Electrolytic rectification system inspection method

Abstract

The invention discloses an inspection method for an electrolytic rectification system. According to the invention, real-time data monitoring and analysis provide early fault early warning, data-driven decision support, improvement of system reliability and optimization of a maintenance strategy for the electrolytic rectification system. Through real-time monitoring, the system can quickly capture an abnormal operation state, timely give out early warning and prevent fault expansion, so that the downtime and the maintenance cost are reduced. Meanwhile, due to the application of the big data analysis technology, an operator can make a more accurate decision based on detailed data, and the stability and reliability of the system are further improved. In addition, accumulation and analysis of real-time data provide a scientific basis for formulating a preventive maintenance strategy, and efficient utilization of maintenance resources is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of electrolytic rectification inspection, and in particular relates to an electrolytic rectification system inspection method. Background Art

[0002] With the rapid development of modern industry, the electrolytic rectifier system is the core equipment in the electrolytic industry, and its stability and reliability are crucial to the production process. However, due to the complex structure and harsh operating environment of the electrolytic rectifier system, frequent failures have become a bottleneck restricting its efficient operation. Traditional inspection methods mainly rely on manual regular inspections, which have problems such as low efficiency, poor accuracy, and slow response, and are difficult to meet the needs of modern industry for efficient and stable operation of equipment. In recent years, although some automated monitoring technologies have been introduced into the inspection of electrolytic rectifier systems.

[0003] However, traditional inspection methods still remain at the level of simple data collection and alarm, lacking in-depth analysis of data and accurate diagnosis of faults. In addition, existing monitoring systems often process data from each monitoring point in isolation, failing to fully utilize the correlation between data, resulting in low accuracy of fault diagnosis and difficulty in achieving predictive maintenance.

[0004] Therefore, there is an urgent need for an intelligent inspection method that can monitor the operating status of the electrolytic rectifier system in real time, accurately diagnose faults, and provide predictive maintenance recommendations. Summary of the invention

[0005] The purpose of the present invention is to provide an inspection method for an electrolytic rectifier system in order to solve the above-mentioned problem.

[0006] The technical solution adopted by the present invention is as follows: a method for inspecting an electrolytic rectifier system, the method comprising the following steps: S1: Deploy high-precision intelligent sensors at key locations of the electrolysis and rectification system, including temperature sensors, current sensors, voltage sensors, and gas sensors, to collect system operation data in real time; S2: preprocessing the collected data, including filtering, denoising and standardization, to ensure data quality; transmitting the preprocessed data to the central processing unit via a wireless network; S3: Use the central processing unit to detect real-time data and set a threshold alarm system. Once the data exceeds the normal range, the alarm will be triggered immediately; use big data analysis technology to conduct in-depth analysis of real-time data and identify potential failure modes; S4: Combine machine learning and deep learning algorithms to perform fault diagnosis on the analyzed data and accurately identify the fault type and location; continuously train and optimize the diagnostic model to improve the diagnostic accuracy; S5: When the system detects a complex or difficult-to-diagnose fault, it automatically connects to a remote expert system; the expert provides professional diagnosis and solutions by remotely accessing real-time data and historical records; S6: deploy an automated inspection robot to perform physical inspections on the electrolytic rectifier system along a preset path; the robot is equipped with a camera and infrared thermal imaging equipment to detect equipment appearance and temperature anomalies; S7: Multi-dimensionally integrate smart sensor data, AI diagnosis results, and robot inspection data; generate comprehensive inspection reports based on integrated data to provide support for decision-making; S8: Make preventive maintenance recommendations based on inspection results and data analysis; formulate maintenance plans to reduce unexpected downtime and extend equipment life; S9: Establish a feedback mechanism to collect problems and suggestions during the inspection process; continuously optimize the inspection methods to improve system reliability and inspection efficiency.

[0007] In a preferred embodiment, in step S1, before the electrolytic rectifier system is started, the system is first initialized and self-checked; this step includes power supply check of hardware devices, connectivity test of communication lines, functional verification of sensors and actuators, and version verification and configuration loading of software systems; system initialization ensures that all components are in a ready state, and the self-check process automatically runs through a preset test program to detect whether each component is working normally, and promptly discovers and reports any potential problems.

