Equipment operation and maintenance data analysis and fault prediction method based on CHATGPT

Abstract

The invention discloses a CHATGPT-based equipment operation and maintenance data analysis and fault prediction method. The method comprises the following steps: acquiring data by using an equipment sensor; performing cleaning and feature extraction on the data to obtain natural language description; natural language description is obtained and analyzed, a fault mode is recognized, and an analysis result and a maintenance suggestion are obtained; and performing trend analysis based on the equipment historical data, the analysis result and the maintenance suggestion, and predicting and outputting a future fault. The ChatGPT-based equipment operation and maintenance technology has intelligent real-time fault detection and high interpretability, the system can convert complex multi-dimensional sensor data into text description easy to understand through natural language processing, maintenance suggestions are automatically generated, and the flexibility and adaptability of the system are improved. According to the method, the operation and maintenance efficiency is improved, the dependence on artificial experience is reduced, and the operation and maintenance personnel can quickly and accurately know the equipment state and take corresponding measures.

Description

Technical Field

[0001] The present invention relates to a device operation and maintenance data analysis and fault prediction method based on CHATGPT, and belongs to the technical field of device operation and maintenance data analysis and fault prediction. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, equipment operation and maintenance is gradually transforming from traditional regular maintenance, that is, preventive maintenance, to predictive maintenance and intelligent operation and maintenance mode. The importance of equipment operation and maintenance in many fields such as industry, manufacturing and energy industry is increasing day by day. The normal operation of equipment and the timely repair of faults are directly related to production efficiency and safety. However, the limitations of traditional operation and maintenance methods are gradually emerging, such as delayed fault detection, high operation and maintenance costs, and insufficient predictive maintenance. These problems have promoted the demand for intelligent and automated operation and maintenance solutions, especially the development of IoT technology, which monitors the operating status of equipment in real time through sensors and uploads data to the cloud or edge computing platform, forming the basis for equipment digitization and data visualization. At the same time, the maturity of big data analysis and machine learning technology has made it possible to predict equipment faults and assess health. Equipment operation and maintenance systems based on these technologies can achieve more intelligent monitoring and maintenance in a data-driven way and improve the level of equipment management.

[0003] Traditional rule-based and statistical equipment operation and maintenance systems usually rely on preset rules and simple statistical models. For example, a threshold-based monitoring system issues an alarm when a parameter exceeds a certain threshold. These systems can detect basic anomalies, but have limitations in identifying complex fault patterns and often cannot cope with complex relationships between multiple variables or parameter drift caused by the gradual aging of equipment. This traditional equipment operation and maintenance system has certain limitations. Rules and statistical models are only applicable to simple and stable fault modes, and it is difficult to adapt to new faults or the diverse operating environment of equipment. This method lacks real-time self-adaptation capabilities, usually requires manual adjustment of rules, and has high maintenance costs.

[0004] In recent years, machine learning-based fault detection and prediction systems have gradually been applied to equipment operation and maintenance. These systems are able to identify complex abnormal patterns in equipment data by training on historical equipment data. For example, algorithms such as support vector machines (SVM), random forests, and neural networks are widely used in predictive maintenance and anomaly detection. However, traditional machine learning models work well for specific fault types and equipment types, but have poor applicability and versatility, and usually need to be retrained for different equipment. In addition, machine learning models can usually only give abnormal values ​​or states, lack the ability to explain and generate natural language, and cannot provide detailed maintenance recommendations or generate natural language reports. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method for analyzing equipment operation and maintenance data and predicting faults based on ChatGPT. The equipment operation and maintenance technology based on ChatGPT has intelligent real-time fault detection and high interpretability. Through natural language processing, the system can convert complex multi-dimensional sensor data into easy-to-understand text descriptions and automatically generate maintenance suggestions. This method improves operation and maintenance efficiency, reduces dependence on manual experience, and enables operation and maintenance personnel to quickly and accurately understand the equipment status and take corresponding measures.

[0006] Preferably, the present invention provides a method for analyzing equipment operation and maintenance data and predicting faults based on CHATGPT, comprising: collecting data using equipment sensors; Clean and extract features from data to obtain natural language description; Capture and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; Perform trend analysis based on historical equipment data, analysis results, and maintenance recommendations to predict and output future failures.

