Switch cabinet operation reliability evaluation method, system, equipment and medium

Abstract

The invention relates to a switch cabinet operation reliability evaluation method, system and device and a medium, and belongs to the technical field of power distribution equipment monitoring, and the evaluation method comprises the steps: receiving real-time monitoring data of a to-be-detected switch cabinet; performing fast Fourier transform on the current waveform data, and calculating to obtain a harmonic distortion rate sequence; performing feature extraction on the vibration spectrum data to obtain a vibration dominant frequency amplitude sequence; correlation analysis is carried out on the harmonic distortion rate sequence and the vibration dominant frequency amplitude sequence, a sliding covariance matrix is constructed, and a dynamic coupling coefficient is calculated; generating a three-dimensional feature space distribution diagram according to the harmonic distortion rate sequence, the vibration dominant frequency amplitude sequence and the dynamic coupling coefficient sequence; and dividing the real-time monitoring data into a plurality of feature regions based on density clustering, determining a current fault state corresponding to the to-be-detected switch cabinet according to the feature regions mapped by the real-time monitoring data, determining a corresponding maintenance strategy, generating a reliability evaluation report, and sending the reliability evaluation report to the management terminal. The operation stability of the switch cabinet equipment can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution equipment monitoring, and in particular to a method, system, equipment and medium for evaluating the operation reliability of a switch cabinet. Background Art

[0002] In industrial production, switchgear is a critical component of power distribution equipment, performing crucial electrical protection functions during daily operation. Frequent start-stop operations can cause aging and wear of electrical components. Furthermore, the mechanical transmission system within the switchgear can also experience wear and vibration due to constant movement and load fluctuations. These factors lead to compound degradation of the switchgear's electrical and mechanical components over long-term operation.

[0003] Currently, common reliability assessment methods mostly focus on monitoring the electrical components, typically relying on electrical parameters (such as current and harmonics) to evaluate the system's operating status. However, this approach overlooks the health of mechanical components, whose wear and vibration are often a root cause of failures. Furthermore, traditional assessment methods fail to effectively capture the interplay between the electrical and mechanical systems. Especially under frequent start-stop conditions, wear on mechanical components can directly impact the electrical system's operating status. Electrical system instability can also trigger abnormal vibration or overload in mechanical components, reducing the switchgear's operational stability. Summary of the Invention

[0004] In order to improve the operational stability of switchgear equipment, the present application provides a switchgear operational reliability assessment method, system, equipment and medium.

[0005] In a first aspect, the present application provides a method for evaluating the operational reliability of a switchgear, which adopts the following technical solution: A switchgear operation reliability evaluation method, the evaluation method comprising: Receive real-time monitoring data of the switchgear to be inspected and perform time stamp synchronization; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; Performing a fast Fourier transform on the current waveform data, extracting integer multiple frequency components of the fundamental frequency, and calculating a harmonic distortion rate sequence; Performing feature extraction on the vibration spectrum data to obtain a vibration main frequency amplitude sequence; Performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix and calculating a dynamic coupling coefficient; Normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generating a three-dimensional feature space distribution map; Dividing the three-dimensional feature space distribution map into a plurality of feature regions based on density clustering; Determining a current fault state corresponding to the switchgear to be detected according to a feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; A corresponding maintenance strategy is determined according to the current fault status, and a reliability assessment report is generated and sent to the management terminal.

[0006] By adopting the above technical solution, the harmonic distortion rate of the electrical system and the vibration characteristics of the mechanical system are correlated and analyzed, and a dynamically coupled cross-domain evaluation system is constructed. By accurately monitoring the health status of the switchgear under complex operating conditions, it can effectively identify electrical, mechanical, and combined faults, generate fault reports in a timely manner, and propose maintenance strategies, thereby significantly improving the operational reliability and safety of the equipment, helping to extend the equipment life and improve the overall reliability of the system.

[0007] Optionally, the step of performing feature extraction on the vibration spectrum data to obtain a vibration main frequency amplitude sequence includes: performing data preprocessing on the vibration spectrum data; Perform fast Fourier transform on the preprocessed vibration spectrum data to convert the time domain signal into the frequency domain signal to obtain the spectrum characteristics; Decomposing the spectrum feature into a plurality of sub-bands, and calculating the kurtosis and spectral entropy of each sub-band; According to the kurtosis and spectral entropy, selecting sub-frequency bands that meet preset requirements as sensitive frequency bands; Extracting the main frequency amplitude of the sensitive frequency band to obtain sensitive features; The vibration main frequency amplitude is calculated based on the sensitive characteristics and arranged in chronological order to obtain a vibration main frequency amplitude sequence.

[0008] By adopting the above technical solution, it is possible to capture the early high-frequency vibration characteristics of mechanical faults such as wear and looseness. These characteristics provide a reliable basis for subsequent fault diagnosis and equipment health monitoring, helping equipment managers to effectively detect and prevent faults.

