Intelligent inspection and preventive maintenance management method for control cabinet

Abstract

The invention relates to the technical field of maintenance and management of industrial automation equipment, and discloses a control cabinet intelligent inspection and preventive maintenance and management method, which comprises the following steps: S10, data acquisition and preprocessing; s20, extracting features and constructing a database; s30, training an AI prediction model; s40, performing intelligent inspection and anomaly detection; and S50, performing maintenance and effect evaluation. By integrating sensor data, historical fault records and the AI prediction model, efficient and accurate routing inspection and maintenance plan making of the control cabinet are realized, so that the equipment reliability is improved, and the non-planned downtime is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance management of industrial automation equipment, and in particular to an intelligent inspection and preventive maintenance management method for a control cabinet. Background Art

[0002] With the rapid development of industrial automation technology, control cabinets, as the core control equipment in the entire production process, have become the cornerstone for ensuring efficient and safe production operations due to their stability and reliability. Control cabinets integrate complex electrical control systems and advanced automation technologies, and can accurately adjust and monitor the operating status of production equipment. They are the key hub for realizing automated production. During the production process, any failure or instability of the control cabinet may lead to shutdown of the production line, deterioration of product quality, or even safety accidents, causing huge economic losses and reputation risks to the company. Therefore, improving the stability and reliability of control cabinets is not only related to improving production efficiency, but is also an important part of ensuring production safety and product quality. However, although the stability and reliability of control cabinets are crucial to industrial automation production, traditional inspection and maintenance methods face many challenges and defects. These methods are often highly dependent on manual experience and regular inspection patterns, leading to a series of problems that cannot be ignored. First, the long inspection cycle is a major drawback of traditional inspection methods. Since each component and system of the control cabinet needs to be checked manually one by one, it is not only time-consuming and labor-intensive, but also difficult to ensure the frequency and comprehensiveness of the inspection. This low-frequency inspection method often fails to detect potential equipment failures or performance degradation in a timely manner, thereby increasing the probability of failure and the severity of the consequences; secondly, delayed fault detection is another significant problem. Traditional inspection methods usually only conduct inspections after equipment failures occur. This "after-the-fact remediation" approach not only affects production progress, but may also cause greater economic losses. In addition, since the failure has already occurred, the causes and mechanisms behind it may have become complex and difficult to trace, increasing the difficulty and cost of repair. Furthermore, low maintenance efficiency is also a major pain point of traditional inspection and maintenance methods. Since inspection and maintenance work mainly rely on manual work, it is not only labor-intensive, but also easily affected by human factors. For example, the experience level, work attitude and fatigue level of the inspectors may affect the quality and effectiveness of the inspection. At the same time, traditional methods lack intelligent data analysis and prediction capabilities, and cannot accurately evaluate and predict the operating status of the equipment, thereby limiting the efficiency and accuracy of maintenance work. Summary of the invention

[0003] In order to solve the current technical problems, the main purpose of the present invention is to provide a control cabinet intelligent inspection and preventive maintenance management method, which realizes efficient and accurate inspection and maintenance planning of the control cabinet by integrating sensor data, historical fault records and AI prediction models, thereby improving equipment reliability and reducing unplanned downtime.

[0004] The technical solution adopted by the present invention is: a control cabinet intelligent inspection and preventive maintenance management method, comprising: S10, data collection and preprocessing: real-time collection of operating data inside the control cabinet, and preprocessing of the collected raw data; S20, feature extraction and database construction: extract key features from the preprocessed data, construct a feature vector of the control cabinet operation status, establish a control cabinet operation status database, and store historical data, fault records and corresponding feature vectors; S30, AI prediction model training: Use deep learning algorithms to train AI prediction models with historical fault records as labels; S40, Intelligent Inspection and Anomaly Detection: Input the real-time collected data into the trained AI prediction model to perform online inspection and anomaly detection. When the model identifies an abnormal pattern or predicts a potential failure, it triggers an alarm and generates a detailed fault report. S50, Maintenance and effect evaluation: Perform maintenance work according to the preventive maintenance plan, record the data and results of the maintenance process, evaluate the maintenance effect, and collect feedback information to optimize the AI ​​prediction model and maintenance plan.

[0005] Furthermore, S10 specifically includes the following steps: S101, Sensor deployment: Deploy multiple types of sensors at key locations inside the control cabinet, including temperature sensors, humidity sensors, vibration sensors, and current and voltage sensors; S102, data acquisition: reading data from the sensor periodically or continuously through the data acquisition system, and transmitting the data to the data processing unit; S103, data cleaning: preliminarily processing the collected raw data to remove duplicate data and erroneous data; S104, denoising: using one of low-pass filtering, high-pass filtering, and band-pass filtering to denoise the sensor signal; S105. Data standardization: use linear standardization or Z-score standardization to standardize the data; S106, data storage: storing the cleaned, denoised and standardized data in a database.

[0006] Among them, temperature sensors are installed in areas inside the control cabinet where heat is easily accumulated. Humidity sensors are usually installed at the top or bottom of the control cabinet, close to the vents. Vibration sensors are installed on the supporting structure or fastening points of key equipment in the control cabinet. Current and voltage sensors are installed on the power input terminal, main load lines and key circuit nodes in the control cabinet.