[0008] In a preferred embodiment, in step S2, the intelligent sensor collects key operating data with high precision and high speed, including current, voltage, temperature, and humidity parameters; these data are transmitted to the central processing unit in real time via a wireless or wired network; during the transmission process, data encryption and verification technology are used to ensure the security and integrity of the data.

[0009] In a preferred embodiment, in step S3, after receiving the data, the central processing unit first performs data format verification and preliminary screening to ensure the accuracy and integrity of the data; The threshold alarm system sets the thresholds of each monitoring indicator according to the normal operating range and historical data of the electrolytic rectifier system; the current threshold is set to I_min to I_max, the voltage threshold is set to V_min to V_max, and the temperature threshold is set to T_min to T_max; the central processing unit compares the real-time monitoring data with the preset thresholds; if the data exceeds the threshold range, the alarm mechanism is immediately triggered to notify the operation and maintenance personnel; Use the machine learning-based isolation forest anomaly detection algorithm to conduct deep data analysis to identify potential failure modes; Model training: Use historical normal data to train the isolation forest model; model parameters include: n_estimators: the number of isolated trees, which affects the complexity of the model and the detection effect; max_samples: The number of samples used by each tree, set as a fraction of the total number of samples; Contamination: An estimate of the proportion of outliers in the data, used to adjust the sensitivity of the model; Real-time detection: Input the real-time normalized data into the trained isolation forest model and calculate the anomaly score of each data point; The anomaly score calculation formula is: anomaly_score=2^-(E(h(x)) / c(n)) Anomaly judgment: Determine whether it is an anomaly point based on the anomaly score and the preset anomaly threshold; the higher the anomaly score, the more likely the data point is an anomaly; where E(h(x)) is the average path length of the data point x in the isolation forest, c(n) is the expected value of the path length, and n is the number of samples.

[0010] In a preferred embodiment, in step S4, a convolutional neural network (CNN) is selected as a fault diagnosis algorithm, and the specific method includes: S41: Model construction: Input layer: input preprocessed feature data; Convolutional layer: Use multiple convolution kernels to extract local features in the data; Pooling layer: reduces the dimension of the features output by the convolutional layer to reduce the amount of calculation; Fully connected layer: converts the feature vector output by the pooling layer into the probability distribution of the fault type; Output layer: outputs the prediction results of fault type; S42: Model training: Loss function: The cross entropy loss function is used to measure the difference between the model prediction result and the true label; Optimization algorithm: Use the Adam optimization algorithm to update model parameters and speed up convergence; Batch training: Divide the data into multiple batches for training, each batch contains a certain number of samples; S3: Model evaluation and optimization: Evaluation indicators: Use accuracy, recall rate, and F1 score indicators to evaluate model performance; Model optimization: Adjust the model structure, parameters, or training strategy based on the evaluation results to improve diagnostic accuracy; Among them: The calculation formula of the cross entropy loss function is: L=-Σ(y_log(p)+(1-y)_log(1-p)); Among them, y is the true label (0 or 1), and p is the probability predicted by the model; The calculation formula of the Adam optimization algorithm is: m_t=β1*m_(t-1)+(1-β1)*g_t v_t=β2*v_(t-1)+(1-β2)*g_t^2 m_t_hat=m_t / (1-β1^t) v_t_hat=v_t / (1-β2^t) θ_t=θ_(t-1)-α*m_t_hat / (sqrt(v_t_hat)+ε); Among them, m_t and v_t are estimates of the first-order moment and the second-order moment respectively, β1 and β2 are decay rates, g_t is the gradient, α is the learning rate, ε is a small constant, and θ_t is the updated parameter; The convolution layer parameters include: convolution kernel size: used to extract local features; step size: represents the step size of the convolution kernel moving on the data; padding: used to control the size of the output feature map.