[0009] Optionally, the steps of performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix, and calculating a dynamic coupling coefficient include: Based on a preset time window length, the harmonic distortion rate sequence and the vibration main frequency amplitude sequence are divided into a plurality of time windows to obtain a window data set; Calculating the covariance between the harmonic distortion rate sequence and the vibration main frequency amplitude sequence in each time window in the window data set to construct a sliding covariance matrix; Perform singular value decomposition on the sliding covariance matrix within each time window, extract the principal components of the sliding covariance matrix and calculate the coupling coefficient; The coupling coefficient is updated in a time-varying manner based on a preset forgetting factor to obtain a dynamic coupling coefficient sequence.

[0010] By adopting the above technical solution, cross-domain correlation analysis of electrical and mechanical systems is effectively achieved. By dividing the time window, the dynamic characteristics of the electrical and mechanical systems are converted into analyzable local data. Then, the main coupling modes between the systems are extracted through the sliding covariance matrix and SVD. Then, the changes in the system status are reflected in real time through a dynamic update mechanism. The final dynamic coupling coefficient sequence can accurately reveal the mutual influence of the electrical and mechanical systems under different working conditions, providing a scientific basis for fault diagnosis and equipment maintenance.

[0011] Optionally, the step of normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence to generate a three-dimensional feature space distribution map includes: Normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence based on a preset target range; Mapping the normalized harmonic distortion rate sequence, vibration main frequency amplitude sequence, and dynamic coupling coefficient sequence into a three-dimensional feature space, defining coordinate axes, and constructing point cloud data; A three-dimensional feature space distribution map is obtained based on the point cloud data.

[0012] By employing the above technical solution, normalization processing and the construction of a three-dimensional feature space enable the comparison and visualization of different types of data within a unified space. This technical solution intuitively displays the health status of electrical and mechanical systems and their interrelationships, helping maintenance personnel quickly identify different failure modes and maintenance priorities. Ultimately, the three-dimensional spatial distribution map enables in-depth analysis of equipment status, providing more accurate fault prediction and management strategies.

[0013] Optionally, the characteristic areas include a normal operating condition area, an electrical degradation area, a mechanical degradation area, and a composite fault area.

[0014] Optionally, after the steps of constructing the sliding covariance matrix and calculating the dynamic coupling coefficient, the following steps are further included: extracting a rate of change of the dynamic coupling coefficient over a plurality of sampling periods; Determining whether the number of times the change rate is greater than a preset threshold value in consecutive sampling periods exceeds a preset number; If so, a mechanical-electrical composite degradation alarm signal is output, and a corresponding maintenance strategy is generated and sent to the management terminal.

[0015] By employing this technical solution, the rate of change of the dynamic coupling coefficient is monitored, screened, and statistically analyzed. Combined with the set threshold and changes within continuous sampling periods, this effectively identifies potential compound degradation issues in the mechanical and electrical systems. Upon detecting an anomaly, the system automatically outputs an alarm signal and generates a corresponding maintenance instruction set, promptly transmitting fault information to equipment management personnel. This technical solution enables early warning intervention before a fault occurs, reducing equipment downtime, improving equipment reliability and maintenance efficiency, and ultimately optimizing equipment lifecycle management.

[0016] In a second aspect, the present application provides a switchgear operation reliability assessment system, which adopts the following technical solutions: A switchgear operation reliability evaluation system, comprising: A data receiving module, configured to receive real-time monitoring data of the switchgear to be inspected and synchronize the time stamp; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; A current signal processing module is used to perform a fast Fourier transform on the current waveform data, extract the integer multiple frequency components of the fundamental frequency, and calculate the harmonic distortion rate sequence; A vibration data processing module, configured to extract features from the vibration spectrum data to obtain a vibration main frequency amplitude sequence; A dynamic coupling coefficient generation module is used to perform correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, construct a sliding covariance matrix and calculate the dynamic coupling coefficient; A three-dimensional spatial distribution module normalizes the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generates a three-dimensional feature space distribution map; A feature region division module, configured to divide the three-dimensional feature space distribution map into a plurality of feature regions based on density clustering; a fault state determination module, which determines the current fault state of the switchgear to be detected according to the feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; A corresponding maintenance strategy is determined according to the current fault status, and a reliability assessment report is generated and sent to the management terminal.

[0017] Optionally, the system further includes: A change rate extraction module, configured to extract the change rate of the dynamic coupling coefficient within a plurality of sampling periods; a judgment module, configured to judge whether the number of times the change rate is greater than a preset threshold value in consecutive sampling periods exceeds a preset number; if so, output an abnormal result; an alarm module, configured to output a mechanical-electrical composite degradation alarm signal in response to the abnormal result; The maintenance prompt module is used to generate a corresponding maintenance strategy according to the mechanical-electrical composite degradation alarm signal and send it to the management terminal.

[0018] In a third aspect, the present application provides a computer device that adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to the first aspect.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program capable of being loaded by a processor and executing any one of the methods in the first aspect.

[0020] To summarize, the present application includes at least one of the following beneficial technical effects: through multi-module collaboration, combined with vibration spectrum data and current waveform data, the health status of electrical and mechanical systems is comprehensively monitored, effectively improving the fault detection accuracy and response speed of the equipment, reducing the downtime of the equipment, optimizing the maintenance resource allocation, and significantly improving the reliability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a first flow chart of a method for evaluating the operation reliability of a switch cabinet according to one embodiment of the present application.