[0007] Furthermore, the specific operation steps in S20 include: S201, define feature set: define a set of key features according to the operating characteristics and monitoring requirements of the control cabinet to fully reflect the operating status and potential faults of the control cabinet; S202, data segmentation: segmenting the preprocessed data according to time or event to form data segments; S203, feature calculation: for each data segment, calculate the defined feature value; S204, constructing a feature vector: combining the calculated feature values ​​into a feature vector, which is used to represent the operating state of the control cabinet in this data segment; S205, designing database structure: designing the structure of the database according to the feature extraction results and storage requirements, including determining the name, field, data type and index of the data table; S206, storing historical data: storing the pre-processed historical data in the database according to the designed database structure.

[0008] Furthermore, in S206, for known fault events, the occurrence time, fault type, fault manifestation and corresponding feature vector information are recorded, and the association relationship between historical data, fault records and feature vectors is established in the database by setting foreign keys, indexes or creating views. Furthermore, S30 specifically includes the following operation steps: S301, data preparation and preprocessing: divide the preprocessed data into a training set, a validation set, and a test set, and use historical fault records to assign corresponding fault categories or status labels to the training set data; S302, model selection and initialization: According to the data characteristics and prediction task requirements, the parameters are randomly initialized through the deep learning model to make the starting point of model training consistent; S303, model training and optimization: Use training set data to train the model, adjust model parameters through back propagation algorithm and optimizer, use cross-validation method to evaluate model performance, and adjust model parameters accordingly to improve generalization ability, and find the optimal hyperparameter combination through grid search, random search or Bayesian optimization method to optimize model performance; S304, model evaluation and adjustment: Use the test set data to evaluate the prediction accuracy and generalization ability of the model, calculate the accuracy, precision, recall and F1 score indicators, and optimize the model performance based on the evaluation results; S305, model deployment and application: Deploy the trained and optimized model to the actual application scenario, and use the deployed model to monitor the operating status of the control cabinet in real time, predict potential faults, and provide intelligent support for operation and maintenance.

[0009] Furthermore, S40 specifically includes the following operation steps: S401, model input and online prediction: The preprocessed real-time data is passed as input to the trained AI prediction model. The model performs online prediction based on the input data, analyzes abnormal patterns in the data, or predicts potential failures. S402, anomaly detection and judgment: the model identifies the abnormal pattern in the data, and further judges the prediction result of the model in combination with the preset threshold or confidence level to determine whether there is actually an abnormality or fault; S403, alarm triggering and notification: Once an abnormality is confirmed or a potential failure is predicted, the alarm mechanism is triggered; S404, fault report generation: collect all information related to the abnormality or fault, including the occurrence time, abnormal parameters, predicted results and possible causes, and automatically generate a detailed fault report based on the collected information.

[0010] Furthermore, it also includes: S405, follow-up processing and feedback: The operation and maintenance personnel conduct troubleshooting and diagnosis according to the fault report, determine the specific cause, take corresponding processing measures according to the diagnosis results, repair the fault or adjust the equipment parameters to restore normal operation, record the processing results and feedback information in the system, and update the operation status database of the control cabinet at the same time; S406. Continuous optimization and improvement: Regularly evaluate the performance of the AI ​​prediction model, and continuously optimize and improve the model based on the evaluation results and new data feedback to improve its prediction accuracy and generalization ability.

[0011] After the S40 intelligent inspection and anomaly detection, the process also includes formulating a preventive maintenance plan based on the fault report and the prediction results of the AI ​​model, combined with the maintenance history of the control cabinet and the equipment manual. The maintenance plan includes maintenance time, maintenance content, required spare parts and personnel arrangements.

[0012] Furthermore, S50 specifically includes the following steps: S501, equipment monitoring data: continuously collect monitoring data of the equipment during operation, including operating status and parameter changes; S502, User Feedback: Communicate with users to understand their satisfaction with the equipment maintenance effect and whether there are other problems or improvement needs during the operation of the equipment; S503, AI model optimization: Based on the collected maintenance data, equipment monitoring data and user feedback, the AI ​​prediction model is trained and optimized to improve its prediction accuracy and timeliness of equipment failures; S504, Maintenance plan adjustment: adjust and optimize the preventive maintenance plan according to the effect evaluation results and feedback information; S505. Continuous improvement: Apply the optimized AI prediction model and maintenance plan to actual production, and continue to collect data and feedback information to form a closed-loop continuous optimization mechanism.

[0013] Compared with the prior art, the technical solution of the present application has the following beneficial technical effects.

[0014] Through the data collection and preprocessing of step S10, real-time and comprehensive collection of control cabinet operation data is achieved, and scientific data cleaning, denoising and standardization processing are carried out to effectively ensure the accuracy and reliability of the data. This improvement significantly shortens the data processing cycle and improves data quality, laying a solid foundation for subsequent analysis and prediction, thus overcoming the problems of incomplete data collection and delayed processing in traditional inspection methods.

[0015] Through feature extraction and database construction in step S20, key features are extracted from massive data using advanced data analysis technology, and a feature vector database of the control cabinet operating status is constructed. This database not only stores historical data and fault records, but also contains corresponding feature vectors, providing rich and accurate data support for the training of the AI ​​model. This improvement enables the AI ​​model to more accurately capture the operating laws and fault modes of the control cabinet, improves the accuracy and reliability of the prediction, and effectively solves the problem of delayed fault detection in traditional inspection methods.

[0016] Through the AI ​​prediction model training in step S30, the deep learning algorithm is used to learn the historical fault records, and the model performance is continuously optimized through cross-validation, hyperparameter tuning and other means, which significantly improves the prediction accuracy and generalization ability of the model. This improvement enables the prediction model to more accurately predict the potential failures of the control cabinet and provide early warning, providing a scientific basis for preventive maintenance. Compared with the post-remediation mode of traditional inspection methods, this method greatly reduces the risk of failures and the severity of the consequences.

