Neural network-based equipment life prediction Web application and system construction method

Abstract

The invention relates to an equipment residual life prediction Web application and system construction method based on a neural network, and the application employs a distributed platform architecture, and comprises a data collection layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer. The data acquisition layer is used for collecting data related to the service life of the equipment from various industrial equipment; the data processing layer is used for cleaning, sorting and converting original data and executing data preprocessing and feature engineering operation; the prediction model layer is used for performing equipment life prediction by adopting a neural network model; the application service layer is used for processing business logic and providing a service interface; and the user interaction layer is used for providing a user interface, allowing a user to access the system, and performing system configuration operation by inputting related data of the equipment and checking the service life prediction result of the equipment. The application can intelligently manage the equipment, optimize the service life and reduce the fault and downtime, and has a wide market application prospect.

Description

Technical Field

[0001] The present application relates to the intersection of artificial intelligence and predictive maintenance, and specifically to a neural network-based equipment life prediction Web application and system construction method. Background Art

[0002] With the continuous development of industrial automation and intelligence, the importance of equipment maintenance technology has become increasingly prominent. In industrial production, the operating status of equipment is directly related to production efficiency, product quality and production safety. However, due to the complex equipment operating environment and variable workload, traditional maintenance methods are difficult to meet the needs of modern industry for efficient, low-cost and precise maintenance.

[0003] Traditional equipment maintenance methods mainly include corrective maintenance and preventive maintenance. Corrective maintenance is to repair equipment after it fails. Although it is simple and direct, it often leads to production interruptions and high maintenance costs. Preventive maintenance is based on fixed time cycles to maintain equipment. Although it can partially reduce the risk of sudden failures, it is out of touch with the actual operating status of the equipment and is prone to waste of resources or insufficient maintenance. These two methods can no longer fully meet the needs of high equipment availability and low-cost operation in modern industrial scenarios.

[0004] In recent years, with the rapid development of artificial intelligence, the Internet of Things, and big data technologies, predictive maintenance based on Web application systems has gradually become an emerging maintenance strategy. Predictive maintenance provides real-time monitoring of equipment operating status and historical data analysis through Web applications, and uses deep learning models to predict the remaining service life of equipment, thereby achieving early prediction of potential failures and providing a scientific basis for equipment maintenance. Compared with traditional maintenance methods, Web-based predictive maintenance systems have significant advantages, such as reducing maintenance costs, reducing downtime, optimizing equipment utilization, and ensuring production safety.

[0005] In this type of Web application system, the equipment life prediction method based on deep learning models such as artificial neural networks, convolutional neural networks, and long short-term memory networks (LSTM) has become a core technology due to its powerful analysis ability of complex time series data. In particular, the LSTM network can capture the long-term dependencies in the equipment operation data and is widely used in the equipment life prediction module in the Web system, showing excellent performance. By integrating these technologies, predictive maintenance Web applications provide industrial enterprises with efficient and intelligent equipment management solutions, opening up new paths for optimizing production efficiency and reducing maintenance costs. Summary of the invention

[0006] The embodiment of the present application provides a neural network-based equipment life prediction Web application and system construction method to achieve life prediction of various types of industrial equipment, improve equipment maintenance efficiency, reduce maintenance costs, and provide a scientific basis for equipment maintenance and replacement, thereby providing intelligent solutions for equipment management and maintenance work of industrial enterprises.

[0007] To solve the above technical problems, in the first aspect, an embodiment of the present application provides a neural network-based equipment life prediction Web application, which adopts a distributed platform architecture, including: a data acquisition layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer connected in sequence; wherein the data acquisition layer is used to collect data related to equipment life from various types of industrial equipment in real time, and transmit the original data to the data processing layer for processing; the data processing layer is used to clean, organize and convert the original data collected by the data acquisition layer, and perform data preprocessing and feature engineering operations to improve data quality and availability; the prediction model layer is used to input the data processed by the data processing layer into a neural network model, and use the neural network model to predict equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict equipment life based on the input data; the application service layer is used to process business logic and provide service interfaces; the business logic includes algorithm logic, data storage and retrieval, model call and management functions of equipment life prediction; the user interaction layer is used to provide a user interface, allowing users to access the system, view equipment life prediction results by inputting equipment-related data, and perform system configuration operations; each module communicates through a RESTful API.

