Mechanical State Fault Diagnosis Method for Circuit Breaker Operating Mechanism

Abstract

The invention discloses a method for diagnosing mechanical state faults of a circuit breaker operating mechanism, belonging to the technical field of circuit breakers. The method for diagnosing mechanical state faults of a circuit breaker operating mechanism includes the following steps: Step 1: Obtain the historical mechanical performance parameters of the circuit breaker operating mechanism; Step 2: Establish a health model of the circuit breaker operating mechanism; Step 3: Collect the operation data of the circuit breaker operating mechanism; Step 4: Establish a feature extraction algorithm according to the extracted data; Step 5: Obtain the mechanical performance parameters of the circuit breaker operating mechanism; Step 6: Diagnose the faults of the circuit breaker operating mechanism. The invention solves the problem that the existing patents cannot diagnose faults according to the operating state of the circuit breaker operating mechanism, resulting in the inability to give corresponding early warnings according to the fault conditions of the circuit breaker operating mechanism. The invention ensures the richness and key nature of the feature information quantity extracted from the mechanical state of the circuit breaker operating mechanism, and ensures the accuracy of fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breakers, and particularly to a method for diagnosing mechanical state faults of a circuit breaker operating mechanism. Background Art

[0002] A circuit breaker is an important component device in the power grid, which has a great influence on regional power supply. If it fails during operation, it may lead to insufficient power supply or power outage in most areas, and may even cause great losses to society and the economy.

[0003] Chinese Patent with publication number CN105930622B discloses a method for determining the motion state of a spring operating mechanism of a circuit breaker, including: 1. determining three parameters: the stiffness, free length, and initial pressure length of the closing and opening springs; 2. calculating the restoring force and the released operating work of the closing spring according to the stiffness, free length, and initial pressure length of the closing spring at any moment during the closing operation of the circuit breaker; 3. calculating the restoring force and the consumed work of the opening spring according to the stiffness, free length, and initial pressure length of the opening spring; 4. obtaining the buffering resistance of the buffer by simulating with Fluent software and calculating its restoring force and the consumed work; 5. reducing the motion resistance of the arc extinguishing chamber and calculating the consumed work thereof; 6. obtaining the resultant force at the reduced position and the remaining work of the system according to steps 2-5; 7. determining the motion state of the spring operating mechanism according to the calculation results of step 6. This patent accurately and effectively describes the motion state of the spring operating mechanism during the closing operation of the circuit breaker, overcomes the limitations of the previous methods such as low efficiency, low accuracy, and inability to reflect the dynamic changes during the closing process, and provides a good theoretical basis for exploring the reliable operation of the spring operating mechanism.

[0004] The above patent can only judge the motion state of the circuit breaker operating mechanism during actual use, but cannot diagnose faults according to the operating state of the circuit breaker operating mechanism, resulting in the inability to give corresponding warnings according to the fault conditions of the circuit breaker operating mechanism; therefore, it does not meet the existing requirements, and for this reason, we propose a method for diagnosing mechanical state faults of a circuit breaker operating mechanism. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for diagnosing mechanical state faults of a circuit breaker operating mechanism, which extracts features of the mechanical state of the circuit breaker operating mechanism by using multi-scale entropy, and at the same time avoids the redundancy of feature quantity decomposition during feature extraction of the mechanical state of the circuit breaker operating mechanism, and evaluates the mechanical state of the circuit breaker operating mechanism by using the intuitionistic analytic hierarchy process, ensuring the accuracy of fault diagnosis and solving the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for diagnosing mechanical state faults of a circuit breaker operating mechanism, including the following steps:

[0007] Step 1: Obtain the historical mechanical characteristics of the circuit breaker operating mechanism, analyze the obtained mechanical characteristics of the circuit breaker operating mechanism, and obtain the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions;

[0008] Step 2: Establish a health model for the circuit breaker operating mechanism based on the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions;

[0009] Step 3: Real-time collect the operation data of the circuit breaker operating mechanism through sensors, and then preprocess the collected data. The preprocessing includes cleaning, transforming, and anomaly detection processing of the collected data;

[0010] Step 4: Extract the characteristic parameters related to the mechanical performance of the circuit breaker operating mechanism from the processed operation data of the circuit breaker operating mechanism, and establish a feature extraction algorithm based on the extracted characteristic parameters related to the mechanical performance of the circuit breaker operating mechanism;

[0011] Step 5: Conduct a quantitative analysis of the mechanical characteristics according to the established feature extraction algorithm, and through simulation, obtain the real-time mechanical performance parameters of the circuit breaker operating mechanism;

[0012] Step 6: Analyze the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism, extract the real-time characteristic parameters of the circuit breaker operating mechanism, input the extracted real-time characteristic parameters into the health model of the circuit breaker operating mechanism, calculate the health score of the circuit breaker operating mechanism, and diagnose the faults of the circuit breaker operating mechanism according to the health score of the circuit breaker operating mechanism.

[0013] Preferably, the establishment of the health model for the circuit breaker operating mechanism based on the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions specifically includes:

[0014] Obtain the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions, conduct model training through the historical mechanical performance parameters. During the training process, by minimizing the loss function, use an optimization algorithm to adjust the weights and parameters of the model. Among them, the historical mechanical performance parameters are the transmission ratio, transmission efficiency, and motion stability of the circuit breaker operating mechanism under normal working conditions, and the historical mechanical performance parameters are divided into a training set and a validation set. The validation set is used to evaluate the performance indicators of the model.