[0017] Through the intelligent inspection and anomaly detection in step S40, the real-time collected data is input into the trained AI prediction model, realizing online inspection and anomaly detection of the operating status of the control cabinet. When the model recognizes an abnormal pattern or predicts a potential fault, it can immediately trigger an alarm and generate a detailed fault report, allowing maintenance personnel to respond quickly and take appropriate measures. This improvement greatly improves the efficiency and accuracy of inspections, reduces the labor intensity of manual inspections, and overcomes the problems of long inspection cycles and low maintenance efficiency in traditional inspection methods.

[0018] Through the maintenance and effect evaluation of step S50, by executing the preventive maintenance plan and recording the data and results of the maintenance process, the maintenance effect is evaluated and feedback information is collected for continuous optimization of the AI ​​prediction model and maintenance plan. This closed-loop feedback mechanism enables the entire intelligent inspection and preventive maintenance management method to be continuously optimized and improved, thereby improving the overall management level and maintenance efficiency.

[0019] Compared with the technical defects of traditional inspection and maintenance methods, the present invention provides a more efficient and intelligent solution for control cabinet management in industrial automation production. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is a method flow chart of the intelligent inspection and preventive maintenance management method of the control cabinet of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Combination Figure 1 As shown, a control cabinet intelligent inspection and preventive maintenance management method of the present invention comprises the following steps: S10, Data collection and preprocessing: Deploy multiple types of sensors inside the control cabinet to collect the operating data of the equipment in real time, and perform preprocessing operations such as cleaning, denoising, and standardization on the collected raw data to ensure data quality.

[0024] Specifically, step S10 includes the following steps: S101, sensor deployment: deploy multiple types of sensors at key locations inside the control cabinet, including but not limited to temperature sensors, humidity sensors, vibration sensors, current and voltage sensors, etc. These sensors are used to monitor the environmental parameters inside the control cabinet and the operating status of the equipment in real time; S102, data acquisition: reading data from the sensor periodically or continuously through a data acquisition system such as a data acquisition card, PLC, etc., and transmitting the data to a data processing unit; S103, data cleaning: preliminarily process the collected raw data to remove duplicate data, erroneous data such as data that is obviously beyond the normal range, and invalid data caused by equipment failure or sensor abnormality; S104, denoising: using one of the methods such as low-pass filtering, high-pass filtering, and band-pass filtering to denoise the signal in the sensor signal, including noise, such as random fluctuations caused by electromagnetic interference, mechanical vibration, etc.; S105, data standardization: To eliminate the dimensional differences between different sensor data and improve the efficiency and accuracy of data processing, the data needs to be standardized, where the standardization method is one of linear standardization, Z-score standardization, etc.; S106. Data storage: The cleaned, denoised and standardized data shall be stored in the database for subsequent data analysis and processing. The database shall have the characteristics of high reliability, high concurrency and easy query.

[0025] During the data collection process, temperature sensors are mainly installed in areas inside the control cabinet where heat is easily accumulated, such as near the power module, around high-power components, and at the air outlet of the cooling fan. Humidity sensors are usually installed at the top or bottom of the control cabinet, close to the vents, in order to accurately measure the humidity level inside the control cabinet. Vibration sensors are installed on the supporting structure or fastening points of key equipment in the control cabinet, such as the motor base, transmission bearing seat, etc. These sensors can capture the tiny vibration signals generated during the operation of the equipment. By analyzing the vibration spectrum, mechanical failures or wear of the equipment can be discovered in time. Current and voltage sensors are installed on the power input terminal, main load lines, and key circuit nodes in the control cabinet. They can monitor the current and voltage values ​​in the circuit in real time to ensure that the equipment operates stably within the rated range.

[0026] By deploying multiple types of sensors at key locations inside the control cabinet, comprehensive real-time monitoring of the internal environmental parameters of the control cabinet and the operating status of the equipment is achieved, which significantly shortens the inspection cycle, improves the real-time and comprehensiveness of the inspection, and can discover potential problems earlier. The data cleaning, denoising and data standardization steps ensure the quality of the collected data, remove duplicate, erroneous and invalid data, reduce noise interference, eliminate dimensional differences, and provide a high-quality data foundation for subsequent data analysis and prediction models, improving the accuracy and reliability of the analysis. The automated process of data collection, processing and storage reduces manual intervention, reduces the risk of human error, and improves the intelligence and automation level of maintenance management, making maintenance work more efficient and accurate. The processed data is stored in a high-reliability, high-concurrency, and easy-to-query database, which is convenient for subsequent data analysis and rapid retrieval, supports real-time data analysis and historical data backtracking, and provides strong data support for troubleshooting and preventive maintenance.

[0027] In order to improve the maintenance efficiency and management level of the control cabinet, a company implemented the following specific measures: Inside the control cabinet, the company deployed various types of sensors at key locations based on the equipment layout and operating characteristics, including temperature sensors installed around the power module, high-power components and cooling fan outlets, humidity sensors installed near the vents on the top and bottom of the control cabinet, vibration sensors installed on the supporting structures of key equipment such as the motor base and transmission bearing seat, and current and voltage sensors installed at the power input terminal, main load lines and key circuit nodes.

[0028] The company uses PLC as a data acquisition system, which reads data from sensors at regular intervals and transmits the data to the data processing unit via wired or wireless means. In the data processing unit, the data cleaning step is first performed, and the algorithm is used to identify and remove duplicate data, data that is obviously beyond the normal range, and invalid data caused by equipment failure or sensor abnormality. After that, the sensor signal is denoised, and a low-pass filter method is used to remove random fluctuations caused by electromagnetic interference. The data is then standardized, and the Z-score standardization method is used to eliminate the dimensional differences between different sensor data.