[0007] Prioritize the delivery of analysis results, maintenance recommendations, and prediction outputs of future failures to operations and maintenance personnel.

[0008] Prioritize, obtain maintenance feedback information from operation and maintenance personnel to determine whether the analysis result is correct; if the analysis result is incorrect, replace the analysis result with the maintenance feedback information.

[0009] Prioritize data cleaning including denoising and normalization.

[0010] Preferably, the equipment historical data includes historical temperature data, historical pressure data and vibration data.

[0011] Prioritize the equipment operation and maintenance data analysis and fault prediction system based on CHATGPT, including: A data acquisition module, used to collect data using device sensors; Data preprocessing module, used to clean and extract features from data to obtain natural language description; Natural language generation module, used to obtain and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; The prediction and decision-making module is used to perform trend analysis based on historical equipment data, analysis results and maintenance recommendations, and predict and output future failures.

[0012] Prioritize, the alarm and reporting module is used to deliver the analysis results, maintenance suggestions and prediction output of future failures to the operation and maintenance personnel.

[0013] Preferably, the feedback and closed-loop optimization module is used to obtain maintenance feedback information from the operation and maintenance personnel to determine whether the analysis result is correct; if the analysis result is incorrect, the maintenance feedback information is used to replace the analysis result.

[0014] Preferably, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the program.

[0015] Preferably, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0016] The beneficial effects achieved by the present invention are: The equipment operation and maintenance technology based on ChatGPT in this invention has intelligent real-time fault detection and high interpretability. Through natural language processing, the system can convert complex multi-dimensional sensor data into easy-to-understand text descriptions and automatically generate maintenance suggestions, which improves the flexibility and adaptability of the system. This method improves operation and maintenance efficiency, reduces dependence on manual experience, and enables operation and maintenance personnel to quickly and accurately understand the equipment status and take corresponding measures.

[0017] In addition, the invention technology supports predictive maintenance. Through historical data collected by equipment sensors and real-time trend analysis, it can identify potential failure risks in advance, identify potential failures and provide maintenance suggestions to help prevent unplanned downtime, significantly improving the intelligence level of predictive maintenance, helping to prevent unplanned equipment downtime and extend equipment life. The feedback and closed-loop optimization module enables the system to learn adaptively, gradually optimize the model and fault library, thereby improving the accuracy of diagnosis and prediction, and meeting the needs of different equipment and environments. By introducing the feedback module, the system continuously optimizes the model and fault library according to operation and maintenance feedback, realizes adaptive learning, and enhances the system's adaptability to different environments.

[0018] When ChatGPT generates natural language descriptions, it may misunderstand or inaccurately judge the status of the device, resulting in incorrect maintenance recommendations. To prevent this from happening, this solution introduces a manual intervention mechanism, where operation and maintenance personnel can review and confirm the output of ChatGPT to ensure the accuracy of the output. The technology of this invention is efficient, flexible and scalable, and can be widely used in equipment operation and maintenance scenarios in different industries. Intelligent and automated operation and maintenance solutions not only improve the operating stability of the equipment, but also significantly reduce operation and maintenance costs, bringing significant economic benefits to enterprises; This solution is not only applicable to the single device described, but also to the system containing multiple devices, such as the operation and maintenance of production lines. In this case, this solution has more advantages because the system is more complex than the equipment, the mechanism model is more difficult to derive, and the amount of data is larger. Therefore, it is more suitable for the black box deep neural network learning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 is a principle block diagram of some embodiments of the present application; Figure 2 It is a flow chart of some embodiments of the present application. DETAILED DESCRIPTION

[0021] Traditional equipment operation and maintenance methods usually rely on fixed rules and empirical judgments, including regular maintenance and threshold-based fault detection. However, these methods have obvious limitations. It is difficult for traditional methods to monitor equipment status in real time, especially in multi-variable and complex industrial scenarios, where fixed rules often fail to capture potential fault signals. Traditional operation and maintenance methods require a lot of manpower to monitor and analyze data. At the same time, the regular maintenance of equipment is prone to waste of resources. Traditional operation and maintenance is mostly a "passive maintenance" mode, that is, the equipment is handled only after it fails, which makes it difficult to prevent equipment downtime in time, resulting in reduced production efficiency and increased costs. In this case, there is an urgent need for a more intelligent operation and maintenance solution that can monitor equipment status in real time, predict faults in advance, and automatically generate maintenance recommendations, thereby reducing labor costs, improving maintenance efficiency, and ultimately bringing higher economic benefits to the enterprise.

