: ; The above steps ensure the training efficiency and accuracy of the model by clarifying the initialization parameters and optimization objectives of the support vector machine; constructing the correspondence between the input features and the operating condition labels to provide the model with a clear data input and output logic; and enhancing the classification ability of the model in complex feature space through kernel function mapping. The support vector machine model finally generated can accurately identify the correlation between the specific feature patterns of the circuit breaker and the fault status, providing a reliable classification basis for subsequent real-time detection.

[0028] S46 specifically includes: S461: input the feature vectors in the validation set divided in S44 into the trained support vector machine model one by one, classify each input feature vector according to the classification decision function, and output the predicted operating condition label; S462: Record the actual operating condition label of each sample in the validation set and the prediction result of the support vector machine model to form a correspondence table between the actual label and the predicted label for subsequent calculation of classification accuracy and error level; S463: Perform statistics on the label correspondence table and calculate the ratio of the number of samples correctly classified by the model in the validation set to the total number of samples in the validation set as the classification accuracy of the model. The calculation formula is: ,in, Indicates classification accuracy; Indicates the number of samples classified correctly; Indicates the total number of samples in the validation set; S464: Compare the classification accuracy calculated in S463 with the preset classification accuracy threshold When compared, the classification accuracy Greater than or equal to the classification accuracy threshold , it is determined that the preset requirements are met, and the model that meets the requirements is saved as the circuit breaker fault pattern recognition model; otherwise, return to S45 to readjust the model parameters and retrain; by setting the classification accuracy threshold, ensure that the accuracy of the model reaches the expected goal, and retrain and optimize when the requirements are not met to improve the robustness and reliability of the model; the fault pattern recognition model finally generated has high accuracy and low error level, providing a reliable classification basis for subsequent real-time fault detection.

[0029] S5 specifically includes: S51: collecting vibration data in real time and preprocessing it according to the same preprocessing method as in S2; then extracting four characteristic parameters of vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics from the preprocessed real-time vibration data according to the method in S3, and constructing a real-time characteristic vector; S52: input the constructed real-time feature vector into the fault mode recognition model generated by S4, and use its classification decision function to determine whether the current vibration data belongs to the defined fault mode; if the output classification result is a fault mode, mark the circuit breaker as abnormal; S53: Record the characteristic parameters extracted in S51 to clarify the specific characteristics of the abnormal vibration signal, including the vibration amplitude fluctuation range, low-frequency harmonic intensity value, resonance frequency offset and nonlinear characteristic index, for subsequent fault cause analysis; S54: Based on the abnormal characteristic parameters and model classification results recorded in S53, a fault diagnosis report is generated, which includes the specific values ​​of the abnormal characteristic parameters, the classification results of the fault mode and the fault type; and the diagnosis report is stored for subsequent maintenance and reference; the above steps quickly determine whether the circuit breaker has an abnormality through the classification function of the fault mode recognition model; record the abnormal characteristic parameters and generate a diagnosis report, which provides a detailed basis for fault location and maintenance, realizes real-time and accurate detection and record management of circuit breaker faults, and significantly improves maintenance efficiency and system operation reliability.

[0030] like Figure 2 As shown, a pump station circuit breaker fault detection system is used to implement the above-mentioned pump station circuit breaker fault detection method, including the following modules: Vibration signal acquisition module: A low-frequency vibration sensor installed at a designated location of the pump station circuit breaker, used to collect vibration signals below 50 Hz in real time and transmit the collected vibration signals in digital form to the signal processing module; Signal processing module: connected to the vibration signal acquisition module, used to pre-process the collected vibration signal, including noise removal, filtering and abnormal data removal to generate processed vibration data; Feature extraction module: connected with the signal processing module, used to analyze the pre-processed vibration data, extract the characteristic parameters of the low-frequency vibration of the circuit breaker, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics, and construct the extracted characteristic parameters into a feature vector; Fault pattern recognition module: connected to the feature extraction module, used to receive feature vectors, and train with historical circuit breaker operation data and historical fault data to generate a circuit breaker fault pattern recognition model, which is used to determine whether the current feature vector belongs to the preset fault mode and output the fault detection result; Data recording and diagnosis module: connected to the fault mode recognition module, used to record the fault detection results output by the fault mode recognition module and generate a fault diagnosis report, which includes the specific values ​​of the abnormal characteristic parameters, the classification results of the fault mode and the fault type.