[0029] The cleaned, denoised and standardized data is stored in a database with high reliability, high concurrency and easy query characteristics. The enterprise uses the data analysis platform supported by the database to monitor and analyze the operating status of the control cabinet in real time, detect potential faults in time and formulate corresponding preventive maintenance plans. Through this embodiment, the enterprise successfully improved the maintenance efficiency and management level of the control cabinet, reduced the fault rate, and ensured the stable operation of the production line.

[0030] S20. Feature extraction and database construction: Extract key features from the preprocessed data, such as temperature change trends, vibration spectra, etc., construct feature vectors of the control cabinet operating status, establish a control cabinet operating status database, store historical data, fault records and corresponding feature vectors, and provide data support for AI model training.

[0031] Specifically, the specific operation steps in step S20 include: S201, define feature set: define a set of key features according to the operating characteristics and monitoring requirements of the control cabinet, which should be able to fully reflect the operating status and potential faults of the control cabinet; S202, data segmentation: segmenting the preprocessed data according to time or event, so as to extract features from the data in each time period or event; S203, feature calculation: for each data segment, calculate the defined feature value, which may require the use of statistical analysis, signal processing or machine learning and other technical means. For example, the temperature change trend can be obtained by calculating the slope or change rate of the temperature data; the vibration spectrum can be obtained by performing Fourier transform and other spectrum analysis methods on the vibration signal; S204, constructing a feature vector: combining the calculated feature values ​​into a feature vector for representing the operating status of the control cabinet in the data segment; S205, designing database structure: designing the database structure according to the feature extraction results and storage requirements, which includes determining the name, field, data type, index, etc. of the data table; S206. Store historical data: Store the preprocessed historical data in the database according to the designed database structure. These data are the basis for subsequent AI model training and verification. For known fault events, record the time of occurrence, fault type, fault manifestation, and corresponding feature vectors. This information is crucial for training AI models that can identify fault modes. Establish an association between historical data, fault records, and feature vectors in the database. This can be achieved by setting foreign keys, indexes, or creating views.

[0032] By defining a key feature set that can comprehensively reflect the operating status and potential faults of the control cabinet, a clear direction and basis is provided for data analysis, and the pertinence and effectiveness of data analysis are enhanced. The data is segmented according to time or event, and characteristic values ​​are calculated for different data segments, which enables a detailed characterization of the operating status of the control cabinet, facilitating subsequent data analysis and fault identification. The calculated characteristic values ​​are combined into characteristic vectors, which provide a standardized representation for the operating status of the control cabinet, facilitating comparison and analysis between different time periods or different devices. The database structure is designed according to the results of feature extraction and storage requirements, ensuring efficient storage and rapid retrieval of data, and providing a solid foundation for subsequent data analysis and AI model training. The preprocessed historical data and its corresponding characteristic vectors, fault information, etc. are stored in the database, and the association relationship between them is established, which provides rich sample data for the training and verification of the AI ​​model and improves the accuracy and reliability of the model.

[0033] In order to improve the maintenance efficiency and management level of the control cabinet, a specific implementation is performed according to step S20.

[0034] According to the operating characteristics and monitoring requirements of the control cabinet, a feature set including multiple key features such as temperature change trend, vibration spectrum, current fluctuation, etc. is defined.

[0035] The preprocessed data is segmented according to time intervals (such as every hour). For each data segment, the defined characteristic values ​​are calculated using statistical analysis, signal processing and other technical means. For example, the temperature change trend can be reflected by calculating the slope of the temperature data; the vibration spectrum characteristics can be obtained by Fourier transforming the vibration signal.

[0036] The calculated eigenvalues ​​are combined into a eigenvector to represent the operating status of the control cabinet during the time period. According to the feature extraction results and storage requirements, a database structure including data table names, fields, data types, indexes and other elements is designed.

[0037] The preprocessed historical data and its corresponding feature vectors, fault information, etc. are stored in the database according to the designed database structure. For known fault events, the occurrence time, fault type, fault manifestation, and corresponding feature vectors are recorded in detail, and the association between historical data, fault records and feature vectors is established in the database. In this way, when the AI ​​model is subsequently trained and verified, these data can be directly extracted from the database as training samples and test samples, thereby improving the accuracy and reliability of the model. Through this embodiment, the data center has successfully improved the maintenance efficiency and management level of the control cabinet, and realized real-time monitoring of the operating status of the control cabinet and fault warning.

[0038] S30, AI prediction model training: Use deep learning algorithms to train AI prediction models with historical fault records as labels. During the model training process, cross-validation, hyperparameter tuning and other means are used to improve the model's prediction accuracy and generalization ability.

[0039] Specifically, step S30 includes the following steps: S301, data preparation and preprocessing: divide the preprocessed data into a training set, a validation set, and a test set, and use historical fault records to assign corresponding fault categories or status labels to the training set data; S302, model selection and initialization: according to the data characteristics and prediction task requirements, select a suitable deep learning model such as CNN, RNN, LSTM, etc., and randomly initialize the parameters of the model to ensure that the starting point of model training is consistent; S303, model training and optimization: Use training set data to train the model, adjust model parameters through back propagation algorithm and optimizer, use cross-validation method to evaluate model performance, and adjust model parameters accordingly to improve generalization ability, and find the optimal hyperparameter combination through grid search, random search or Bayesian optimization to optimize model performance; S304, model evaluation and adjustment: Use the test set data to evaluate the prediction accuracy and generalization ability of the model, calculate indicators such as accuracy, precision, recall rate and F1 score, and optimize the model performance by adjusting the model structure, optimizing the algorithm or increasing the training data according to the evaluation results; S305, model deployment and application: Deploy the trained and optimized model to actual application scenarios, such as the monitoring system of the control cabinet. Use the deployed model to monitor the operating status of the control cabinet in real time, predict potential failures, and provide intelligent support for operation and maintenance.