[0022] With the development of Internet of Things technology, the amount of data generated by industrial equipment has exploded. This data contains a lot of information about the operating status of the equipment, but its diversity and complexity make it difficult for traditional analysis methods to fully tap its value. How to effectively use these massive data to achieve real-time fault diagnosis and predictive maintenance has become a major demand in the field of equipment operation and maintenance. ChatGPT has powerful text understanding and generation capabilities, which can convert complex multivariate data (such as temperature, pressure, vibration, etc.) into natural language descriptions, and generate fault diagnosis and maintenance suggestions based on these descriptions. This capability can bring many advantages to equipment operation and maintenance. For example, ChatGPT can identify abnormal patterns and predict possible failures by analyzing the natural language description of sensor data, which helps to improve the effect of predictive maintenance; it can generate detailed maintenance plans based on the diagnosis results to help operation and maintenance personnel make decisions quickly and reduce downtime; it can present data analysis results in the form of natural language reports, making it easier for operation and maintenance personnel and non-technical personnel to understand and apply the analysis results.

[0023] The equipment operation and maintenance solution based on ChatGPT solves many technical problems in traditional operation and maintenance systems. First, it realizes real-time fault detection and diagnosis of equipment sensor data through intelligent data analysis and natural language processing. It can automatically identify complex fault modes and generate easy-to-understand natural language explanations, improving the readability and interpretability of data. This method enables operation and maintenance personnel to quickly grasp the status of equipment and intuitively understand potential faults. Secondly, the solution can automatically generate maintenance suggestions and optimization plans to help operation and maintenance personnel make decisions quickly, thereby reducing the need for manual analysis and significantly improving maintenance efficiency.

[0024] In addition, the ChatGPT-based solution supports predictive maintenance, identifying possible faults in advance by analyzing historical data and trends, reducing equipment downtime and maintenance costs. Faced with massive amounts of industrial data, ChatGPT's data preprocessing and feature extraction capabilities can efficiently process multiple data types and adapt to cross-device and cross-domain application scenarios in a highly versatile natural language generation method. This solution effectively improves the level of intelligence in equipment operation and maintenance, bringing higher efficiency and significant cost savings to industrial production.

[0025] Natural language processing (NLP) technology, especially the development of generative pre-trained models such as ChatGPT, provides new possibilities for intelligent equipment operation and maintenance. ChatGPT has powerful natural language understanding and generation capabilities, and can be trained based on massive text data, making it excellent in processing unstructured descriptive data. In equipment operation and maintenance, ChatGPT can convert complex sensor data into natural language descriptions and generate fault analysis and maintenance recommendations, injecting intelligence and automation capabilities into traditional operation and maintenance systems.

[0026] See also Figure 1 ,This application discloses a method for analyzing equipment operation and maintenance data and predicting faults based on CHATGPT, including: collecting data using equipment sensors; Clean and extract features from data to obtain natural language description; Capture and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; Perform trend analysis based on historical equipment data, analysis results, and maintenance recommendations to predict and output future failures.

[0027] In an embodiment of the present application, the analysis results, maintenance suggestions and prediction outputs of future faults are delivered to the operation and maintenance personnel.

[0028] In an embodiment of the present application, maintenance feedback information from the operation and maintenance personnel is obtained to determine whether the analysis result is correct; if the analysis result is incorrect, the maintenance feedback information is used to replace the analysis result.

[0029] In the embodiment of the present application, cleaning the data includes denoising and standardization.

[0030] In an embodiment of the present application, the equipment historical data includes historical temperature data, historical pressure data and vibration data.

[0031] In an embodiment of the present application, a CHATGPT-based equipment operation and maintenance data analysis and fault prediction system includes: A data acquisition module, used to collect data using device sensors; Data preprocessing module, used to clean and extract features from data to obtain natural language description; Natural language generation module, used to obtain and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; Prediction and decision-making module, which is used to perform trend analysis based on historical equipment data, analysis results and maintenance recommendations, and predict and output future failures; The alarm and reporting module is used to deliver analysis results, maintenance recommendations and predictive outputs of future failures to operation and maintenance personnel.