[0031] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0032] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A pump station circuit breaker fault detection method, characterized in that: The following steps are involved: S1: Install low-frequency vibration sensors at designated locations of the pump station circuit breakers to collect vibration signals below 50 Hz during the operation of the circuit breakers to form initial vibration data; S2: preprocessing the initial vibration data collected by S1, including removing noise, filtering and eliminating abnormal data, to obtain preprocessed vibration data; S3: Analyze the vibration data preprocessed by S2, extract characteristic parameters related to the low-frequency vibration of the circuit breaker, the characteristic parameters include vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics; and construct the extracted characteristic parameters into a characteristic vector; S4: Establish a feature pattern training set using historical circuit breaker operation data and historical fault data, and combine it with the feature vector in S3 to train the feature pattern training set using the support vector machine algorithm to generate a circuit breaker fault pattern recognition model; S5: convert the current vibration data into a real-time feature vector and input it into the fault mode recognition model generated by S4 to determine whether the circuit breaker is abnormal; When an abnormal pattern is detected, the fault identification result is output and the corresponding characteristic parameters are recorded, and a fault diagnosis report is generated.

2. A pump station circuit breaker fault detection method according to claim 1, characterized in that: The S1 specifically includes: S11: Selecting a vibration signal sensitive area as the installation position of the sensor, wherein the sensitive area is a support frame of the circuit breaker or a fixed connection point of a switch operating mechanism; S12: At the installation position determined in S11, use threaded fasteners to fix the low-frequency vibration sensor to the surface of the circuit breaker, and use silicone to fill the gap around the sensor; S13: Connect a standard signal generator to simulate a vibration signal of 0 Hz to 50 Hz, adjust the sensitivity and output amplitude of the low-frequency vibration sensor, and ensure that the error of the vibration signal output by the low-frequency vibration sensor within the target frequency range is controlled within ±1%; S14: directly connect the vibration signal output by the low-frequency vibration sensor to a high-speed data recording device, and set the sampling frequency to 100 Hz; the collected vibration signal is continuously recorded in the form of amplitude and time series; S15: The vibration signal collected in S14 is saved in a digital format as initial vibration data.

3. A pump station circuit breaker fault detection method according to claim 1, characterized in that: The S2 specifically includes: S21: using a noise removal method based on wavelet transform, the initial vibration data is decomposed into multiple layers of wavelet coefficients, and threshold filtering is performed on the high-frequency coefficient part to remove the background noise caused by environmental interference and retain the low-frequency signal component; S22: Filter the vibration data processed by S21 using an FIR bandpass filter with a frequency range of 0.5 Hz to 50 Hz to remove non-target frequency components with frequencies lower than 0.5 Hz and higher than 50 Hz; S23: Apply a statistical method to perform abnormality detection on the vibration signal processed by S22, calculate the mean and standard deviation of the signal amplitude, determine the data points whose amplitude exceeds the range of two times the standard deviation of the mean as abnormal data, and eliminate the abnormal data; S24: The vibration signal obtained after removing the abnormal data in S23 is smoothed by a sliding average algorithm to eliminate small peaks caused by signal fluctuations.