[0040] By clearly dividing the data into training sets, validation sets, and test sets and assigning fault category labels, a high-quality data foundation is provided for model training, which helps to improve the accuracy and reliability of the model. According to the data characteristics and prediction task requirements, a suitable deep learning model is selected and randomly initialized to ensure that the starting point of model training is consistent, which provides a good start for model optimization. Cross-validation, grid search, random search, or Bayesian optimization methods are used to effectively improve the efficiency and generalization ability of model training, ensure the stability and reliability of the model in different scenarios, and comprehensively evaluate the model through a variety of evaluation indicators, including accuracy, precision, recall, F1 score, etc., and adjust the model structure, optimize the algorithm, or increase training data according to the evaluation results to further improve the model performance. The trained and optimized model is directly deployed to the actual application scenario, such as the monitoring system of the control cabinet, to achieve real-time and intelligent fault prediction, and provide strong intelligent support for operation and maintenance personnel.

[0041] In the fault prediction project of the substation control cabinet, the specific implementation is carried out according to step S30.

[0042] First, the company collected the historical operation data and fault records of the control cabinet, cleaned and preprocessed the data, and divided the preprocessed data into training set, verification set and test set. At the same time, the historical fault records were used to assign corresponding fault categories or status labels to the training set data.

[0043] Based on the data characteristics and forecasting task requirements, the company selected the LSTM long short-term memory network as the deep learning model and randomly initialized the model parameters. Subsequently, the model was trained using the training set data, and the model parameters were continuously adjusted through the back propagation algorithm and optimizer. During the training process, the company used the cross-validation method to evaluate the model performance and used grid search to find the optimal hyperparameter combination to optimize the model performance.

[0044] After completing the model training, the company evaluated the model using the test set data and calculated indicators such as accuracy, precision, recall, and F1 score. Based on the evaluation results, the company fine-tuned the model structure and added some training data to improve the generalization ability of the model.

[0045] The trained and optimized LSTM model was deployed to the monitoring system of the substation control cabinet. By monitoring the operating status of the control cabinet in real time, the model can accurately predict potential faults and notify the operation and maintenance personnel in advance to handle them. This embodiment not only improves the operating efficiency and safety of the substation, but also reduces the operation and maintenance costs, bringing significant economic benefits to the power company.

[0046] S40, intelligent inspection and anomaly detection: The real-time collected data is input into the trained AI prediction model for online inspection and anomaly detection. When the model identifies an abnormal pattern or predicts a potential failure, an alarm is immediately triggered and a detailed fault report is generated.

[0047] Specifically, step S40 includes the following steps: S401, model input and online prediction, the preprocessed real-time data is passed as input to the trained AI prediction model, and the model performs online prediction based on the input data, analyzes abnormal patterns in the data or predicts potential failures; S402, anomaly detection and judgment, the model identifies abnormal patterns in the data through internal algorithms, which may indicate abnormal operation status or potential failures of the equipment. Combined with the preset threshold or confidence level, the model's prediction results are further judged to determine whether there is actually an anomaly or failure; S403, alarm triggering and notification, once an abnormality is confirmed or a potential failure is predicted, the system immediately triggers the alarm mechanism and promptly notifies the operation and maintenance personnel or relevant responsible persons through sound, light signal, SMS, email or mobile application push; S404, fault report generation, collect all information related to the anomaly or fault, including the time of occurrence, abnormal parameters, predicted results, possible causes, etc., and automatically generate a detailed fault report based on the collected information. The report should contain enough information for the operation and maintenance personnel to quickly understand the problem and take corresponding measures; S405, Subsequent processing and feedback, the operation and maintenance personnel conduct troubleshooting and diagnosis according to the fault report, determine the specific cause, take corresponding treatment measures according to the diagnosis results, repair the fault or adjust the equipment parameters to restore normal operation, and record the processing results and feedback information into the system for subsequent data analysis and model optimization. At the same time, the operation status database of the control cabinet is updated to ensure the timeliness and accuracy of the data; S406. Continuous optimization and improvement. Regularly evaluate the performance of AI prediction models, including accuracy, recall, F1 score and other indicators. Based on the evaluation results and new data feedback, continuously optimize and improve the model to improve its prediction accuracy and generalization ability.

[0048] The pre-processed real-time data is directly input into the trained AI model, which realizes the immediacy of fault prediction and can quickly respond to any changes in the equipment status. The model accurately identifies abnormal patterns in the data through internal algorithms, and makes further judgments based on preset thresholds or confidence levels, effectively reducing the false alarm rate and improving the accuracy of fault detection. Once an abnormality is detected or a potential fault is predicted, the system immediately triggers the alarm mechanism and promptly notifies relevant personnel in a variety of ways to ensure the timeliness of fault handling. It automatically generates a fault report containing rich information, providing operation and maintenance personnel with a basis for quickly understanding problems, locating causes and taking corresponding measures, improving the efficiency of fault handling. From troubleshooting and diagnosis to recording of processing results and feedback information, a complete closed-loop processing process is formed, which not only solves the current problem, but also provides valuable data support for subsequent data analysis and model optimization. The performance of the AI ​​model is evaluated regularly, and continuous optimization and improvement are carried out based on the evaluation results and new data feedback, ensuring the continuous improvement of the model's prediction accuracy and generalization ability.