[0011] In a preferred embodiment, in step S5, the system not only issues an immediate warning to on-site operators by sound, light or text message, but also integrates multiple communication channels, such as e-mail and mobile application push, to ensure that the warning information can be quickly and accurately conveyed to all relevant responsible persons; the early warning system adopts a graded alarm mechanism, and divides the alarm into different levels, such as general warning, important warning and emergency warning, according to the severity and urgency of the fault, so that relevant personnel can take corresponding response measures according to the alarm level.

[0012] In a preferred embodiment, in step S6, during the fault analysis and location stage, the system uses advanced data analysis techniques and machine learning algorithms to conduct in-depth mining and analysis of the massive amounts of data collected; first, the system improves data quality through data preprocessing techniques; then, feature extraction algorithms, such as principal component analysis or autoencoders, are applied to extract key features from complex data; then, classification algorithms are used to classify and identify faults; and finally, association analysis and techniques, such as causal analysis or fault tree analysis, are used to determine the specific location and cause of the fault.

[0013] In a preferred embodiment, in step S7, not only a detailed maintenance guidance plan is provided, but also animation demonstrations, video tutorials and multimedia materials are included to help maintenance personnel better understand and perform maintenance operations; the maintenance guidance plan is automatically generated according to the fault type and equipment model, including detailed step-by-step instructions, a list of required tools and materials, safe operating procedures and possible alternatives; the system supports access to mobile devices, and maintenance personnel can view maintenance instructions on their mobile phones or tablets anytime and anywhere.

[0014] In a preferred embodiment, in step S8, during the system recovery and verification phase, the system adopts a gradual recovery strategy, first testing key equipment individually to ensure that it functions normally, and then gradually recovering the operation of the entire system; during the recovery process, the system monitors various parameters in real time, including current, voltage, and temperature, to ensure stable operation of the system; the verification process includes performance testing, load testing, and stability testing to comprehensively check whether the system meets the preset performance indicators; the system also supports automatic generation of recovery reports, which record the recovery process, test results, and verification data in detail; in addition, the system also performs a comprehensive self-check, including hardware self-check and software self-check, to ensure that all components are working normally and there are no legacy issues; system recovery and verification ensure that the electrolytic rectifier system can be put back into operation safely and efficiently after maintenance, and reduce the risk of failure again.

[0015] In a preferred embodiment, in step S9, in the report generation and archiving step, the system automatically summarizes all data and analysis results in the inspection process to generate a detailed inspection report; the report content includes inspection time, inspection personnel, detection data, fault records, maintenance process, recovery verification results and improvement suggestions; the report adopts a structured format for easy reading and analysis; the system supports multiple report formats, including PDF and Excel, to facilitate the needs of different users; after the report is generated, the system automatically archives the report to the central database, and classifies and indexes it according to time, equipment, and fault type to facilitate subsequent query and tracing.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In the present invention, real-time data monitoring and analysis provide early fault warning, data-driven decision support, improved system reliability, and optimized maintenance strategies for the electrolytic rectifier system. Through real-time monitoring, the system can quickly capture abnormal operating conditions, issue early warnings in a timely manner, and prevent faults from expanding, thereby reducing downtime and maintenance costs. At the same time, the application of big data analysis technology enables operators to make more accurate decisions based on detailed data, further improving the stability and reliability of the system. In addition, the accumulation and analysis of real-time data also provides a scientific basis for the formulation of preventive maintenance strategies, realizing the efficient use of maintenance resources.