[0022] Figure 2 This is a second flow chart of the switch cabinet operation reliability evaluation method according to one embodiment of the present application.

[0023] Figure 3 This is a third flow chart of a method for evaluating the operation reliability of a switch cabinet according to one embodiment of the present application.

[0024] Figure 4 This is a fourth flow chart of a method for evaluating the operation reliability of a switch cabinet according to one embodiment of the present application.

[0025] Figure 5 This is a fifth flow chart of a method for evaluating the operation reliability of a switch cabinet according to one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-5 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0027] An embodiment of the present application discloses a method for evaluating the operational reliability of a switch cabinet.

[0028] Reference Figure 1 , a switchgear operation reliability evaluation method, the evaluation method includes: Step S101: receiving real-time monitoring data of the switchgear to be inspected and performing time stamp synchronization; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; Specifically, when monitoring the operating status of the switchgear, it is necessary to collect real-time vibration spectrum data and current waveform data through sensors. These data come from various components of the switchgear, such as electrical components and mechanical components.

[0029] Furthermore, to ensure data consistency and comparability, data from different sources need to be time synchronized. Timestamp alignment algorithms, such as hardware clock synchronization or those based on the Network Time Protocol (NTP), can be used to mark data from different sensors to a unified time axis, so that each sample in the dataset is analyzed at the same timestamp.

[0030] Step S102, performing a fast Fourier transform on the current waveform data, extracting integer multiple frequency components of the fundamental frequency, and calculating a harmonic distortion rate sequence; The current waveform data reflects the operating status of the electrical system. By performing a Fast Fourier Transform (FFT) on the current waveform, the time-domain signal can be converted to the frequency domain, identifying the frequency components within the signal. Typically, integer multiples of the fundamental frequency are key indicators of electrical system health. Harmonics, in particular, can indicate electrical faults or abnormal operating conditions.

[0031] Therefore, by extracting the integer multiple frequency components of the fundamental frequency, the harmonic distortion rate (THD) of the electrical system can be accurately quantified, thereby evaluating the health status of the electrical part. The final harmonic distortion rate sequence can reveal the degradation trend of the electrical part.

[0032] In some embodiments, a 1024-point FFT is performed on the current waveform to extract the 3rd, 5th, and 7th harmonic components (150 Hz, 250 Hz, and 350 Hz) of the fundamental wave (50 Hz). The harmonic distortion (THD) is calculated as: ; Among them, I1 is the effective value of the fundamental current, I h is the effective value of the hth harmonic.

[0033] Step S103, extracting features from the vibration spectrum data to obtain a vibration main frequency amplitude sequence; Vibration spectrum data reflects the operating status of mechanical components. In particular, the vibration amplitude in the primary frequency band is often closely related to wear and damage. Spectral analysis identifies the primary frequency band of mechanical vibration (e.g., 500-1500 Hz) and extracts the energy peak within this band as a characteristic value.

[0034] In some embodiments, the 0-10kHz vibration spectrum is decomposed into 6 layers (64 sub-bands), the energy of the 500-1500Hz frequency band (corresponding to the natural frequency of the transmission mechanism) is extracted, the kurtosis and spectral entropy of each sub-band are calculated, and the frequency bands with kurtosis > 3 and entropy < 1.5 are selected as sensitive features to capture the early high-frequency vibration characteristics of mechanical faults such as mechanism jamming and loose bolts.

[0035] It can be understood that by calculating the main frequency amplitude sequence of the vibration spectrum, the health status of the mechanical components can be monitored. If the main frequency amplitude fluctuates abnormally, it may indicate that the mechanical components are worn, loose or have other potential faults.

[0036] Step S104, performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix and calculating the dynamic coupling coefficient; It should be noted that the electrical part and the mechanical part affect each other. Especially under frequent start-stop conditions, electrical faults may affect the mechanical system, and vice versa.

[0037] Therefore, harmonic distortion and vibration amplitude may affect each other, necessitating correlation analysis to explore their relationship. By constructing a sliding covariance matrix, we can dynamically assess the correlation between the two and further derive the coupling coefficients between the electrical and mechanical systems in different time windows. This reflects the interaction between electrical and mechanical components during fault evolution, such as the impact of electrical problems on mechanical components or the feedback effect of mechanical wear on the electrical system.

[0038] It is understandable that if the coupling coefficient of the harmonic distortion rate and the vibration main frequency amplitude changes significantly, it may mean that the electrical fault has affected the mechanical components, or vice versa.

[0039] Step S105, normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generating a three-dimensional feature space distribution map; To unify data with different units and dimensions, it is necessary to normalize the harmonic distortion rate series, the main vibration frequency amplitude series, and the dynamic coupling coefficient. This normalized data can be mapped into the same feature space, generating a three-dimensional feature space distribution map that intuitively displays the feature distribution under different states.