[0015] Adjust the weights and parameters of the model according to the loss function on the training set to make the model gradually converge and fit the historical data;

[0016] During the training process, evaluate the effect of the model through the performance indicators on the validation set, and adjust the hyperparameters and parameters of the model according to the evaluation results;

[0017] Repeat the parameter adjustment and verification until the optimal parameter settings are found, and a health model of the circuit breaker operating mechanism is obtained.

[0018] Preferably, the repeating the parameter adjustment and verification until the optimal parameter settings are found further includes adjusting the hyperparameters and parameters of the model according to the performance of the validation set, and finding the optimal parameter settings by testing different parameter combinations using methods such as grid search or random search.

[0019] Preferably, analyzing the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism to extract the real-time characteristic parameters of the circuit breaker operating mechanism specifically includes:

[0020] Obtain the historical types of mechanical faults of the circuit breaker operating mechanism, and obtain the characteristic parameters of the multi-dimensional perception signals of the operating mechanism under faults;

[0021] Establish a characteristic parameter database corresponding to different fault types according to the characteristic parameters of the multi-dimensional perception signals, and compare the real-time mechanical performance parameters of the circuit breaker operating mechanism collected in real time with the data in the characteristic parameter database corresponding to different fault types;

[0022] Extract the data that matches the data in the characteristic parameter database corresponding to different fault types to obtain the characteristic parameters of the circuit breaker operating mechanism.

[0023] Preferably, the obtaining the characteristic parameters of the multi-dimensional perception signals of the operating mechanism under faults specifically includes:

[0024] Extract the data of the energy storage current, and determine whether the energy storage motor of the circuit breaker operating mechanism is idling according to the change in the magnitude of the energy storage current;

[0025] Extract the spectral amplitude magnitude, spectral waveform similarity of the sound and vibration signals of the circuit breaker operating mechanism, or the speed in the monitoring signal of the giant magnetoresistive sensor, and determine whether the circuit breaker operating mechanism fails to close or open in place according to the change curve of the spectral amplitude magnitude, spectral waveform similarity of the sound and vibration signals of the circuit breaker operating mechanism, or the speed in the monitoring signal of the giant magnetoresistive sensor;

[0026] Extract the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the duration of the spectral waveform of the sound and vibration signals, and determine whether the closing and opening speeds of the circuit breaker operating mechanism are too low according to the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the duration of the spectral waveform of the sound and vibration signals;

[0027] Extract the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the spectral waveform data of the sound and vibration signals, and determine whether there is jamming in the breaker drive mechanism of the breaker operating mechanism, insufficient spring energy storage of the breaker, or abnormal mechanical wear of the breaker operating mechanism according to the similarity of the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the spectral waveform of the sound and vibration signals;

[0028] Obtain the multi-dimensional perception signal characteristic parameters of the operating mechanism under fault according to the discrimination result.

[0029] Preferably, the feature extraction algorithm is established according to the extracted data, specifically including:

[0030] Extract the frequency-domain characteristics of the time series in the operating data of the breaker operating mechanism to obtain 6 kinds of information entropy, such as energy entropy, singular entropy, approximate entropy, sample entropy, and fuzzy entropy of the vibration signal, as the feature entropy;

[0031] Extract the global characteristics in the time domain and the frequency-domain characteristics in the operating data of the breaker operating mechanism to jointly form a feature matrix, and select the global characteristics of the vibration signal as the skewness coefficient, kurtosis coefficient, and peak coefficient;

[0032] Use feature entropy dimensionality reduction for normalization processing, and subtract the average value of each dimension from each feature of the feature matrix;

[0033] Calculate the covariance matrix, where the diagonal and non-diagonal elements represent the autocovariance coefficient and the cross-covariance coefficient respectively;

[0034] Calculate the eigenvectors and eigenvalues of the covariance matrix, sort them from large to small, and use the eigenvectors corresponding to the first n largest eigenvalues as the new orthogonal feature matrix values.

[0035] Preferably, the sensor specifically includes:

[0036] A Hall sensor for extracting the mechanical state information of the breaker operating mechanism and the changes in the load characteristics;

[0037] A voiceprint and vibration sensor for performing spectral and envelope analysis on the sound and vibration signals of the breaker operating mechanism;

[0038] An image sensor for pasting punctuation marks on the moving parts of the breaker and using a high-speed camera to record the displacement of the operating rod of the breaker operating mechanism during the opening process to obtain its stroke and time characteristics;

[0039] A giant magnetoresistive sensor for detecting the changes in angle and acceleration by sensing the changes in the magnetic field according to the actions of the transmission shaft of the breaker operating mechanism during opening and closing.

[0040] Preferably, the working process of the voiceprint and vibration sensor specifically includes:

[0041] Conduct feature analysis on the vibration signal of the circuit breaker operating mechanism, compare the time-domain waveforms of different fault types, and analyze the spectrum of the circuit breaker operating mechanism signal after filtering processing;

[0042] Judge whether the signal spectrum of the circuit breaker operating mechanism meets the fault characteristics. If it does, judge the fault type according to the fault characteristics;

[0043] Merge the characteristics of all fault types to generate a feature matrix, and use principal component dimensionality reduction PCA to compress the feature matrix;

[0044] Diagnose the faults of the circuit breaker operating mechanism according to the compressed feature matrix.