[0017] 2. In the present invention, through accurate fault location, improved diagnostic accuracy, realization of intelligent maintenance and promotion of predictive maintenance, a solid guarantee is provided for the efficient operation and long-term stability of the system. The application of machine learning and deep learning algorithms makes fault diagnosis more intelligent and accurate, reduces manual intervention and errors, and improves maintenance efficiency. At the same time, the continuous training and optimization of the model not only improves the diagnostic accuracy, but also accumulates valuable knowledge and experience, providing support for subsequent maintenance and management. The implementation of predictive maintenance brings maintenance work forward before the failure occurs, further reducing maintenance costs and avoiding production interruptions. In summary, S3 and S4 together constitute an efficient and intelligent fault monitoring and diagnosis system, which significantly reduces the risk and cost of failure, and improves production efficiency and corporate competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Reference Figure 1 Example: A method for inspecting an electrolytic rectifier system, the method comprising the following steps: S1: Deploy high-precision intelligent sensors at key locations of the electrolysis rectifier system, including temperature sensors, current sensors, voltage sensors, and gas sensors, to collect system operation data in real time; S2: Preprocess the collected data, including filtering, denoising and standardization, to ensure data quality. The preprocessed data is transmitted to the central processing unit via a wireless network; S3: Use the central processing unit to detect real-time data and set up a threshold alarm system. Once the data exceeds the normal range, the alarm will be triggered immediately. Use big data analysis technology to conduct in-depth analysis of real-time data and identify potential failure modes; S4: Combine machine learning and deep learning algorithms to perform fault diagnosis on the analyzed data and accurately identify the fault type and location. Continuously train and optimize the diagnostic model to improve the diagnostic accuracy; S5: When the system detects a complex or difficult-to-diagnose fault, it automatically connects to the remote expert system. Experts can provide professional diagnosis and solutions by remotely accessing real-time data and historical records; S6: Deploy an automated inspection robot to conduct physical inspections of the electrolytic rectifier system along a preset path. The robot is equipped with cameras, infrared thermal imagers and other equipment to detect equipment appearance and temperature anomalies; S7: Multi-dimensional fusion of intelligent sensor data, artificial intelligence diagnosis results and robot inspection data. Generate a comprehensive inspection report based on the fused data to provide support for decision-making; S8: Make preventive maintenance recommendations based on inspection results and data analysis. Develop maintenance plans to reduce unexpected downtime and extend equipment life; S9: Establish a feedback mechanism to collect problems and suggestions during the inspection process. Continuously optimize the inspection method to improve system reliability and inspection efficiency.

[0021] In step S1, before the electrolytic rectifier system is started, the system is first initialized and self-checked. This step includes power supply check of hardware devices, connectivity test of communication lines, functional verification of sensors and actuators, and version verification and configuration loading of software systems. System initialization ensures that all components are in a ready state, while the self-check process automatically runs through a preset test program to detect whether each component is working properly, and promptly discovers and reports any potential problems, such as abnormal power supply voltage, communication failure or sensor reading error, so as to ensure that the system starts in a safe and stable state.

[0022] In step S2, the intelligent sensor collects key operating data with high accuracy and high speed, including parameters such as current, voltage, temperature, humidity, etc. These data are transmitted to the central processing unit in real time via wireless or wired networks. During the transmission process, data encryption and verification technology are used to ensure the security and integrity of the data. In addition, in order to adapt to the data collection needs in different environments, the system supports a variety of communication protocols and interfaces, such as MODBUS, TCP / IP, etc., to achieve flexible and reliable data collection and transmission.

[0023] In step S3, after receiving the data, the central processing unit first performs data format verification and preliminary screening to ensure the accuracy and completeness of the data; The threshold alarm system sets the thresholds of each monitoring indicator according to the normal operating range and historical data of the electrolytic rectifier system. The current threshold is set to I_min to I_max, the voltage threshold is set to V_min to V_max, and the temperature threshold is set to T_min to T_max. The central processing unit compares the real-time monitoring data with the preset thresholds. If the data exceeds the threshold range, the alarm mechanism is immediately triggered to notify the operation and maintenance personnel; Use the machine learning-based isolation forest anomaly detection algorithm to perform deep data analysis to identify potential failure modes.

[0024] Model training: Use historical normal data to train the isolation forest model. Model parameters include: n_estimators: The number of isolated trees, which affects the complexity of the model and the detection effect.

[0025] max_samples: The number of samples used by each tree, usually set as a fraction of the total number of samples.

[0026] Contamination: An estimate of the proportion of outliers in the data, used to adjust the sensitivity of the model.

[0027] Real-time detection: Input the real-time normalized data into the trained isolation forest model and calculate the anomaly score of each data point.