[0040] For example, by normalizing the three features (harmonic distortion rate, vibration main frequency amplitude, and coupling coefficient) and mapping them into a three-dimensional feature space for visualization, the relative importance of each feature and their relationship can be clearly presented.

[0041] Specifically, the three-dimensional feature space can be imagined as a "cube room" with three walls representing three indicators. The X-axis (wall 1) represents the harmonic distortion rate (the degree of current abnormality); the Y-axis (wall 2) represents the amplitude of the main vibration frequency (the degree of mechanical wear); and the Z-axis (wall 3) represents the dynamic coupling coefficient (the strength of the correlation between mechanics and current).

[0042] Step S106, dividing the three-dimensional feature space distribution map into multiple feature regions based on density clustering; Among them, the characteristic areas include normal operating condition area, electrical degradation area, mechanical degradation area and composite fault area; Specifically, a density clustering algorithm (such as DBSCAN) is used to cluster the three-dimensional feature space. Different feature regions can be divided according to the density distribution characteristics of the data. Different regions represent the operating status of the switchgear under different working conditions, providing accurate information for fault diagnosis and helping equipment managers to judge the operating status of the switchgear.

[0043] In some embodiments, when the value of the harmonic distortion rate is larger, the arc erosion is more serious; when the value of the main frequency amplitude is larger, the mechanical wear is more serious; when the value of the dynamic coupling coefficient is larger, the probability of mechanical and electrical problems occurring at the same time is higher.

[0044] Step S107, determining the current fault state corresponding to the switchgear to be detected based on the feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; Based on real-time monitoring data, the corresponding harmonic distortion rate, vibration main frequency amplitude, and dynamic coupling coefficient are calculated. These three values are normalized to obtain a three-dimensional coordinate. This coordinate can be mapped according to the three-dimensional feature space distribution diagram to determine the characteristic region in which it is located. Based on the characteristic region, the current fault status of the switchgear can be determined, such as whether there is electrical degradation, mechanical degradation, or a combined fault.

[0045] In this embodiment, three values are combined into a three-dimensional coordinate (e.g., 0.4, 0.8, 0.6), and the corresponding data point is found in the cube. The region boundaries are compared. If the coordinate is in the lower left corner of the cube (small value), it indicates the normal operating condition. If the coordinate is near the top of the X-axis (high harmonic distortion), it indicates the electrical degradation zone. If the coordinate is near the top of the Y-axis (high vibration amplitude), it indicates the mechanical degradation zone. If the coordinate is in the upper right corner (all three values are high), it indicates the combined fault zone.

[0046] It is understandable that if a fault is determined based on only a single indicator, such as only looking at the high current harmonic distortion rate, it may be a temporary power grid fluctuation and not necessarily an equipment failure; however, through the joint analysis in this application, when the current is abnormal (X is high) and the vibration is abnormal (Y is high), and the two are highly correlated (Z is high), it can be determined that it is a combined fault of internal contact erosion + mechanical jamming of the equipment.

[0047] In this embodiment, data points act as a "three-dimensional coordinate fingerprint" of the device's health. Three key indicators (current, vibration, and correlation) calculated from real-time monitoring data are used to mark their locations within a virtual cube. By comparing these indicators against pre-set "fault zone boundaries," the system automatically determines whether the device is in a normal, single-fault, or combined-fault state. This approach is more intelligent than traditional single-indicator analysis and can proactively identify hidden, correlated faults.

[0048] Step S108: determining a corresponding maintenance strategy according to the current fault status, and generating a reliability evaluation report and sending it to the management terminal.

[0049] Among them, a detailed reliability assessment report is generated according to the fault status of the switchgear. The report includes information such as fault type, fault cause analysis, and maintenance strategy. It is sent to the management terminal for staff to respond and process in a timely manner, providing equipment management personnel with detailed fault analysis and early warning information, which helps to improve the reliability and operational efficiency of the switchgear.

[0050] In the above implementation, the harmonic distortion rate of the electrical system and the vibration characteristics of the mechanical system are correlated and analyzed, and a dynamically coupled cross-domain evaluation system is constructed. By accurately monitoring the health status of the switchgear under complex working conditions, it is possible to effectively identify electrical, mechanical, and composite faults, generate fault reports in a timely manner, and propose maintenance strategies, thereby significantly improving the operational reliability and safety of the equipment, helping to extend the life of the equipment and improve the overall reliability of the system.

[0051] Reference Figure 2 As an implementation method of step S103, the step of extracting features from the vibration spectrum data to obtain a vibration main frequency amplitude sequence includes: Step S201, performing data preprocessing on the vibration spectrum data; Data preprocessing is a fundamental step in signal processing. Its purpose is to remove noise, smooth data, and address missing values to improve the accuracy and stability of subsequent feature extraction. Vibration signals are often subject to external interference (such as electromagnetic noise and sensor errors). This noise can affect spectral analysis results and lead to incorrect fault diagnosis.