[0049] In the control cabinet monitoring system, the specific implementation is performed according to step S40.

[0050] Enterprises use pre-processed real-time operation data as input and pass it to the trained AI prediction model. The model performs online prediction based on the input data, quickly analyzes abnormal patterns in the data, and predicts potential failures.

[0051] When the model identifies an abnormal pattern in the data, the system further judges the prediction results based on the preset threshold and confidence level. Once an abnormality or potential failure is confirmed, the system immediately triggers the alarm mechanism and notifies the relevant personnel in a timely manner through the sound and light alarm devices on the production line and by sending text messages and emails to the operation and maintenance personnel's mobile phones.

[0052] The system collects all information related to abnormalities or failures, including the time of occurrence, abnormal parameters, predicted results, possible causes, etc., and automatically generates a detailed fault report. The operation and maintenance personnel quickly locate the cause of the problem based on the fault report and take corresponding treatment measures, such as replacing faulty parts, adjusting equipment parameters, etc., to restore the normal operation of the production line.

[0053] After the processing is completed, the operation and maintenance personnel will record the processing results and feedback information into the system for subsequent data analysis and model optimization. At the same time, the system updates the operating status database of the control cabinet to ensure the timeliness and accuracy of the data.

[0054] In addition, the company also regularly evaluates the performance of the AI ​​prediction model by comparing the model's prediction results with the actual fault conditions and calculating indicators such as accuracy, recall, and F1 score. Based on the evaluation results and new data feedback, the company continuously optimizes and improves the model to improve its prediction accuracy and generalization ability. This implementation not only improves the operating efficiency and safety of the production line, but also brings significant economic benefits to the company.

[0055] After the intelligent inspection and anomaly detection in step S40, a personalized preventive maintenance plan is formulated based on the fault report and the prediction results of the AI ​​model, combined with the maintenance history of the control cabinet and the equipment manual. The maintenance plan includes detailed information such as maintenance time, maintenance content, required spare parts and personnel arrangements to ensure that the maintenance work is carried out in an orderly manner.

[0056] Based on actual operation data and AI prediction results, combined with the equipment maintenance history and equipment manual, a maintenance plan is tailored for each control cabinet, which improves the pertinence and effectiveness of maintenance. Through predictive maintenance plans, intervention can be carried out before a failure occurs, effectively reducing the occurrence of sudden failures and improving the reliability and stability of equipment. Detailed maintenance plans include maintenance time, content, required spare parts and personnel arrangements, which help companies optimize resource allocation, reduce unnecessary waste and improve maintenance efficiency. Personalized preventive maintenance plans can reduce downtime and repair costs caused by failures, thereby reducing overall maintenance costs. It provides management with data-based decision support, helping them better understand the status of equipment and formulate more reasonable maintenance strategies and investment plans.

[0057] In the management of its control cabinets, a personalized preventive maintenance plan was implemented. After the intelligent inspection and abnormality detection in step S40, the system generated a detailed fault report and, combined with the prediction results of the AI ​​model, conducted a comprehensive assessment of the operating status of the control cabinet.

[0058] In order to develop a personalized maintenance plan, the company's technical team first consulted the maintenance history of the control cabinet to understand the equipment's past failures and maintenance experience. At the same time, they also referred to the equipment manual to understand the control cabinet's structure, performance parameters, and maintenance requirements.

[0059] Based on the above information, the technical team developed a detailed preventive maintenance plan for each control cabinet. The plan specifies the maintenance time, including the specific dates for regular inspections and irregular inspections; the maintenance content lists the inspections, tests, cleaning, and replacements that need to be performed based on the actual conditions of the equipment and the predicted results; the required spare parts section lists the parts and tools that may be needed during the maintenance process so that they can be prepared in advance; and the personnel arrangement clarifies the specific personnel responsible for maintenance work and their responsibilities.

[0060] By implementing a personalized preventive maintenance plan, the company's control cabinets have been maintained more scientifically and effectively, the failure rate has been significantly reduced, and the reliability and stability of the equipment have been improved. At the same time, the orderly maintenance work has also optimized resource allocation, reduced maintenance costs, and provided strong guarantees for the company's production operations.

[0061] S50, maintenance and effect evaluation, perform maintenance work according to the preventive maintenance plan, record the data and results of the maintenance process, evaluate the maintenance effect, and collect feedback information to optimize the AI ​​prediction model and maintenance plan.

[0062] Specifically, step S50 includes the following steps: S501, equipment monitoring data: continuously collect monitoring data of the equipment during operation, including operating status, parameter changes, etc. These data can reflect the health status and potential problems of the equipment; S502, User feedback: Communicate with users to understand their satisfaction with the equipment maintenance effect, and whether there are other problems or improvement needs during the operation of the equipment; S503, AI model optimization: Based on the collected maintenance data, equipment monitoring data and user feedback, the AI ​​prediction model is trained and optimized to improve its prediction accuracy and timeliness of equipment failures; S504, maintenance plan adjustment: adjust and optimize the preventive maintenance plan according to the effect evaluation results and feedback information. For example, for components or systems that frequently fail, the maintenance cycle can be appropriately shortened; for components or systems with significant maintenance effects, the maintenance cycle can be maintained or appropriately extended; S505. Continuous improvement: Apply the optimized AI prediction model and maintenance plan to actual production, and continue to collect data and feedback information to form a closed-loop continuous optimization mechanism.