[0028] The anomaly score calculation formula is: anomaly_score=2^-(E(h(x)) / c(n)) Anomaly judgment: Determine whether it is an anomaly point based on the anomaly score and the preset anomaly threshold. The higher the anomaly score, the more likely the data point is to be an anomaly; where E(h(x)) is the average path length of the data point x in the isolation forest, c(n) is the expected value of the path length, and n is the number of samples.

[0029] In step S4, a convolutional neural network (CNN) is selected as a fault diagnosis algorithm, and the specific method includes: S41: Model construction: Input layer: input preprocessed feature data.

[0030] Convolutional layer: Use multiple convolution kernels to extract local features in the data.

[0031] Pooling layer: Reduce the dimension of the features output by the convolutional layer to reduce the amount of calculation.

[0032] Fully connected layer: Converts the feature vector output by the pooling layer into a probability distribution of fault types.

[0033] Output layer: outputs the prediction results of fault type.

[0034] S42: Model training: Loss function: The cross entropy loss function is used to measure the difference between the model prediction results and the true labels.

[0035] Optimization algorithm: Use the Adam optimization algorithm to update model parameters and speed up convergence.

[0036] Batch training: Divide the data into multiple batches for training, each batch contains a certain number of samples.

[0037] S3: Model evaluation and optimization: Evaluation indicators: Use accuracy, recall, F1 score and other indicators to evaluate model performance. Model optimization: Adjust the model structure, parameters or training strategy according to the evaluation results to improve the diagnostic accuracy.

[0038] Among them: The calculation formula of the cross entropy loss function is: L=-Σ(y_log(p)+(1-y)_log(1-p)); Among them, y is the true label (0 or 1), and p is the probability predicted by the model.

[0039] The calculation formula of the Adam optimization algorithm is: m_t=β1*m_(t-1)+(1-β1)*g_t v_t=β2*v_(t-1)+(1-β2)*g_t^2 m_t_hat=m_t / (1-β1^t) v_t_hat=v_t / (1-β2^t) θ_t=θ_(t-1)-α*m_t_hat / (sqrt(v_t_hat)+ε); Among them, m_t and v_t are estimates of the first-order moment and the second-order moment respectively, β1 and β2 are decay rates, g_t is the gradient, α is the learning rate, ε is a small constant, and θ_t is the updated parameter; Convolution layer parameters include: Convolution kernel size: used to extract local features. Step size: represents the step size of the convolution kernel moving on the data. Padding: used to control the size of the output feature map.

[0040] In step S5, the system not only issues an immediate warning to the on-site operators through sound, light or text message, but also integrates multiple communication channels, such as email, mobile application push, etc., to ensure that the warning information can be quickly and accurately conveyed to all relevant responsible persons. The early warning system adopts a hierarchical alarm mechanism, which divides the alarm into different levels according to the severity and urgency of the fault, such as general warning, important warning and emergency warning, so that relevant personnel can take corresponding countermeasures according to the alarm level. In addition, the system also supports custom alarm rules, allowing users to set specific alarm conditions and notification methods according to actual needs, thereby improving the flexibility and pertinence of the alarm. The early warning information contains a detailed description of the fault, recommended emergency measures and possible consequence predictions to help operators make judgments and responses quickly.

[0041] In step S6, during the fault analysis and location phase, the system uses advanced data analysis techniques and machine learning algorithms to conduct in-depth mining and analysis of the massive amounts of data collected. First, the system improves data quality through data preprocessing techniques such as filtering, denoising, and normalization. Then, feature extraction algorithms such as principal component analysis (PCA) or autoencoders are applied to extract key features from complex data. Next, classification algorithms such as support vector machines (SVM) or random forests are used to classify and identify faults. Finally, association analysis and techniques such as cause-and-effect analysis or fault tree analysis are used to determine the specific location and cause of the fault. The system also supports real-time data visualization, which intuitively displays the results of fault analysis and location through charts and dashboards, making it easier for technicians to quickly understand and process them.