[0045] Preferably, it further includes:

[0046] Extract the action profile of the circuit breaker operating mechanism based on a Hall sensor;

[0047] Optimize the action profile based on an optimization model;

[0048]

[0049] Among them, W(k) is the discrete signal of the motion profile before optimization processing; W N (k) is the optimized signal of the motion profile obtained after N iterations in the optimization model; S is the motion profile of the circuit breaker operating mechanism before optimization processing; M is the number of pixel points on the motion profile of the circuit breaker operating mechanism; T N-1 (k + i) is the weight value at the i-th pixel point during the (N - 1)-th iteration in the optimization model, T N-1 (k + i) > 0; W N-1 (k + i) is the optimized signal at the i-th pixel point during the (N - 1)-th iteration in the optimization model;

[0050] Select any pixel point of the non-central pixel points on the optimized motion profile as the segmentation pixel point, and segment the motion profile into a first sub-motion profile and a second sub-motion profile based on the segmentation pixel point, and respectively determine the center points of the first sub-motion profile and the second sub-motion profile as the first feature point and the second feature point;

[0051] Obtain the first attitude angle corresponding to the first sub-motion profile according to the first feature point and the segmentation pixel point;

[0052]

[0053] Among them, θ a(j) is the first attitude angle corresponding to the first sub-motion profile; y(j) is the ordinate of the segmented pixel point j; x(j) is the abscissa of the segmented pixel point j; y a (j) is the ordinate of the first feature point; x a (j) is the abscissa of the first feature point;

[0054] Obtain the second attitude angle corresponding to the second sub-motion profile according to the second feature point and the segmented pixel point;

[0055]

[0056] Among them, θ b (j) is the second attitude angle corresponding to the second sub-motion profile; y b (j) is the ordinate of the second feature point; x b (j) is the abscissa of the second feature point;

[0057] Query the preset attitude data table according to the first attitude angle corresponding to the first sub-motion profile and the second attitude angle corresponding to the second sub-motion profile, and determine the motion attitude information of the circuit breaker operating mechanism as the mechanical state information of the circuit breaker operating mechanism.

[0058] Preferably, inputting the extracted real-time feature parameters into the health model of the circuit breaker operating mechanism to calculate the health score of the circuit breaker operating mechanism specifically includes:

[0059] When the health status score is 100, the evaluation level is no maintenance;

[0060] When the health status score is 90≤X<100, the evaluation level is D-level maintenance;

[0061] When the health status score is 80≤X<90, the evaluation level is C-level maintenance;

[0062] When the health status score is 60≤X<80, the evaluation level is B-level maintenance;

[0063] When the health status score is 0≤X<60, the evaluation level is A-level maintenance.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] The present invention extracts features of the mechanical state of the circuit breaker operating mechanism by using multi-scale entropy, which ensures the richness and key nature of the feature information amount of the mechanical state feature extraction of the circuit breaker operating mechanism, and at the same time avoids the redundancy of the feature quantity decomposition when extracting the mechanical state features of the circuit breaker operating mechanism. The mechanical state of the circuit breaker operating mechanism is evaluated by using the intuitionistic analytic hierarchy process, which ensures the accuracy of the fault diagnosis. Description of the Drawings

[0066] Figure 1 Schematic diagram of the mechanical state fault diagnosis method for the circuit breaker operating mechanism of the present invention;

[0067] Figure 2 Flowchart for obtaining characteristic parameters of the mechanical state fault diagnosis method for the circuit breaker operating mechanism of the present invention;

[0068] Figure 3 Schematic diagram of the fault diagnosis of the mechanical state fault diagnosis method for the circuit breaker operating mechanism of the present invention. Detailed implementation manners

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0070] To solve the problem that in the actual use process of the prior art, only the motion state of the circuit breaker operating mechanism can be judged, and the fault cannot be diagnosed according to the operating state of the circuit breaker operating mechanism, resulting in the inability to give corresponding warnings according to the fault conditions of the circuit breaker operating mechanism, please refer to Figures 1 - 3 , the following technical solutions are provided in this embodiment:

[0071] The mechanical state fault diagnosis method for the circuit breaker operating mechanism includes the following steps:

[0072] Step 1: Obtain the historical mechanical characteristics of the circuit breaker operating mechanism, analyze the obtained mechanical characteristics of the circuit breaker operating mechanism, such as transmission ratio, transmission efficiency, motion stability, etc., and obtain the historical mechanical performance parameters of the circuit breaker operating mechanism in the normal working state.

[0073] Step 2: Establish a health model for the circuit breaker operating mechanism according to the historical mechanical performance parameters of the circuit breaker operating mechanism in the normal working state.

[0074] Step 3: Collect the operation data of the circuit breaker operating mechanism in real time through sensors, and then preprocess the collected data. The preprocessing includes filtering, denoising, signal analysis, etc. The preprocessing includes cleaning, transforming, and anomaly detection processing of the collected data to accurately obtain the information related to faults, select a suitable model, and train and optimize it using historical data to improve the prediction accuracy and stability. Collect the operation data of the circuit breaker operating mechanism in real time through sensors, and then preprocess the collected data. Extract the characteristic parameters related to the mechanical performance from the preprocessed data, such as the frequency-domain characteristics, time-domain characteristics, and energy characteristics of the sensor signals, etc., to accurately describe the mechanical state of the circuit breaker operating mechanism.

[0075] Step 4: Extract the characteristic parameters related to the mechanical performance of the circuit breaker operating mechanism from the processed operation data of the circuit breaker operating mechanism, such as the frequency-domain characteristics, time-domain characteristics, and energy characteristics of the sensor signals, etc. Establish a feature extraction algorithm based on the extracted characteristic parameters related to the mechanical performance of the circuit breaker operating mechanism to accurately describe the mechanical state of the circuit breaker operating mechanism.