[0052] Step S202, performing fast Fourier transform on the pre-processed vibration spectrum data to convert the time domain signal into a frequency domain signal to obtain a spectrum feature; The Fourier transform is a mathematical tool that converts time-domain signals into frequency-domain signals. By converting vibration time-domain signals into frequency-domain signals through FFT, the amplitude of each frequency component can be visualized, allowing analysis of the frequency characteristics of the vibration signal. Frequency-domain analysis can reveal the frequency distribution and energy concentration areas within a mechanical system, helping to identify potential mechanical failures.

[0053] Step S203, decomposing the spectrum feature into multiple sub-bands, and calculating the kurtosis and spectral entropy of each sub-band; Decomposing the spectrum into multiple subbands allows for more detailed analysis of the signal's frequency regions. The characteristics of each subband can reveal local features of the vibration signal. Kurtosis, a statistic that describes the sharpness of a signal, and spectral entropy, a measure of signal complexity, effectively distinguish the characteristics of different frequency bands, providing a basis for further screening of sensitive features.

[0054] Step S204: selecting sub-frequency bands that meet preset requirements as sensitive frequency bands based on kurtosis and spectral entropy; By selecting sub-bands that meet specific kurtosis and spectral entropy conditions, early signals of mechanical failure can be more sensitively captured. For example, higher kurtosis and lower spectral entropy typically indicate the presence of sharper frequency components in the signal, often associated with mechanical failures such as jamming and loosening.

[0055] In some embodiments, a frequency band with a kurtosis greater than 3 and a spectral entropy less than 1.5 may be selected as a preset screening condition for the sensitive frequency band.

[0056] Step S205, extracting the main frequency amplitude of the sensitive frequency band to obtain sensitive features; Extracting the dominant frequency amplitude from the selected sensitive frequency bands accurately reflects the vibration intensity of the fault-related frequency band. A larger dominant frequency amplitude generally indicates stronger vibration in that frequency band, which may be a sign of a fault.

[0057] Step S206 , calculating the vibration main frequency amplitude according to the sensitive features, and arranging them in chronological order to obtain a vibration main frequency amplitude sequence.

[0058] The main frequency amplitudes of the extracted sensitive frequency bands are arranged in chronological order to form a vibration main frequency amplitude sequence. This sequence reflects the health status of mechanical components at different time points and is a key indicator for monitoring wear and failure of mechanical components.

[0059] In the above implementation, the early high-frequency vibration characteristics of mechanical wear, looseness and other faults can be captured. These characteristics provide a reliable basis for subsequent fault diagnosis and equipment health monitoring, helping equipment management personnel to effectively detect and prevent faults.

[0060] Reference Figure 3 As an implementation method of step S104, the steps of performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix and calculating the dynamic coupling coefficient include: Step S301: based on a preset time window length, the harmonic distortion rate sequence and the vibration main frequency amplitude sequence are divided into a number of time windows to obtain a window data set; The preset time window length T needs to cover the typical operating cycle of the equipment (such as a 60-second start-stop cycle of a rolling mill) to ensure that the complete mechanical-electrical interaction process is captured. The window sliding step size s = 0.5T (50% overlap) can be configured to maintain timing continuity and avoid transient signal truncation (such as tripping arc shock).

[0061] For example, assuming that the harmonic distortion rate sequence is H(t), the vibration main frequency amplitude sequence is V(t), the window length is w, and the window sliding step is s, the data sets in the kth time window are: H k =[H(t),H(t+s),…,H(t+w)]; V k =[V(t),V(t+s),…,V(t+w)].

[0062] Step S302, calculating the covariance between the harmonic distortion rate sequence and the vibration main frequency amplitude sequence in each time window in the window data set, and constructing a sliding covariance matrix; Covariance is used to measure the strength of the linear relationship between two variables and the direction of its change. In the embodiments of the present application, the covariance matrix reveals the correlation between the electrical and mechanical systems within a specific time period by calculating the covariance between the harmonic distortion rate and the amplitude of the main vibration frequency within each time window. By calculating the covariance matrix for each time window, the dynamic correlation between the electrical and mechanical systems can be observed.

[0063] Specifically, the formula for calculating covariance is: ; In the above formula, H i and V i are the harmonic distortion rate value and the vibration main frequency amplitude at the i-th moment, μ h and μ v are the means of the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, respectively, and w is the window length.

[0064] Step S303, performing singular value decomposition on the sliding covariance matrix in each time window, extracting the principal components of the sliding covariance matrix and calculating the coupling coefficient; Singular value decomposition (SVD) is used to extract principal components from the covariance matrix, which helps reveal the main coupling modes between the electrical and mechanical systems. SVD decomposes a matrix into multiple components, helping to extract the most important patterns of variation in the covariance matrix. By extracting principal components, redundant information can be reduced, focusing on the most representative interactions between the electrical and mechanical systems.

[0065] Specifically, let the covariance matrix be C k , then SVD decomposition is: C k =U k Σ k V k T Among them, U k is the left singular vector matrix of the covariance matrix in the kth time window, V k T is the transpose of the right singular vector matrix of the covariance matrix in the kth time window, Σ k is the singular value matrix of the covariance matrix in the kth time window.