[0042] In step S7, in the maintenance guidance and execution step, the system not only provides detailed maintenance guidance plans, but also multimedia materials such as animation demonstrations and video tutorials to help maintenance personnel better understand and perform maintenance operations. The maintenance guidance plan is automatically generated according to the fault type and equipment model, including detailed step-by-step instructions, a list of required tools and materials, safe operating procedures, and possible alternatives. The system supports mobile device access, and maintenance personnel can view maintenance instructions on their mobile phones or tablets anytime, anywhere. In addition, the system also provides remote expert support services. Through video conferencing or real-time chat, experts can remotely guide maintenance personnel to solve complex problems. After the maintenance is completed, the system automatically records the maintenance process and results, including maintenance time, materials used, maintenance results, etc., to provide detailed data support for subsequent maintenance and improvements.

[0043] In step S8, during the system recovery and verification phase, the system adopts a step-by-step recovery strategy, first testing key equipment individually to ensure that it functions normally, and then gradually recovering the operation of the entire system. During the recovery process, the system monitors various parameters in real time, such as current, voltage, temperature, etc., to ensure stable operation of the system. The verification process includes performance testing, load testing, and stability testing to comprehensively check whether the system meets the preset performance indicators. The system also supports automatic generation of recovery reports, which record the recovery process, test results, and verification data in detail. In addition, the system also performs a comprehensive self-test, including hardware self-test and software self-test, to ensure that all components are working properly and there are no legacy issues. System recovery and verification ensure that the electrolytic rectifier system can be safely and efficiently put back into operation after maintenance, and reduce the risk of re-failure.

[0044] In step S9, during the report generation and archiving step, the system automatically aggregates all data and analysis results during the inspection process to generate a detailed inspection report. The report content includes inspection time, inspection personnel, detection data, fault records, repair process, restoration verification results, and improvement suggestions, etc. The report adopts a structured format for easy reading and analysis. The system supports multiple report formats such as PDF, Excel, etc., to meet the needs of different users. After the report is generated, the system automatically archives the report into the central database and classifies and indexes it according to time, equipment, fault type, etc., for subsequent query and traceability. In addition, the system also provides an audit and approval process for the report to ensure the accuracy and integrity of the report. Report generation and archiving not only provide an important basis for the maintenance and management of the electrolytic rectification system, but also provide data support for subsequent improvement and optimization, promoting knowledge accumulation and experience inheritance. In the present invention, real-time data monitoring and analysis provide early fault warning, data-driven decision support, improvement of system reliability, and optimization of maintenance strategies for the electrolytic rectification system. Through real-time monitoring, the system can quickly capture abnormal operating states, issue early warnings in a timely manner, prevent the expansion of faults, thereby reducing downtime and maintenance costs. At the same time, the application of big data analysis technology enables operators to make more accurate decisions based on detailed data, further improving the stability and reliability of the system. In addition, the accumulation and analysis of real-time data also provide a scientific basis for formulating preventive maintenance strategies, realizing the efficient utilization of maintenance resources.

[0045] In the present invention, through accurate fault location, improvement of diagnostic accuracy, realization of intelligent maintenance, and promotion of predictive maintenance, it provides a solid guarantee for the efficient operation and long-term stability of the system. The application of machine learning and deep learning algorithms makes fault diagnosis more intelligent and accurate, reduces manual intervention and errors, and improves the repair efficiency. At the same time, the continuous training and optimization of the model not only improve the diagnostic accuracy, but also accumulate valuable knowledge and experience, providing support for subsequent maintenance and management. The realization of predictive maintenance advances the repair work even before the occurrence of faults, further reducing maintenance costs and avoiding production interruptions. In summary, S3 and S4 together constitute an efficient and intelligent fault monitoring and diagnosis system, significantly reducing the fault risk and cost, and improving production efficiency and enterprise competitiveness.