[0076] Step 5: Conduct a quantitative analysis of the mechanical characteristics according to the established feature extraction algorithm, such as transmission ratio, transmission efficiency, motion stability, etc. Through simulation, obtain the real-time mechanical performance parameters of the circuit breaker operating mechanism. By analyzing the mechanical characteristics of the circuit breaker operating mechanism, such as transmission ratio, transmission efficiency, motion stability, etc., and conducting simulation, obtain the real-time mechanical performance parameters of the circuit breaker operating mechanism, and be able to understand the common fault modes and fault characteristics of the circuit breaker operating mechanism, such as fracture of transmission parts, looseness of transmission parts, jamming of transmission mechanisms, etc. By collecting and summarizing existing fault cases and experiences, form a fault diagnosis knowledge base to provide a basis for the fault diagnosis of the mechanical state of the circuit breaker mechanism.

[0077] Step 6: Analyze the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism, extract the real-time characteristic parameters of the circuit breaker operating mechanism. The characteristic parameters include vibration frequency, amplitude, kurtosis, etc. Input the extracted real-time characteristic parameters into the health model of the circuit breaker operating mechanism, calculate the health score of the circuit breaker operating mechanism, and diagnose the faults of the circuit breaker operating mechanism according to the health score of the circuit breaker operating mechanism. By extracting the characteristic parameters that can reflect the health state and fault characteristics of the circuit breaker operating mechanism, and analyzing and processing the multi-dimensional perception data, establish a mechanism health state evaluation model to judge the current health state of the mechanism, model and analyze the known fault modes, and realize the accurate diagnosis of the mechanism faults according to the results of the feature parameter and model matching.

[0078] Diagnose the faults according to the health score, refer to Figure 3, where T is time and N is the state. In the initial stage, the mechanical state of the circuit breaker operating mechanism is relatively stable, and the curve is a horizontal line. After a period of time, the state starts to decline at point A, and a significant decline occurs at point P, which may be detectable. It develops into a functional failure at point F. The state before approaching point F (the functional failure point) is the potential failure state. Point S is a power grid failure. T is the interval from the potential failure to the functional failure, Ts is the interval from the functional failure to the power grid failure, and Tc is the interval for maintenance detection. As can be seen from the figure, only when Tc is less than T can the potential failure be detected before the functional failure occurs, and only when Tc is less than Ts can the functional failure be detected before the power grid failure occurs.

[0079] Establish a health model for the circuit breaker operating mechanism based on the historical mechanical performance parameters in the normal working state, specifically including:

[0080] Obtain the historical mechanical performance parameters of the circuit breaker operating mechanism in the normal working state, conduct model training through the historical mechanical performance parameters, and divide the historical mechanical performance parameters into a training set and a validation set. The historical mechanical performance parameters are the transmission ratio, transmission efficiency, and motion stability of the circuit breaker operating mechanism in the normal working state.

[0081] According to the loss function on the training set, adjust the weights and parameters of the model to make the model gradually converge and fit the historical data.

[0082] During the training process, evaluate the effect of the model through the performance indicators on the validation set, and adjust the hyperparameters and parameters of the model according to the evaluation results.

[0083] Repeat the parameter adjustment and validation until the best parameter settings are found to obtain the health model of the circuit breaker operating mechanism, so as to improve the prediction accuracy and stability of the model.

[0084] Conducting model training through the historical mechanical performance parameters also includes: during the training process, by minimizing the loss function, use an optimization algorithm to adjust the weights and parameters of the model. The validation set is used to evaluate the performance indicators of the model, such as accuracy, precision, recall, F1 value, etc.

[0085] Repeating the parameter adjustment and validation until the best parameter settings are found also includes adjusting the hyperparameters and parameters of the model according to the performance of the validation set. By testing different parameter combinations, use methods such as grid search or random search to find the best parameter settings. Cross-validation can further verify the stability and generalization ability of the model.

[0086] Analyze the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism, and extract the real-time characteristic parameters of the circuit breaker operating mechanism, specifically including:

[0087] Obtain the historical types of mechanical faults of the circuit breaker operating mechanism, and obtain the characteristic parameters of the multi-dimensional sensing signals of the operating mechanism under faults.

[0088] Establish a characteristic parameter database corresponding to different fault types according to the characteristic parameters of the multi-dimensional sensing signals.

[0089] Compare the real-time mechanical performance parameters of the circuit breaker operating mechanism collected in real time with the data in the characteristic parameter database corresponding to different fault types.

[0090] Extract the data that matches the data in the characteristic parameter database corresponding to different fault types to obtain the characteristic parameters of the circuit breaker operating mechanism. By statistically analyzing and trend monitoring the operating data of the circuit breaker operating mechanism, the occurrence of anomalies and faults can be detected. By analyzing the historical data, when the real-time data differs significantly from the health model of the circuit breaker operating mechanism, it can be judged that there may be a fault. Establishing a characteristic parameter database corresponding to different fault types helps to classify and diagnose mechanism faults. By collecting and organizing the information of known fault samples and constructing fault modes and matching rules, it can be compared with the mechanism data during real-time monitoring to further improve the accuracy and reliability of early warning.