[0032] The feedback and closed-loop optimization module is used to obtain maintenance feedback information from operation and maintenance personnel and determine whether the analysis result is correct; if the analysis result is incorrect, the maintenance feedback information is used to replace the analysis result.

[0033] The system includes data acquisition module, data preprocessing module, natural language generation module (ChatGPT), prediction and decision module, alarm and report module, feedback and closed-loop optimization module. Its main workflow is as follows: after the equipment sensor in the data acquisition module collects data and sends it to the system, the data collected by the sensor is cleaned and feature extracted by the data preprocessing module, and then converted into natural language description and input into the natural language generation module for analysis and reasoning. The generated analysis results and maintenance suggestions are finally sent to the operation and maintenance personnel in the form of alarms or reports.

[0034] Data acquisition module, which is responsible for collecting various data from equipment sensors in real time, such as temperature, pressure, vibration, and humidity. The data acquisition frequency and sampling rate can be flexibly adjusted according to the equipment operation to ensure the real-time and accuracy of the data.

[0035] The data preprocessing module performs denoising, standardization, and feature extraction on the data collected by the device sensors. Since the natural language generation module ChatGPT has limited ability to directly process the numerical values ​​of the data, it is necessary to convert the data into text descriptions that the natural language generation module ChatGPT can understand.

[0036] The natural language generation module (ChatGPT) is the core of the system. It analyzes the descriptive text generated by the data preprocessing module by calling the ChatGPT model, identifies fault modes and generates maintenance suggestions. ChatGPT has powerful natural language understanding and generation capabilities. It can combine data descriptions and equipment status to infer possible fault causes and give recommended operations.

[0037] The prediction and decision module implements predictive maintenance by combining the output of the natural language generation module with the equipment historical data for trend analysis. This module is able to predict possible future failures based on data (such as temperature, pressure, and vibration) and trend changes.

[0038] The alarm and report module is responsible for delivering the system's analysis results, maintenance suggestions and fault predictions to operation and maintenance personnel in the form of alarms and reports to ensure the timeliness and effectiveness of information transmission.

[0039] Feedback and closed-loop optimization module, which uses maintenance feedback information from operation and maintenance personnel to continuously optimize the system's fault detection, diagnosis and prediction capabilities to improve the accuracy and adaptability of the system and achieve intelligent self-improvement.

[0040] In an embodiment of the present application, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the program.

[0041] In an embodiment of the present application, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0042] In the test case of the implementation of the present invention, the equipment operation and maintenance system integrates ChatGPT for fault detection, predictive maintenance and feedback optimization. The test starts with the system startup, and the system enters the monitoring state and prepares to collect real-time data of the equipment. The temperature sensor, pressure sensor and vibration sensor on the equipment are activated to collect data on the equipment operation status in real time, and transmit the data to the main system every 10 seconds to ensure accurate monitoring of the equipment status.

[0043] After receiving the data collected by the sensor, the system's preprocessing module begins to clean and standardize the data to remove noise and outliers. For example, when an accidental temperature peak appears in the data, the system will smooth the data to eliminate the influence of errors and ensure the accuracy of the data. Next, the preprocessing module extracts key feature values, such as temperature average, pressure change rate, and vibration peak, to further optimize the data for easy analysis. The preprocessed data is converted into natural language descriptions, such as "the temperature continues to rise to 85°C, exceeding the normal range", or "the vibration level of the equipment has increased significantly."

[0044] The generated natural language description is then passed to the natural language generation module, and the system enters the fault analysis phase. After receiving these descriptions, the natural language generation module uses its natural language understanding and generation capabilities to identify possible failure modes. In the test, the natural language generation module successfully identified common failure modes such as "overtemperature" or "cooling system abnormality" and generated maintenance suggestions, such as "check the cooling system" or "replace the fan." If the natural language generation module fails to identify the failure mode, it returns to the "normal" state and the system continues to monitor the device data; if a potential failure is identified, the system triggers an alarm and generates a detailed maintenance report.