[0066] Furthermore, the coupling coefficient can be calculated by the principal component, and the coupling coefficient is: ; In the above formula, σ1 is the maximum singular value of the covariance matrix, σ i is the i-th singular value of the covariance matrix, and n is the number of singular values of the covariance matrix.

[0067] Step S304 : performing time-varying update on the coupling coefficient based on a preset forgetting factor to obtain a dynamic coupling coefficient sequence.

[0068] To address dynamic changes in system state, a forgetting factor is introduced to perform a time-varying update of the coupling coefficient. The forgetting factor is a constant less than 1 that gradually reduces the influence of older observations on the current coupling coefficient, reflecting the system's dynamic evolution over time. This update mechanism enables the coupling coefficient to adapt in real time to changes in the electrical and mechanical system states.

[0069] Specifically, the preset forgetting factor is configured as α, and the dynamic coupling coefficient update formula is: C couple (k)=αC couple (k−1)+(1−α)C couple (k); In the above formula, Ccouple (k-1) is the coupling coefficient at the k-1th moment, and α can be configured to 0.9 to achieve time-varying updates.

[0070] In the above implementation, cross-domain correlation analysis of electrical and mechanical systems is effectively achieved. By dividing the time window, the dynamic characteristics of the electrical and mechanical systems are converted into analyzable local data. Then, the main coupling modes between the systems are extracted through the sliding covariance matrix and SVD. Then, the changes in the system status are reflected in real time through the dynamic update mechanism. The dynamic coupling coefficient sequence finally obtained can accurately reveal the mutual influence of the electrical and mechanical systems under different working conditions, providing a scientific basis for fault diagnosis and equipment maintenance.

[0071] Reference Figure 4 As an implementation method of step S105, the steps of normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence and generating a three-dimensional feature space distribution map include: Step S401, normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence based on a preset target range; To map data of different units and dimensions into the same feature space, the harmonic distortion rate, vibration frequency amplitude, and dynamic coupling coefficient must be normalized. Normalization converts data of different scales to the same preset target range (usually [0, 1] or [-1, 1]), ensuring that they have the same influence on subsequent calculations.

[0072] Step S402 , mapping the normalized harmonic distortion rate sequence, vibration main frequency amplitude sequence, and dynamic coupling coefficient sequence into a three-dimensional feature space, defining coordinate axes, and constructing point cloud data; Specifically, the three normalized features (harmonic distortion rate, vibration main frequency amplitude, and dynamic coupling coefficient) are mapped into the three-dimensional feature space. The position of each data point in the three-dimensional space is determined by the normalized values of these three features. The normalized features at all times constitute the point cloud data in the three-dimensional feature space.

[0073] Step S403: obtaining a three-dimensional feature space distribution map based on the point cloud data.

[0074] Among them, by constructing point cloud data in three-dimensional space, these data points can be visualized in three-dimensional charts, and their distribution can be further analyzed to help analysts more intuitively understand the relationship between features under different working conditions.

[0075] Specifically, 3D visualization tools can be used to plot point cloud data, generating a 3D distribution graph. In the graph, the position of each point represents the feature value at that moment, and different areas of the graph correspond to different categories of device status (such as normal, mechanical degradation, and electrical degradation). This 3D distribution graph allows users to clearly visualize the distribution of features under different states, identify relationships and interactions between features, and provide more insightful visual analysis results.

[0076] In the above implementation, normalization and the construction of a three-dimensional feature space enable the comparison and visualization of different types of data within a unified space. This technical solution intuitively displays the health status of electrical and mechanical systems and their interrelationships, helping maintenance personnel quickly identify different failure modes and maintenance priorities. Ultimately, the three-dimensional spatial distribution map enables in-depth analysis of equipment status, providing more accurate fault prediction and management strategies.

[0077] Reference Figure 5 As a further implementation of the switchgear operation reliability evaluation method, after the steps of constructing the sliding covariance matrix and calculating the dynamic coupling coefficient, the method further includes: Step S501, extracting the change rate of the dynamic coupling coefficient within multiple sampling periods; In the health monitoring of mechanical and electrical systems, the dynamic coupling coefficient reflects the strength of the connection between the two systems. By extracting the rate of change of the dynamic coupling coefficient over multiple sampling periods, we can help identify the changing trends of the system status. A large rate of change in the coupling coefficient usually indicates that the system is undergoing a more drastic change, which may be a precursor to a failure.

[0078] Step S502, determining whether the number of times the rate of change is greater than a preset threshold in consecutive sampling periods exceeds a preset number; if so, skipping to step S503; if not, re-executing step S502 for the next sampling period; Among them, the rate of change of the dynamic coupling coefficient at a single moment may be affected by random fluctuations. Therefore, it is necessary to confirm whether there is an abnormal trend through judgment of multiple consecutive sampling periods. By setting a preset threshold and judging whether the number of times the rate of change is greater than the threshold exceeds the preset number, continuous abnormal changes can be effectively identified.

[0079] Step S503: outputting a mechanical-electrical composite degradation alarm signal, generating a corresponding maintenance strategy and sending it to a management terminal.