[0091] Through studying the relationships between data of different state types of the circuit breaker operating mechanism and combining the empirical knowledge of mechanism faults, establish a state evaluation system for the mechanical performance of the circuit breaker operating mechanism. Through on-line monitoring of the mechanical state of the circuit breaker operating mechanism and analyzing the observed data of various characteristic parameters reflecting the mechanical state of the circuit breaker operating mechanism, obtain the health state of the mechanical state of the circuit breaker operating mechanism, providing technical support for maintenance decision-making. Different state characteristic parameters can reflect the state of the equipment from different angles. At the same time, by using the method of health score, it simply and intuitively reflects the health assessment result of the circuit breaker, thus ensuring the accuracy of fault diagnosis.

[0092] Obtain the characteristic parameters of the multi-dimensional sensing signals of the operating mechanism under faults, specifically including:

[0093] Extract the data of the energy storage current, and determine whether the energy storage motor of the circuit breaker operating mechanism is idling according to the change of the energy storage current magnitude.

[0094] Extract the spectral amplitude magnitude, spectral waveform similarity of the sound and vibration signals of the circuit breaker operating mechanism, or the speed in the monitoring signal of the giant magnetoresistive sensor, and determine whether the circuit breaker operating mechanism fails to close or open in place according to the change curve of the spectral amplitude magnitude, spectral waveform similarity of the sound and vibration signals of the circuit breaker operating mechanism, or the speed in the monitoring signal of the giant magnetoresistive sensor.

[0095] Extract the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the duration of the spectral waveform of the sound and vibration signals, and determine whether the opening and closing speeds of the breaker operating mechanism are too low according to the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the duration of the spectral waveform of the sound and vibration signals;

[0096] Extract the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the spectral waveform data of the sound and vibration signals, and determine whether there is jamming in the breaker transmission mechanism of the breaker operating mechanism, insufficient spring energy storage of the breaker, or abnormal mechanical wear of the breaker operating mechanism according to the similarity of the change curve of the speed in the monitoring signal of the giant magnetoresistive sensor or the spectral waveform of the sound and vibration signals;

[0097] Obtain the multi-dimensional perception signal characteristic parameters of the operating mechanism under faults according to the discrimination results.

[0098] Obtain the multi-dimensional perception signal characteristic parameters of the operating mechanism under faults, establish a characteristic parameter database corresponding to different fault types, conduct research on the relationships between data of different state types, combine the empirical knowledge of mechanism faults, establish an expert system for state evaluation of the mechanical performance of the breaker operating mechanism, online monitor the mechanical state of the breaker operating mechanism through sensors, and evaluate the health state of the breaker operating mechanism by analyzing the observed data of various characteristic parameters reflecting the breaker operating mechanism. Provide technical support for maintenance decision-making. Different state characteristic parameters can reflect the state of the equipment from different angles. At the same time, adopt the method of health score to simply and intuitively reflect the health assessment result of the breaker.

[0099] Extract the characteristic parameters related to the mechanical performance of the breaker operating mechanism from the processed operating data of the breaker operating mechanism, specifically including:

[0100] Based on the information entropy theory, the concept of cross-correlation entropy is proposed to extract the frequency-domain characteristics of the time series in the operating data of the breaker operating mechanism, and six information entropies, namely energy entropy, singular entropy, approximate entropy, sample entropy, and fuzzy entropy, of the vibration signal are obtained as characteristic entropies.

[0101] Extract the global characteristics in the time domain and the frequency-domain characteristics in the operating data of the breaker operating mechanism to jointly form a characteristic matrix. The selected global characteristics of the vibration signal are the skewness coefficient, kurtosis coefficient, and peak coefficient. These characteristics can complement the local characteristics in the time domain and can more completely depict the differences between different fault waveforms.

[0102] Use feature entropy reduction for normalization processing, and subtract the average value of each feature in the feature matrix from the corresponding dimension.

[0103] Calculate the covariance matrix, where the diagonal and non-diagonal elements represent the auto-covariance coefficient and the cross-covariance coefficient respectively.

[0104] Calculate the eigenvectors and eigenvalues of the covariance matrix, sort them from largest to smallest, and take the eigenvectors corresponding to the first n largest eigenvalues as the new orthogonal eigenmatrix values, which are the characteristic parameters related to the mechanical performance of the circuit breaker operating mechanism. The value of n can be determined according to the variance contribution rate.

[0105] Sensors, specifically including:

[0106] Hall sensors, used to extract the mechanical state information of the circuit breaker operating mechanism and the changes in load characteristics.

[0107] Acoustic and vibration sensors, used to detect fault types through spectral and envelope analysis of the sound and vibration signals of the circuit breaker operating mechanism: idling of the circuit breaker energy storage motor, incomplete closing and opening, too low closing and opening speed, jamming of the circuit breaker transmission mechanism, insufficient spring energy storage of the circuit breaker, abnormal mechanical wear of the circuit breaker operating mechanism. By analyzing the spectrum and envelope of the vibration signal of the circuit breaker operating mechanism, characteristic parameters such as vibration frequency, amplitude, and kurtosis can be extracted to evaluate the health status of the mechanism. According to the fault mode and mechanical characteristics, appropriate feature extraction methods such as time-domain features, frequency-domain features, wavelet transform, and modal analysis can be selected to capture the characteristics of the fault signal and perform quantitative analysis.

[0108] Image sensors, used to paste punctuation marks on the moving parts of the circuit breaker, and use a high-speed camera to record the displacement of the operating rod of the circuit breaker operating mechanism during the opening process to obtain its stroke and time characteristics, and realize the detection of the mechanical characteristics of high-voltage switches.

[0109] Giant magnetoresistive sensors, used to detect the changes in angle and acceleration by sensing the changes in magnetic field according to the actions of the transmission shaft of the circuit breaker operating mechanism during closing and opening, and detect fault types: incomplete closing and opening, too low closing and opening speed, jamming of the circuit breaker transmission mechanism, insufficient spring energy storage of the circuit breaker, abnormal mechanical wear of the circuit breaker operating mechanism.