4. A pump station circuit breaker fault detection method according to claim 1, characterized in that: The S3 specifically includes: S31: Divide the vibration data preprocessed by S2 into segments according to time windows, calculate the amplitude range of the vibration signal in each segment, and calculate the mean and standard deviation of the amplitude range of all time windows as characteristic parameters of vibration amplitude fluctuation; S32: Perform fast Fourier transform on the vibration signal preprocessed by S2, extract the frequency components in the spectrum, and calculate the square sum of the amplitudes of each frequency component below 10 Hz as the characteristic parameter of the low-frequency harmonic intensity; S33: Based on the spectrum analysis of S32, determine the frequency point with the largest amplitude in the vibration signal as the resonance frequency; compare the resonance frequency with the reference resonance frequency when the circuit breaker is working normally, and calculate the frequency offset as the characteristic parameter of the resonance frequency offset; S34: Use Hilbert-Huang transform to perform nonlinear analysis on the vibration signal preprocessed by S2, decompose it to obtain several intrinsic mode functions IMF, and calculate the IMF energy proportion and kurtosis as characteristic parameters of the nonlinear characteristics of the vibration signal; S35: Arrange the characteristic parameters extracted from S31 to S34 in a fixed order, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics, to form a characteristic vector of four dimensions, where each dimension corresponds to a characteristic parameter.

5. A pump station circuit breaker fault detection method according to claim 4, characterized in that: The S35 specifically includes: S351: normalizing all feature parameters extracted in S31 to S34, converting the original value of each feature parameter into a relative value so that its range is unified into a fixed interval; S352: in a fixed order, the normalized characteristic parameters are assigned to the four dimensions of the characteristic vector; the first dimension represents the comprehensive situation of the vibration amplitude fluctuation, which is calculated by combining the mean and standard deviation of the vibration amplitude fluctuation; the second dimension represents the low-frequency harmonic intensity, which is directly assigned by the normalized low-frequency harmonic intensity characteristic parameter; the third dimension represents the resonance frequency offset, which is assigned by the normalized resonance frequency offset characteristic; the fourth dimension is used to express the nonlinear characteristics of the vibration signal, which is determined by weighting the normalized energy proportion and kurtosis parameter; S353: Arrange the characteristic parameters of the above four dimensions in a predetermined order to form a four-dimensional characteristic vector, where each dimension of the characteristic vector represents the vibration amplitude fluctuation, the low-frequency harmonic intensity, the resonance frequency offset and the nonlinear characteristic.

6. A pump station circuit breaker fault detection method according to claim 4, characterized in that: The S4 specifically includes: S41: Collecting historical operation data and historical fault data of the circuit breaker in normal operation and known fault states, wherein the collected data includes original records of vibration signals and corresponding operation condition labels, and the operation condition labels clearly mark normal states and different fault types; S42: pre-process the historical data collected in S41, using the same denoising, filtering and outlier removal methods as S2 to ensure that the quality of the historical data is consistent with the real-time data; S43: The historical data processed by S42 is processed according to the method of extracting characteristic parameters in S3, and parameters such as vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics are extracted to generate a characteristic vector consistent with S35, and the characteristic vector is matched with the operating condition label one by one to form a characteristic pattern training set; S44: Divide the feature pattern training set generated by S43 into a training set and a validation set in a ratio of 8:2, where 80% of the data is used to train the support vector machine model and 20% of the data is used to test the model performance; S45: inputting the feature vector extracted in S43 into a support vector machine algorithm, and training the support vector machine model using the data of the training set and its corresponding operating condition labels, so that the support vector machine model can identify the mapping relationship between the specified feature pattern and the circuit breaker fault state; S46: After completing the model training, the classification accuracy of the support vector machine model is verified through the verification set. If the accuracy meets the preset requirements, the model is saved as a circuit breaker fault mode recognition model.

7. A pump station circuit breaker fault detection method according to claim 6, characterized in that: The S45 specifically includes: S451: define training parameters of the support vector machine model, including kernel function, penalty coefficient and tolerance parameter; S452: extracting feature vectors from the training set, and arranging the feature vectors of all samples into an input data matrix in the order of the samples; at the same time, extracting the operating condition label corresponding to each sample to construct an operating state label vector; S453: Setting a training objective of the support vector machine model so that the model can find a classification hyperplane in the high-dimensional space, which can maximize the interval of the classification boundary and reduce the classification error; S454: During the training process, the kernel function is used to map the input data from the original feature space to the high-dimensional feature space; and in the high-dimensional space, an optimal classification hyperplane is constructed by iteratively optimizing the weights and bias parameters of the model to distinguish different operating states; S455: Save the support vector machine model obtained by iterative optimization, which is used to identify the mapping relationship between the input feature vector and the circuit breaker fault state.