[0045] During the test, the system combines historical data including temperature data, pressure data and vibration data with the change trajectory of key equipment parameters obtained by the real-time monitoring system to perform trend analysis to assess the risk of possible equipment failure. Through the change trajectory of key equipment parameters obtained by the real-time monitoring system, the current operating status change of the equipment can be identified, which is the current trend. At the same time, the system will also analyze the historical trend of the equipment in combination with the data accumulation of long-term operation of the equipment, and find potential signs of failure by comparing the normal operation mode in the current data with the historical data. For example, when the vibration data matches the vibration data in the historical "bearing damage" mode, the system can generate prediction results including normal bearing operation and bearing damage by combining the comprehensive analysis of the current trend and the historical trend, providing forward-looking judgments on possible equipment failures, helping operation and maintenance personnel to identify and take preventive measures in advance to avoid equipment downtime or damage, and giving predictive maintenance suggestions: prompting operation and maintenance personnel to check the bearing in advance to prevent potential damage failures. At the same time, the system automatically generates an alarm and sends a report to the operation and maintenance team. The report content includes fault analysis, historical trends, prediction results and maintenance suggestions, so that the operation and maintenance personnel can take measures according to the report.

[0046] After the test is completed, the system enters the closed-loop optimization and feedback collection phase. The operation and maintenance personnel complete the maintenance operations according to the recommendations in the report and input the maintenance feedback into the system. The system uses this feedback data to optimize the fault library and analysis model to improve the accuracy and reliability of the next fault detection. With the help of feedback data, the system can gradually optimize the diagnostic rules, realize adaptive learning, and form a closed-loop intelligent operation and maintenance process. Finally, the system returns to the monitoring state and continues to wait for and monitor the real-time data of the equipment.

[0047] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0048] Those skilled in the art will readily appreciate other embodiments of the invention after considering the specification and practicing the invention invented herein. This application is intended to cover any variations, uses or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not invented by the present invention. The specification and examples are intended to be exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0049] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above are only specific implementation methods of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. Equipment operation and maintenance data analysis and fault prediction method based on CHATGPT, characterized in that: include: Collect data using device sensors; Clean and extract features from data to obtain natural language description; Capture and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; Perform trend analysis based on historical equipment data, analysis results, and maintenance recommendations to predict and output future failures.

2. The equipment operation and maintenance data analysis and fault prediction method based on CHATGPT according to claim 1 is characterized in that: The analysis results, maintenance recommendations and prediction outputs of future failures are delivered to the operation and maintenance personnel.

3. The equipment operation and maintenance data analysis and fault prediction method based on CHATGPT according to claim 2 is characterized in that: Obtain maintenance feedback information from the operation and maintenance personnel to determine whether the analysis result is correct; if the analysis result is incorrect, replace the analysis result with the maintenance feedback information.

4. The equipment operation and maintenance data analysis and fault prediction method based on CHATGPT according to claim 1 is characterized in that: Data cleaning includes denoising and standardization.

5. The equipment operation and maintenance data analysis and fault prediction method based on CHATGPT according to claim 1 is characterized in that: Equipment historical data includes historical temperature data, historical pressure data and vibration data.

6. Equipment operation and maintenance data analysis and fault prediction system based on CHATGPT, characterized by: include: A data acquisition module, used to collect data using device sensors; Data preprocessing module, used to clean and extract features from data to obtain natural language description; Natural language generation module, used to obtain and analyze natural language descriptions, identify failure modes, and obtain analysis results and maintenance recommendations; The prediction and decision-making module is used to perform trend analysis based on historical equipment data, analysis results and maintenance recommendations, and predict and output future failures.

7. The equipment operation and maintenance data analysis and fault prediction system based on CHATGPT according to claim 6 is characterized in that: The alarm and reporting module is used to deliver analysis results, maintenance recommendations and predictive outputs of future failures to operation and maintenance personnel.

8. The equipment operation and maintenance data analysis and fault prediction system based on CHATGPT according to claim 7 is characterized in that: The feedback and closed-loop optimization module is used to obtain maintenance feedback information from operation and maintenance personnel and determine whether the analysis result is correct; if the analysis result is incorrect, the maintenance feedback information is used to replace the analysis result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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