[0110] The working principle of the acoustic and vibration sensors specifically includes:

[0111] Conduct feature analysis on the vibration signal of the circuit breaker operating mechanism, compare the time-domain waveforms of different fault types, and analyze the spectrum of the circuit breaker operating mechanism signal after filtering.

[0112] Judge whether the spectrum of the circuit breaker operating mechanism signal meets the fault characteristics. If it does, judge the fault type according to the fault characteristics.

[0113] Merge the characteristics of all fault types to generate a feature matrix, and compress the feature matrix using principal component dimensionality reduction PCA.

[0114] Diagnose the faults of the circuit breaker operating mechanism according to the compressed feature matrix.

[0115] Input the extracted real-time feature parameters into the health model of the circuit breaker operating mechanism to calculate the health score of the circuit breaker operating mechanism, specifically including:

[0116] When the health status score is 100, the evaluation level is no maintenance.

[0117] When the health status score is 90 ≤ X < 100, the evaluation level is Class D maintenance.

[0118] When the health status score is 80 ≤ X < 90, the evaluation level is Class C maintenance.

[0119] When the health status score is 60 ≤ X < 80, the evaluation level is Class B maintenance.

[0120] When the health status score is 0 ≤ X < 60, the evaluation level is Class A maintenance.

[0121] Preferably, it further includes:

[0122] Extract the motion profile of the circuit breaker operating mechanism based on the Hall sensor;

[0123] Optimize the motion profile based on the optimization model;

[0124]

[0125] Among them, W(k) is the discrete signal of the motion profile before optimization; W N (k) is the optimized signal of the motion profile obtained after N iterations in the optimization model; S is the motion profile of the circuit breaker operating mechanism before optimization; M is the number of pixel points on the motion profile of the circuit breaker operating mechanism; T N-1 (k + i) is the weight value at the i-th pixel point during the (N - 1)-th iteration in the optimization model, T N-1 (k + i) > 0; W N-1 (k + i) is the optimized signal at the i-th pixel point during the (N - 1)-th iteration in the optimization model;

[0126] Select any pixel point of the non-central pixel points on the optimized motion profile as the segmentation pixel point, and based on the segmentation pixel point, divide the motion profile into a first sub-motion profile and a second sub-motion profile, and respectively determine the center points of the first sub-motion profile and the second sub-motion profile as the first feature point and the second feature point;

[0127] Obtain the first attitude angle corresponding to the first sub-motion profile according to the first feature point and the segmentation pixel point;

[0128]

[0129] where, θ a (j) is the first attitude angle corresponding to the first sub-motion profile; y(j) is the ordinate of the segmented pixel point j; x(j) is the abscissa of the segmented pixel point j; y a (j) is the ordinate of the first feature point; x a (j) is the abscissa of the first feature point;

[0130] Obtain the second attitude angle corresponding to the second sub-motion profile according to the second feature point and the segmented pixel point;

[0131]

[0132] where, θ b (j) is the second attitude angle corresponding to the second sub-motion profile; y b (j) is the ordinate of the second feature point; x b (j) is the abscissa of the second feature point;

[0133] Query the preset attitude data table according to the first attitude angle corresponding to the first sub-motion profile and the second attitude angle corresponding to the second sub-motion profile, and determine the motion attitude information of the breaker operating mechanism as the mechanical state information of the breaker operating mechanism.

[0134] Working principle and beneficial effects of the above technical solution: First, based on the Hall sensor, the action profile of the circuit breaker operating mechanism is extracted. During the process of optimizing the action profile based on the optimization model, in step 1, the initial position v(0) of the action profile is given based on the initial estimate or prior knowledge; in step 2, iterative calculation: A relatively large time step Δt is selected, and the iterative formula is used for calculation to obtain the iterative result v(nΔt) at each step. This step is to gradually approach the true action profile through iteration; in step 3, convergence judgment: It is judged whether the current profile converges to a given threshold. If so, the iteration is stopped; otherwise, the next step is continued; in step 4, direction adjustment and step size optimization: If it is found during the iteration that the movement direction of the profile is reversed (i.e., jitter or discontinuity occurs), the profile is rolled back to the previous step (such as the (n - 1)-th step), and the time step Δt is halved (becomes Δt / 2). Then, the iterative result v(nΔt) of the current step is recalculated. This step is to avoid the profile crossing the equilibrium point due to too large a step size. Continuous iteration and optimization: Repeat steps 3 to 4 until the profile converges to the given threshold. In this way, the time step Δt can be dynamically adjusted, enabling the algorithm to converge to the equilibrium point faster while reducing unnecessary iteration times. It is convenient to implement fast optimization processing of the motion profile based on the optimization model. The optimization signal of the motion profile obtained after N iterations in the optimization model is accurately calculated. An arbitrary pixel point of the non-central pixel points is selected on the optimized motion profile, aiming to achieve unequal division of the motion profile. Based on the divided pixel point, the motion profile is divided into a first sub-motion profile and a second sub-motion profile, and the center points of the first sub-motion profile and the second sub-motion profile are respectively determined as the first feature point and the second feature point; the first attitude angle corresponding to the first sub-motion profile is obtained according to the first feature point and the divided pixel point, the second attitude angle corresponding to the second sub-motion profile is obtained according to the second feature point and the divided pixel point, and the preset attitude data table is queried according to the first attitude angle corresponding to the first sub-motion profile and the second attitude angle corresponding to the second sub-motion profile. The preset attitude data table is a comparison data table of the first attitude angle - the second attitude angle - the motion attitude information. When determining the motion attitude information of the circuit breaker operating mechanism, based on the motion profile and the angle information, the motion attitude information of the circuit breaker operating mechanism is accurately determined, and then the mechanical state information of the circuit breaker operating mechanism is accurately determined.