[0080] If the rate of change of the dynamic coupling coefficient exceeds the threshold multiple times in a row, it indicates that the interaction between the mechanical and electrical systems may have significantly changed, and the system may be experiencing an electrical or mechanical failure, or a combination of both. At this point, an alarm signal is sent to the equipment management personnel. The system then generates a corresponding fault strategy based on the fault type, providing them with specific operational guidance for rapid response and resolution.

[0081] In this implementation, the rate of change of the dynamic coupling coefficient is monitored, screened, and statistically analyzed. Combined with the set threshold and the changes within consecutive sampling periods, this effectively identifies potential combined degradation issues in the mechanical and electrical systems. Upon detecting an anomaly, the system automatically outputs an alarm signal and generates a corresponding maintenance instruction set, promptly transmitting fault information to equipment management personnel. This technical solution enables early warning intervention before a fault occurs, reducing equipment downtime, improving equipment reliability and maintenance efficiency, and ultimately optimizing equipment lifecycle management.

[0082] The embodiment of the present application also discloses a switch cabinet operation reliability evaluation system.

[0083] A switchgear operation reliability evaluation system, comprising: A data receiving module is used to receive real-time monitoring data of the switchgear to be inspected and synchronize the timestamp; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; The current signal processing module is used to perform fast Fourier transform on the current waveform data, extract the integer multiple frequency components of the fundamental frequency, and calculate the harmonic distortion rate sequence; The vibration data processing module is used to extract features from the vibration spectrum data and obtain the vibration main frequency amplitude sequence; Dynamic coupling coefficient generation module, used to perform correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, construct the sliding covariance matrix and calculate the dynamic coupling coefficient; The three-dimensional spatial distribution module normalizes the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generates a three-dimensional feature space distribution map; A feature region division module is used to divide the three-dimensional feature space distribution map into multiple feature regions based on density clustering; The fault status determination module determines the current fault status of the switchgear to be detected based on the feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; Determine the corresponding maintenance strategy based on the current fault status, and generate a reliability assessment report and send it to the management terminal.

[0084] In this implementation, by processing vibration spectrum and current waveform data in real time, key features are extracted and dynamic coupling coefficients are calculated. A three-dimensional feature space distribution map is constructed and then segmented into feature regions to identify the health status of the switchgear. Density cluster analysis allows the system to accurately delineate normal and abnormal operating conditions, providing a reliable basis for fault early warning. When an abnormal condition is detected, the system automatically generates a maintenance strategy and assessment report, which is promptly transmitted to the management terminal.

[0085] In actual application, the system of this application can effectively improve the accuracy and response speed of equipment management, reduce the risk of failure, extend the service life of equipment, and optimize the allocation of maintenance resources, which has important practical significance and application value.

[0086] As a further embodiment of the switchgear operation reliability evaluation system, the system further includes: A change rate extraction module is used to extract the change rate of the dynamic coupling coefficient within multiple sampling periods; A judgment module is used to judge whether the number of times the rate of change is greater than a preset threshold in a continuous sampling period exceeds a preset number; if so, output an abnormal result; an alarm module, configured to output a mechanical-electrical composite degradation alarm signal in response to an abnormal result; The maintenance prompt module is used to generate corresponding maintenance strategies based on mechanical-electrical composite degradation alarm signals and send them to the management terminal.

[0087] In the above implementation, when the rate of change exceeds a preset threshold within a continuous sampling period, the system automatically detects and outputs an abnormality, triggering a mechanical-electrical combined degradation alarm. This signal promptly notifies management personnel, preventing the fault from escalating. Furthermore, the system generates a corresponding maintenance strategy based on the alarm information and automatically transmits it to the management terminal, providing precise repair guidance. This system can effectively improve the response speed of fault warnings, reduce equipment downtime, optimize equipment maintenance management, and enhance system reliability and operational efficiency.

[0088] A switch cabinet operation reliability evaluation system according to an embodiment of the present application can implement any of the above-mentioned evaluation methods, and the specific working process of each module in the evaluation system can refer to the corresponding process in the above-mentioned method embodiment.

[0089] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a module is merely a logical functional division, and in actual implementation, other division methods may be used, such as combining or integrating multiple modules into another system, or ignoring or not implementing certain features.

[0090] The embodiment of the present application also discloses a computer device.

[0091] The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the switch cabinet operation reliability evaluation method as described above is implemented.

[0092] The embodiment of the present application also discloses a computer-readable storage medium.

[0093] A computer-readable storage medium stores a computer program that can be loaded by a processor and executed by any one of the above-mentioned methods for evaluating the operation reliability of a switch cabinet.

[0094] Among them, computer-readable storage media can be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0095] It should be noted that, in the above embodiments, the description of each embodiment has different emphases. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A switchgear operation reliability evaluation method, characterized in that: The evaluation method includes: Receive real-time monitoring data of the switchgear to be inspected and perform time stamp synchronization; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; Performing a fast Fourier transform on the current waveform data, extracting integer multiple frequency components of the fundamental frequency, and calculating a harmonic distortion rate sequence; Performing feature extraction on the vibration spectrum data to obtain a vibration main frequency amplitude sequence; Performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix and calculating a dynamic coupling coefficient; Normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generating a three-dimensional feature space distribution map; Dividing the three-dimensional feature space distribution map into a plurality of feature regions based on density clustering; Determining a current fault state corresponding to the switchgear to be detected according to a feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; A corresponding maintenance strategy is determined according to the current fault status, and a reliability assessment report is generated and sent to the management terminal.