[0063] Continuously collect equipment monitoring data to ensure that the AI ​​prediction model can be trained and predicted based on the latest data, which improves the timeliness and accuracy of the prediction results. By collecting user feedback, it not only increases user satisfaction, but also makes the maintenance strategy closer to the actual usage scenario, which helps to discover potential problems and improvement needs. The AI ​​model is continuously optimized based on maintenance data, equipment monitoring data and user feedback to form a closed-loop model optimization mechanism to ensure that the model can continuously adapt to changes in equipment status and improve prediction accuracy. The maintenance plan is dynamically adjusted according to the effect evaluation results and feedback information, making the maintenance plan more flexible and effective, and can perform personalized maintenance according to the actual condition of the equipment. The optimized model and plan are applied to actual production, and data and feedback are continuously collected to form a closed-loop mechanism for continuous improvement, which helps to improve the efficiency and effectiveness of equipment maintenance in the long term.

[0064] The company uses the collected maintenance data, equipment monitoring data and user feedback to regularly train and optimize the AI ​​prediction model that has been deployed on the production line. By adjusting model parameters, introducing new feature variables or adopting more advanced algorithms, the company continuously improves the model's prediction accuracy and timeliness of equipment failures. Based on the prediction results and effect evaluation reports of the AI ​​model, the company dynamically adjusts the preventive maintenance plan. For example, for some parts that frequently fail, such as bearings or transmission belts, the company shortens their maintenance cycle; while for parts with significant maintenance effects, such as motors or controllers, the company maintains or appropriately extends their maintenance cycle. In addition, the company also fine-tunes the maintenance plan based on user feedback to meet the actual needs of users. The company applies the optimized AI prediction model and maintenance plan to actual production, and continuously collects data and feedback information. Through continuous iteration and optimization of maintenance strategies, the company has formed a set of equipment maintenance management system with strong adaptability and significant effects. This system not only improves the operating efficiency and stability of the production line, but also reduces maintenance costs and downtime, bringing significant economic benefits to the company.

[0065] By integrating advanced data acquisition, processing, analysis and automated maintenance strategies, comprehensive monitoring and efficient management of the operating status of the control cabinet can be achieved. This method first deploys multiple types of sensors inside the control cabinet. These sensors can collect the operating data of the equipment in real time and accurately, including but not limited to key parameters such as temperature, vibration, and current. The collected raw data is then preprocessed by cleaning, denoising, and standardization to eliminate noise interference, unify the data format, and ensure the data quality of subsequent analysis. After the data preprocessing is completed, the system will extract key features from these data, such as temperature change trends, vibration spectra, etc. These features can reflect the operating status and potential problems of the control cabinet. These features are used to construct the feature vector of the control cabinet operating status and store it in a special database to form a control cabinet operating status database. This database not only stores historical data and fault records, but also associates the corresponding feature vectors, providing rich data support for subsequent AI model training. Next, advanced algorithms such as deep learning are used to train the AI ​​prediction model with historical fault records as labels. During the model training process, the prediction accuracy and generalization ability of the model are continuously optimized through cross-validation, hyperparameter tuning and other means to ensure that the model can accurately identify abnormal patterns and potential faults of the control cabinet. The trained AI prediction model is applied to the intelligent inspection and anomaly detection links. The real-time collected data is input into the model. The model analyzes these data online and performs anomaly detection based on the learned feature patterns. Once the model identifies an abnormal pattern or predicts a potential fault, the system will immediately trigger an alarm and generate a detailed fault report to provide maintenance personnel with timely fault information and processing suggestions. Finally, in the maintenance and effect evaluation stage, the system performs maintenance work according to the predetermined preventive maintenance plan and records the data and results of the maintenance process. By evaluating the maintenance effect, collecting user feedback and equipment operation data, this information is used to further optimize the AI ​​prediction model and maintenance plan. This closed-loop continuous optimization mechanism ensures that the control cabinet intelligent inspection and preventive maintenance management method can continuously adapt to changes in equipment status, improve maintenance efficiency and accuracy, reduce failure rates, and ensure stable operation of equipment.

[0066] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0067] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control cabinet intelligent inspection and preventive maintenance management method, characterized in that: include S10, data collection and preprocessing: real-time collection of operating data inside the control cabinet, and preprocessing of the collected raw data; S20, feature extraction and database construction: extract key features from the preprocessed data, construct a feature vector of the control cabinet operation status, establish a control cabinet operation status database, and store historical data, fault records and corresponding feature vectors; S30, AI prediction model training: Use deep learning algorithms to train AI prediction models with historical fault records as labels; S40, Intelligent Inspection and Anomaly Detection: Input the real-time collected data into the trained AI prediction model to perform online inspection and anomaly detection. When the model identifies an abnormal pattern or predicts a potential failure, it triggers an alarm and generates a detailed fault report. S50, Maintenance and effect evaluation: Perform maintenance work according to the preventive maintenance plan, record the data and results of the maintenance process, evaluate the maintenance effect, and collect feedback information to optimize the AI ​​prediction model and maintenance plan.

2. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1 is characterized in that: S10 specifically includes the following steps: S101, Sensor deployment: Deploy multiple types of sensors at key locations inside the control cabinet, including temperature sensors, humidity sensors, vibration sensors, and current and voltage sensors; S102, data acquisition: reading data from the sensor periodically or continuously through the data acquisition system, and transmitting the data to the data processing unit; S103, data cleaning: preliminarily processing the collected raw data to remove duplicate data and erroneous data; S104, denoising: using one of low-pass filtering, high-pass filtering, and band-pass filtering to denoise the sensor signal; S105. Data standardization: use linear standardization or Z-score standardization to standardize the data; S106, data storage: storing the cleaned, denoised and standardized data in a database.

3. The control cabinet intelligent inspection and preventive maintenance management method according to claim 2 is characterized in that: Temperature sensors are installed in areas inside the control cabinet where heat is easily accumulated. Humidity sensors are usually installed at the top or bottom of the control cabinet, near the vents. Vibration sensors are installed on the supporting structure or fastening points of key equipment in the control cabinet. Current and voltage sensors are installed on the power input terminal, main load lines and key circuit nodes in the control cabinet.

4. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1 is characterized in that: The specific operation steps in S20 include: S201, define feature set: define a set of key features according to the operating characteristics and monitoring requirements of the control cabinet to fully reflect the operating status and potential faults of the control cabinet; S202, data segmentation: segmenting the preprocessed data according to time or event to form data segments; S203, feature calculation: for each data segment, calculate the defined feature value; S204, constructing a feature vector: combining the calculated feature values ​​into a feature vector for representing the operating state of the control cabinet in this data segment; S205, designing database structure: designing the structure of the database according to the feature extraction results and storage requirements, including determining the name, field, data type and index of the data table; S206, storing historical data: storing the pre-processed historical data in the database according to the designed database structure.

5. The control cabinet intelligent inspection and preventive maintenance management method according to claim 4 is characterized in that: In S206, for known fault events, the occurrence time, fault type, fault manifestation and corresponding feature vector information are recorded, and the association relationship between historical data, fault records and feature vectors is established in the database by setting foreign keys, indexes or creating views.

6. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1 is characterized in that: S30 specifically includes the following operation steps: S301, data preparation and preprocessing: divide the preprocessed data into a training set, a validation set, and a test set, and use historical fault records to assign corresponding fault categories or status labels to the training set data; S302, model selection and initialization: According to the data characteristics and prediction task requirements, the parameters are randomly initialized through the deep learning model to make the starting point of the model training consistent; S303, model training and optimization: Use training set data to train the model, adjust model parameters through back propagation algorithm and optimizer, use cross-validation method to evaluate model performance, and adjust model parameters accordingly to improve generalization ability, and find the optimal hyperparameter combination through grid search, random search or Bayesian optimization method to optimize model performance; S304, model evaluation and adjustment: Use the test set data to evaluate the prediction accuracy and generalization ability of the model, calculate the accuracy, precision, recall and F1 score indicators, and optimize the model performance based on the evaluation results; S305, model deployment and application: Deploy the trained and optimized model to the actual application scenario, and use the deployed model to monitor the operating status of the control cabinet in real time, predict potential faults, and provide intelligent support for operation and maintenance.

7. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1 is characterized in that: S40 specifically includes the following operation steps: S401, model input and online prediction: The preprocessed real-time data is passed as input to the trained AI prediction model. The model performs online prediction based on the input data, analyzes abnormal patterns in the data, or predicts potential failures. S402, anomaly detection and judgment: the model identifies the abnormal pattern in the data, and further judges the prediction result of the model in combination with the preset threshold or confidence level to determine whether there is actually an abnormality or fault; S403, alarm triggering and notification: Once an abnormality is confirmed or a potential failure is predicted, the alarm mechanism is triggered; S404, fault report generation: collect all information related to the abnormality or fault, including the occurrence time, abnormal parameters, predicted results and possible causes, and automatically generate a detailed fault report based on the collected information.

8. The control cabinet intelligent inspection and preventive maintenance management method according to claim 7 is characterized in that: Also includes: S405, follow-up processing and feedback: The operation and maintenance personnel conduct troubleshooting and diagnosis according to the fault report, determine the specific cause, take corresponding processing measures according to the diagnosis results, repair the fault or adjust the equipment parameters to restore normal operation, record the processing results and feedback information in the system, and update the operation status database of the control cabinet at the same time; S406. Continuous optimization and improvement: Regularly evaluate the performance of the AI ​​prediction model, and continuously optimize and improve the model based on the evaluation results and new data feedback to improve its prediction accuracy and generalization ability.

9. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1, 6 or 7, characterized in that: After the S40 intelligent inspection and anomaly detection, the process also includes formulating a preventive maintenance plan based on the fault report and the prediction results of the AI ​​model, combined with the maintenance history of the control cabinet and the equipment manual. The maintenance plan includes maintenance time, maintenance content, required spare parts and personnel arrangements.

10. The control cabinet intelligent inspection and preventive maintenance management method according to claim 1, characterized in that: S50 specifically includes the following steps: S501, equipment monitoring data: continuously collect monitoring data of the equipment during operation, including operating status and parameter changes; S502, User Feedback: Communicate with users to understand their satisfaction with the equipment maintenance effect and whether there are other problems or improvement needs during the operation of the equipment; S503, AI model optimization: Based on the collected maintenance data, equipment monitoring data and user feedback, the AI ​​prediction model is trained and optimized to improve its prediction accuracy and timeliness of equipment failures; S504, Maintenance plan adjustment: adjust and optimize the preventive maintenance plan according to the effect evaluation results and feedback information; S505. Continuous improvement: Apply the optimized AI prediction model and maintenance plan to actual production, and continue to collect data and feedback information to form a closed-loop continuous optimization mechanism.

Images