[0135] In summary, for the mechanical state fault diagnosis method of the circuit breaker operating mechanism of the present invention, by using multi-scale entropy to extract the characteristics of the mechanical state of the circuit breaker operating mechanism, it ensures the richness and key nature of the characteristic information quantity in the extraction of the mechanical state characteristics of the circuit breaker operating mechanism. At the same time, it also avoids the redundancy of the feature quantity decomposition during the extraction of the mechanical state characteristics of the circuit breaker operating mechanism. According to various different types of parameters in terms of the mechanical characteristics of the circuit breaker, an evaluation model capable of evaluating the operating state of the circuit breaker is constructed. On the basis of the fuzzy analytic hierarchy process, the concept of intuition is added, and the intuitionistic analytic hierarchy process is used to evaluate the mechanical state of the circuit breaker operating mechanism, ensuring the accuracy of the fault diagnosis.

[0136] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device.

[0137] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for diagnosing mechanical state faults of a circuit breaker operating mechanism, characterized in that: The following steps are involved: Step 1: Obtain historical mechanical characteristics of the circuit breaker operating mechanism, analyze the mechanical characteristics of the circuit breaker operating mechanism, and obtain historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions; Step 2: Establish a health model of the circuit breaker operating mechanism according to the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions; Step 3: The operation data of the circuit breaker operating mechanism is collected in real time through the sensor, and then the collected data is preprocessed. The preprocessing includes cleaning, transforming and abnormal detection processing of the collected data. The motion profile of the circuit breaker operating mechanism is extracted through the Hall sensor in the sensor. After optimization processing, the motion profile is divided into a first sub-motion profile and a second sub-motion profile. According to the first posture angle corresponding to the first sub-motion profile and the second posture angle corresponding to the second sub-motion profile, the preset posture data table is queried to determine the motion profile and angle information as the mechanical state information of the circuit breaker operating mechanism; Step 4: extracting characteristic parameters related to the mechanical properties of the circuit breaker operating mechanism from the preprocessed operating data, and establishing a feature extraction algorithm based on the extracted characteristic parameters related to the mechanical properties of the circuit breaker operating mechanism; Step 5: Quantitatively analyze the mechanical characteristics according to the established feature extraction algorithm, and obtain the real-time mechanical performance parameters of the circuit breaker operating mechanism through simulation; Step six: Analyze the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism, extract the real-time characteristic parameters of the circuit breaker operating mechanism, input the extracted real-time characteristic parameters into the health model of the circuit breaker operating mechanism, calculate the health score of the circuit breaker operating mechanism, and diagnose the circuit breaker operating mechanism fault according to the health score of the circuit breaker operating mechanism.

2. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 1, characterized in that: The establishing of the circuit breaker operating mechanism health model according to the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions specifically includes: Obtain the historical mechanical performance parameters of the circuit breaker operating mechanism under normal working conditions, and train the model through the historical mechanical performance parameters. During the training process, the weights and parameters of the model are adjusted by the optimization algorithm by minimizing the loss function. The historical mechanical performance parameters are the transmission ratio, transmission efficiency, and motion stability of the circuit breaker operating mechanism under normal working conditions. The historical mechanical performance parameters are divided into a training set and a validation set. The validation set is used to evaluate the performance indicators of the model. According to the loss function of the training set, adjust the weights and parameters of the model so that the model gradually converges and fits the historical data; During the training process, the performance indicators on the validation set are used to evaluate the effect of the model, and the hyperparameters and parameters of the model are adjusted based on the evaluation results; Repeat parameter adjustment and verification until the optimal parameter setting is found and the circuit breaker operating mechanism health model is obtained.

3. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 2, characterized in that: The method of repeatedly adjusting and verifying parameters until the best parameter setting is found also includes: adjusting the hyperparameters and parameters of the model according to the performance of the verification set, and finding the best parameter setting by experimenting with different parameter combinations and using grid search or random search.

4. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 1, characterized in that: The analysis of the spectrum and envelope of the real-time mechanical performance parameters of the circuit breaker operating mechanism to extract the real-time characteristic parameters of the circuit breaker operating mechanism specifically includes: Obtain the historical types of mechanical faults of the circuit breaker operating mechanism, and obtain the characteristic parameters of the multi-dimensional sensing signal of the operating mechanism under the fault; Establish a characteristic parameter database corresponding to different fault types based on the characteristic parameters of the multi-dimensional sensing signal, and compare the real-time mechanical performance parameters of the circuit breaker operating mechanism collected in real time with the data in the characteristic parameter database corresponding to different fault types; The data matching the data in the characteristic parameter database corresponding to different fault types are extracted to obtain the real-time characteristic parameters of the circuit breaker operating mechanism.

5. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 4, characterized in that: The obtaining of the characteristic parameters of the multi-dimensional sensing signal of the operating mechanism under fault specifically includes: Extract the energy storage current data, and judge whether the energy storage motor of the circuit breaker operating mechanism is idling according to the change of the energy storage current; Extract the frequency spectrum amplitude of the sound and vibration signal of the circuit breaker operating mechanism, the frequency spectrum waveform similarity or the speed in the giant magnetoresistance sensor monitoring signal, and judge whether the circuit breaker operating mechanism is not in place according to the frequency spectrum amplitude of the sound and vibration signal of the circuit breaker operating mechanism, the frequency spectrum waveform similarity or the speed change curve in the giant magnetoresistance sensor monitoring signal; Extract the speed change curve or the duration of the spectrum waveform of the sound and vibration signal in the giant magnetoresistance sensor monitoring signal, and judge whether the opening and closing speed of the circuit breaker operating mechanism is too low according to the speed change curve or the duration of the spectrum waveform of the sound and vibration signal in the giant magnetoresistance sensor monitoring signal; Extract the speed change curve or the spectrum waveform data of the sound and vibration signals in the giant magnetoresistance sensor monitoring signal, and judge whether the circuit breaker transmission mechanism of the circuit breaker operating mechanism is stuck, the circuit breaker spring energy storage is insufficient, or the circuit breaker operating mechanism is abnormally mechanically worn according to the speed change curve or the spectrum waveform similarity of the sound and vibration signals in the giant magnetoresistance sensor monitoring signal; According to the results of the discrimination, the characteristic parameters of the multi-dimensional sensing signal of the operating mechanism under fault are obtained.

6. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 1, characterized in that: The feature extraction algorithm is established according to the extracted data, specifically comprising: The frequency domain characteristics of the time series in the operation data of the circuit breaker operating mechanism are extracted, and the energy entropy, singular entropy, approximate entropy, sample entropy and fuzzy entropy of the vibration signal are obtained as characteristic entropy; The global features in the time domain and the frequency domain in the operation data of the circuit breaker operating mechanism are extracted to form a feature matrix, and the global features of the vibration signal are selected as the skewness coefficient, the kurtosis coefficient, and the peak coefficient. Use feature entropy dimensionality reduction to perform normalization processing, and subtract the average value of the corresponding dimension from each feature of the feature matrix; Calculate the covariance matrix, where the diagonal and off-diagonal elements represent the autocovariance coefficients and cross-covariance coefficients, respectively; Calculate the eigenvectors and eigenvalues ​​of the covariance matrix and sort them from large to small, and use the eigenvectors corresponding to the first n largest eigenvalues ​​as the new orthogonal eigenmatrix values.

7. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 1, characterized in that: The sensor specifically comprises: Hall sensor, used to extract mechanical state information of the circuit breaker operating mechanism and changes in load characteristics; Voiceprint and vibration sensors are used to perform spectrum and envelope analysis on the sound and vibration signals of the circuit breaker operating mechanism; Image sensor, used to paste marks on the moving parts of the circuit breaker, use a high-speed camera to record the displacement of the operating rod of the circuit breaker operating mechanism during the breaking process, and obtain its stroke and time characteristics; The giant magnetoresistance sensor is used to detect changes in angle and acceleration by sensing changes in the magnetic field based on the movement of the transmission shaft of the circuit breaker operating mechanism during opening and closing.

8. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 7, characterized in that: The workflow of the voiceprint and vibration sensor specifically includes: Perform feature analysis on the vibration signal of the circuit breaker operating mechanism, compare the time domain waveforms of different fault types, and analyze the spectrum of the circuit breaker operating mechanism signal after filtering; Determine whether the signal spectrum of the circuit breaker operating mechanism meets the fault characteristics. If yes, determine the fault type according to the fault characteristics. The features of all fault types are combined to generate a feature matrix, and the feature matrix is ​​compressed using principal component reduction (PCA). Diagnose circuit breaker operating mechanism faults based on the compressed special diagnosis matrix.

9. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 1, characterized in that: Also includes: Optimizing the action profile based on the optimization model; Where W(k) is the discrete signal of the motion profile before optimization processing; W N (k) is the optimized signal of the motion profile obtained after N iterations in the optimization model; S is the motion profile of the circuit breaker operating mechanism before optimization; M is the number of pixel points on the motion profile of the circuit breaker operating mechanism; T N-1 (k+i) is the weight at the i-th pixel at the N-1th iteration in the optimization model, T N-1 (k+i)>0;W N-1 (k+i) is the optimization signal at the i-th pixel at the N-1th iteration in the optimization model; Obtaining a first posture angle corresponding to the first sub-motion profile according to the first feature point and the segmented pixel point; Among them, θ a (j) is the first posture angle corresponding to the first sub-motion profile; y(j) is the ordinate of the segmented pixel point j; x(j) is the abscissa of the segmented pixel point j; y a (j) is the ordinate of the first feature point; x a (j) is the horizontal coordinate of the first feature point; Obtaining a second posture angle corresponding to the second sub-motion profile according to the second feature point and the segmented pixel point; Among them, θ b (j) is the second posture angle corresponding to the second sub-motion profile; y b (j) is the ordinate of the second feature point; x b (j) is the horizontal coordinate of the second feature point.

10. The circuit breaker operating mechanism mechanical state fault diagnosis method according to claim 7, characterized in that: The step of inputting the extracted real-time characteristic parameters into the health model of the circuit breaker operating mechanism and calculating the health score of the circuit breaker operating mechanism specifically includes: When the health status score is 100, the assessment level is no maintenance; When the health status score is 90≤X<100, the assessment level is D-level maintenance; When the health status score is 80≤X<90, the assessment level is C-level maintenance; When the health status score is 60≤X<80, the assessment level is B-level maintenance; When the health status score is 0≤X<60, the assessment level is A-level maintenance.

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