8. A pump station circuit breaker fault detection method according to claim 7, characterized in that: The S46 specifically includes: S461: input the feature vectors in the validation set divided in S44 into the trained support vector machine model one by one, classify each input feature vector according to the classification decision function, and output the predicted operating condition label; S462: Record the actual operating condition label of each sample in the validation set and the prediction result of the support vector machine model to form a correspondence table between the actual label and the predicted label; S463: Perform statistics on the label correspondence table and calculate the ratio of the number of samples correctly classified by the model in the validation set to the total number of samples in the validation set as the classification accuracy of the model. The calculation formula is: ,in, Indicates classification accuracy; Indicates the number of samples classified correctly; Indicates the total number of samples in the validation set; S464: Compare the classification accuracy calculated in S463 with the preset classification accuracy threshold When compared, the classification accuracy Greater than or equal to the classification accuracy threshold , it is determined that the preset requirements are met, and the model that meets the requirements is saved as the circuit breaker fault mode recognition model; otherwise, return to S45 to readjust the model parameters and retrain.

9. A pump station circuit breaker fault detection method according to claim 1, characterized in that: The S5 specifically includes: S51: collecting vibration data in real time and preprocessing it according to the same preprocessing method as in S2; then extracting four characteristic parameters of vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics from the preprocessed real-time vibration data according to the method in S3, and constructing a real-time characteristic vector; S52: input the constructed real-time feature vector into the fault mode recognition model generated by S4, and use its classification decision function to determine whether the current vibration data belongs to the defined fault mode; if the output classification result is a fault mode, mark the circuit breaker as abnormal; S53: recording the characteristic parameters extracted in S51, and clarifying the specific characteristics of the abnormal vibration signal, including the vibration amplitude fluctuation range, low-frequency harmonic intensity value, resonance frequency offset and nonlinear characteristic index; S54: Based on the abnormal characteristic parameters and model classification results recorded in S53, a fault diagnosis report is generated, wherein the report includes specific values ​​of the abnormal characteristic parameters, classification results of the fault mode, and the fault type.

10. A pump station circuit breaker fault detection system, used to implement a pump station circuit breaker fault detection method according to any one of claims 1 to 9, characterized in that: Includes the following modules: Vibration signal acquisition module: a low-frequency vibration sensor installed at a designated location of the pump station circuit breaker, used to collect vibration signals below 50 Hz in real time, and transmit the collected vibration signals in digital form to the signal processing module; signal processing module: connected to the vibration signal acquisition module, used to pre-process the collected vibration signals, the pre-processing includes noise removal, filtering and elimination of abnormal data to generate processed vibration data; Feature extraction module: connected to the signal processing module, used to analyze the pre-processed vibration data, extract the characteristic parameters of the circuit breaker's low-frequency vibration, including vibration amplitude fluctuation, low-frequency harmonic intensity, resonance frequency offset and nonlinear characteristics, and construct the extracted characteristic parameters into a feature vector; Fault mode recognition module: connected to the feature extraction module, used to receive the feature vector, and train it in combination with the historical circuit breaker operation data and historical fault data, and then generate a circuit breaker fault mode recognition model, which is used to determine whether the current feature vector belongs to the preset fault mode, and output the fault detection result; Data recording and diagnosis module: connected to the fault mode recognition module, used to record the fault detection results output by the fault mode recognition module and generate a fault diagnosis report, which includes the specific values ​​of the abnormal characteristic parameters, the classification results of the fault mode and the fault type.

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