Claims

1. A method for inspecting an electrolytic rectifier system, characterized in that: The method comprises the following steps: S1: Deploy high-precision intelligent sensors at key locations of the electrolysis and rectification system, including temperature sensors, current sensors, voltage sensors, and gas sensors, to collect system operation data in real time; S2: preprocessing the collected data, including filtering, denoising and standardization, to ensure data quality; transmitting the preprocessed data to the central processing unit via a wireless network; S3: Use the central processing unit to detect real-time data and set a threshold alarm system. Once the data exceeds the normal range, the alarm will be triggered immediately; use big data analysis technology to conduct in-depth analysis of real-time data and identify potential failure modes; S4: Combine machine learning and deep learning algorithms to perform fault diagnosis on the analyzed data and accurately identify the fault type and location; continuously train and optimize the diagnostic model to improve the diagnostic accuracy; S5: When the system detects a complex or difficult-to-diagnose fault, it automatically connects to a remote expert system; the expert provides professional diagnosis and solutions by remotely accessing real-time data and historical records; S6: deploy an automated inspection robot to perform physical inspections on the electrolytic rectifier system along a preset path; the robot is equipped with a camera and infrared thermal imaging equipment to detect equipment appearance and temperature anomalies; S7: Multi-dimensionally integrate smart sensor data, AI diagnosis results, and robot inspection data; generate comprehensive inspection reports based on integrated data to provide support for decision-making; S8: Make preventive maintenance recommendations based on inspection results and data analysis; formulate maintenance plans to reduce unexpected downtime and extend equipment life; S9: Establish a feedback mechanism to collect problems and suggestions during the inspection process; continuously optimize the inspection methods to improve system reliability and inspection efficiency.

2. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S1, before the electrolytic rectifier system is started, the system is first initialized and self-checked; this step includes power supply check of hardware devices, connectivity test of communication lines, function verification of sensors and actuators, and version verification and configuration loading of software systems; system initialization ensures that all components are in a ready state, while the self-check process automatically runs through a preset test program to detect whether each component is working normally, and promptly discovers and reports any potential problems.

3. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S2, the intelligent sensor collects key operating data with high accuracy and high speed, including current, voltage, temperature, and humidity parameters; these data are transmitted to the central processing unit in real time via a wireless or wired network; During the transmission process, data encryption and verification technology are used to ensure the security and integrity of data.

4. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S3, after receiving the data, the central processing unit first performs data format verification and preliminary screening to ensure the accuracy and integrity of the data; The threshold alarm system sets the threshold of each monitoring indicator according to the normal operating range and historical data of the electrolytic rectifier system; The current threshold is set to I_min to I_max, the voltage threshold is set to V_min to V_max, and the temperature threshold is set to T_min to T_max; the central processing unit compares the real-time monitoring data with the preset thresholds; if the data exceeds the threshold range, the alarm mechanism is immediately triggered to notify the operation and maintenance personnel; Use the machine learning-based isolation forest anomaly detection algorithm to conduct deep data analysis to identify potential failure modes; Model training: Use historical normal data to train the isolation forest model; model parameters include: n_estimators: the number of isolated trees, which affects the complexity of the model and the detection effect; max_samples: The number of samples used by each tree, set as a fraction of the total number of samples; Contamination: An estimate of the proportion of outliers in the data, used to adjust the sensitivity of the model; Real-time detection: Input the real-time normalized data into the trained isolation forest model and calculate the anomaly score of each data point; The anomaly score calculation formula is: anomaly_score=2^-(E(h(x)) / c(n)) Anomaly judgment: Determine whether it is an anomaly point based on the anomaly score and the preset anomaly threshold; the higher the anomaly score, the more likely the data point is an anomaly; where E(h(x)) is the average path length of the data point x in the isolation forest, c(n) is the expected value of the path length, and n is the number of samples.

5. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S4, a convolutional neural network (CNN) is selected as a fault diagnosis algorithm, and the specific method includes: S41: Model construction: Input layer: input preprocessed feature data; Convolutional layer: Use multiple convolution kernels to extract local features in the data; Pooling layer: reduces the dimension of the features output by the convolutional layer to reduce the amount of calculation; Fully connected layer: converts the feature vector output by the pooling layer into the probability distribution of the fault type; Output layer: outputs the prediction results of fault type; S42: Model training: Loss function: The cross entropy loss function is used to measure the difference between the model prediction result and the true label; Optimization algorithm: Use the Adam optimization algorithm to update model parameters and speed up convergence; Batch training: Divide the data into multiple batches for training, each batch contains a certain number of samples; S43: Model evaluation and optimization: Evaluation indicators: Use accuracy, recall, and F1 score indicators to evaluate model performance; Model optimization: Adjust the model structure, parameters, or training strategy based on the evaluation results to improve diagnostic accuracy; Among them: The calculation formula of the cross entropy loss function is: L=-Σ(y_log(p)+(1-y)_log(1-p)); Among them, y is the true label (0 or 1), and p is the probability predicted by the model; The calculation formula of the Adam optimization algorithm is: m_t=β1*m_(t-1)+(1-β1)*g_t v_t=β2*v_(t-1)+(1-β2)*g_t^2 m_t_hat=m_t / (1-β1^t) v_t_hat=v_t / (1-β2^t) θ_t=θ_(t-1)-α*m_t_hat / (sqrt(v_t_hat)+ε); Among them, m_t and v_t are estimates of the first-order moment and the second-order moment respectively, β1 and β2 are decay rates, g_t is the gradient, α is the learning rate, ε is a small constant, and θ_t is the updated parameter; The convolution layer parameters include: convolution kernel size: used to extract local features; step size: represents the step size of the convolution kernel moving on the data; padding: used to control the size of the output feature map.

6. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S5, the system not only issues an immediate warning to the on-site operators by sound, light or SMS, but also integrates multiple communication channels, such as email and mobile application push, to ensure that the warning information can be quickly and accurately conveyed to all relevant responsible persons; The early warning system adopts a graded alarm mechanism, which divides the alarm into different levels, such as general warning, important warning and emergency warning, according to the severity and urgency of the fault, so that relevant personnel can take corresponding response measures according to the alarm level.

7. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S6, during the fault analysis and location phase, the system uses advanced data analysis techniques and machine learning algorithms to conduct in-depth mining and analysis of the massive amounts of data collected. First, the system improves data quality through data preprocessing techniques. Then, feature extraction algorithms, such as principal component analysis or autoencoders, are applied to extract key features from complex data. Next, classification algorithms are used to classify and identify faults. Finally, association analysis and techniques, such as cause-effect analysis or fault tree analysis, are used to determine the specific location and cause of the fault.

8. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S7, not only a detailed maintenance guidance plan is provided, but also animation demonstrations, video tutorials and multimedia materials are included to help maintenance personnel better understand and perform maintenance operations; the maintenance guidance plan is automatically generated according to the fault type and equipment model, including detailed step-by-step instructions, a list of required tools and materials, safe operating procedures and possible alternatives; the system supports access from mobile devices, and maintenance personnel can view maintenance instructions on their mobile phones or tablets anytime and anywhere.

9. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In the step S8, during the system recovery and verification phase, the system adopts a step-by-step recovery strategy, first testing key equipment individually to ensure that it functions normally, and then gradually recovering the operation of the entire system; During the recovery process, the system monitors various parameters in real time, including current, voltage, and temperature, to ensure stable operation of the system; the verification process includes performance testing, load testing, and stability testing to comprehensively check whether the system meets the preset performance indicators; the system also supports automatic generation of recovery reports, which record the recovery process, test results, and verification data in detail; in addition, the system also performs a comprehensive self-check, including hardware self-check and software self-check, to ensure that all components are working properly and there are no remaining issues; system recovery and verification ensure that the electrolytic rectifier system can be put back into operation safely and efficiently after maintenance, and reduce the risk of recurrence of failures.

10. The electrolytic rectifier system inspection method according to claim 1, characterized in that: In step S9, in the report generation and archiving step, the system automatically summarizes all data and analysis results during the inspection process and generates a detailed inspection report; The report content includes inspection time, inspection personnel, detection data, fault records, maintenance process, recovery verification results and improvement suggestions; the report adopts a structured format for easy reading and analysis; the system supports multiple report formats, including PDF and Excel, to meet the needs of different users; after the report is generated, the system automatically archives the report to the central database and classifies and indexes it according to time, equipment, and fault type to facilitate subsequent query and tracing.

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