2. A switchgear operation reliability evaluation method according to claim 1, characterized in that: The characteristic areas of the three-dimensional characteristic space distribution diagram include a normal operating condition area, an electrical degradation area, a mechanical degradation area, and a compound fault area.

3. A switchgear operation reliability evaluation method according to claim 1, characterized in that: The step of extracting features from the vibration spectrum data to obtain a vibration main frequency amplitude sequence includes: performing data preprocessing on the vibration spectrum data; Perform fast Fourier transform on the preprocessed vibration spectrum data to convert the time domain signal into the frequency domain signal to obtain the spectrum characteristics; Decomposing the spectrum feature into a plurality of sub-bands, and calculating the kurtosis and spectral entropy of each sub-band; According to the kurtosis and spectral entropy, selecting sub-frequency bands that meet preset requirements as sensitive frequency bands; Extracting the main frequency amplitude of the sensitive frequency band to obtain sensitive features; The vibration main frequency amplitude is calculated based on the sensitive characteristics and arranged in chronological order to obtain a vibration main frequency amplitude sequence.

4. A switchgear operation reliability evaluation method according to claim 1, characterized in that: The steps of performing correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, constructing a sliding covariance matrix and calculating a dynamic coupling coefficient include: Based on a preset time window length, the harmonic distortion rate sequence and the vibration main frequency amplitude sequence are divided into a plurality of time windows to obtain a window data set; Calculating the covariance between the harmonic distortion rate sequence and the vibration main frequency amplitude sequence in each time window in the window data set to construct a sliding covariance matrix; Perform singular value decomposition on the sliding covariance matrix within each time window, extract the principal components of the sliding covariance matrix and calculate the coupling coefficient; The coupling coefficient is updated in a time-varying manner based on a preset forgetting factor to obtain a dynamic coupling coefficient sequence.

5. A switchgear operation reliability evaluation method according to claim 4, characterized in that: The steps of normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence and generating a three-dimensional feature space distribution map include: Normalizing the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence based on a preset target range; Mapping the normalized harmonic distortion rate sequence, vibration main frequency amplitude sequence, and dynamic coupling coefficient sequence into a three-dimensional feature space, defining coordinate axes, and constructing point cloud data; A three-dimensional feature space distribution map is obtained based on the point cloud data.

6. A switchgear operation reliability assessment method according to any one of claims 1 to 5, characterized in that: After the steps of constructing the sliding covariance matrix and calculating the dynamic coupling coefficients, also include: extracting a rate of change of the dynamic coupling coefficient over a plurality of sampling periods; Determining whether the number of times the change rate is greater than a preset threshold value in consecutive sampling periods exceeds a preset number; If so, a mechanical-electrical composite degradation alarm signal is output, and a corresponding maintenance strategy is generated and sent to the management terminal.

7. A switchgear operation reliability evaluation system, characterized in that: The evaluation system comprises: A data receiving module, configured to receive real-time monitoring data of the switchgear to be inspected and synchronize the time stamp; wherein the real-time monitoring data includes vibration spectrum data and current waveform data; A current signal processing module is used to perform a fast Fourier transform on the current waveform data, extract the integer multiple frequency components of the fundamental frequency, and calculate the harmonic distortion rate sequence; A vibration data processing module, configured to extract features from the vibration spectrum data to obtain a vibration main frequency amplitude sequence; A dynamic coupling coefficient generation module is used to perform correlation analysis on the harmonic distortion rate sequence and the vibration main frequency amplitude sequence, construct a sliding covariance matrix and calculate the dynamic coupling coefficient; A three-dimensional spatial distribution module normalizes the harmonic distortion rate sequence, the vibration main frequency amplitude sequence, and the dynamic coupling coefficient sequence, and generates a three-dimensional feature space distribution map; A feature region division module, configured to divide the three-dimensional feature space distribution map into a plurality of feature regions based on density clustering; a fault state determination module, which determines the current fault state of the switchgear to be detected according to the feature area mapped by the real-time monitoring data in the three-dimensional feature space distribution map; A corresponding maintenance strategy is determined according to the current fault status, and a reliability assessment report is generated and sent to the management terminal.

8. A switchgear operation reliability evaluation system according to claim 7, characterized in that: The system further comprises: A change rate extraction module, configured to extract the change rate of the dynamic coupling coefficient within a plurality of sampling periods; a judgment module, configured to judge whether the number of times the change rate is greater than a preset threshold value in consecutive sampling periods exceeds a preset number; if so, output an abnormal result; an alarm module, configured to output a mechanical-electrical composite degradation alarm signal in response to the abnormal result; The maintenance prompt module is used to generate a corresponding maintenance strategy according to the mechanical-electrical composite degradation alarm signal and send it to the management terminal.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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