[0008] In some exemplary embodiments, the data collection layer includes a data collection terminal, an industrial equipment interface, and a data upload module; the data collection terminal is used to collect equipment operating parameters, including temperature, pressure, humidity, and vibration parameters; the industrial equipment interface is used to connect to various types of industrial equipment; the data upload module is used to upload real-time data and historical data respectively. The data upload module supports uploading equipment data files in CSV and JSON formats, and provides a data analysis portal.

[0009] In some exemplary embodiments, the data processing layer includes a data cleaning module, a feature extraction module, a data formatting module and an anomaly detection module; the data processing layer uses data cleaning, feature engineering and anomaly detection techniques to process the raw data to improve the accuracy and reliability of the prediction model.

[0010] In some exemplary embodiments, the prediction model layer includes an artificial neural network deep learning model and a long short-term memory network deep learning model.

[0011] In some exemplary embodiments, the application service layer includes a model management module, a prediction service module, a task management module and a report generation module; the prediction service module includes an equipment life prediction algorithm module, and the application service layer can support integration with other systems or modules to realize business processes related to equipment management and maintenance.

[0012] In some exemplary embodiments, the user interaction layer includes a user interface module, a permission management module, a configuration management module, and a prediction result display module; the prediction result display module includes a key performance indicator display module, through which the user can view the device status, access the trained model and view related data.

[0013] In some exemplary embodiments, the neural network-based equipment life prediction Web application provided by the present application also includes: a physical device layer connected to the data acquisition layer; the physical device layer includes an industrial equipment module, a sensor module, a wireless transmission module and an edge computing module.

[0014] In a second aspect, the embodiment of the present application further provides a method for constructing a neural network-based equipment life prediction Web application system, which is used for the neural network-based equipment life prediction Web application described in the above embodiment, and includes the following steps: collecting real-time operation data of the equipment; cleaning, collating, converting the collected data, and performing data preprocessing, feature engineering, and anomaly detection;

[0015] A neural network model is used to predict the equipment life; the neural network model includes an artificial neural network deep learning model and a long short-term memory network deep learning model; an equipment life prediction report is provided, and the prediction report includes the remaining life of the equipment, health status, risk level and maintenance recommendations.

[0016] In some exemplary embodiments, a neural network model is used to predict equipment life, including: the neural network model learns patterns and trends of equipment life from historical data, and predicts equipment life based on input data.

[0017] The technical solution provided by the embodiments of the present application has at least the following advantages:

[0018] The embodiment of the present application provides a neural network-based equipment life prediction Web application and system construction method, the application adopts a distributed platform architecture, including: a data acquisition layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer connected in sequence; wherein the data acquisition layer is used to collect data related to equipment life from various types of industrial equipment in real time, and transmit the original data to the data processing layer for processing; the data processing layer is used to clean, organize and convert the original data collected by the data acquisition layer, and perform data preprocessing and feature engineering operations to improve data quality and availability; the prediction model layer is used to input the data processed by the data processing layer into a neural network model, and use the neural network model to predict equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict equipment life based on the input data; the application service layer is used to process business logic and provide service interface; the business logic includes algorithm logic, data storage and retrieval, model call and management functions of equipment life prediction; the user interaction layer is used to provide a user interface, allowing users to access the system, view equipment life prediction results by inputting equipment-related data, and perform system configuration operations; the modules of the back-end service and the front-end service communicate through a RESTful API.

[0019] The present application relates to a neural network-based equipment remaining life prediction Web application and system construction method, which aims to improve equipment maintenance efficiency, reduce maintenance costs, and provide a scientific basis for equipment management. The application includes five levels: data collection, processing, prediction, application, and interaction. The data collection layer is responsible for collecting equipment operation data, the data processing layer cleans and preprocesses the data, the prediction model layer realizes equipment life prediction through neural networks, and the application service layer integrates data processing and model analysis results, providing model management, life prediction, task scheduling, report generation, and notification push functions. The user interaction layer supports users to upload equipment data through the interface or RESTful API and generates a prediction report containing the remaining life, health status, and maintenance recommendations of the equipment, providing decision support for equipment managers. The application can intelligently manage equipment, optimize service life, reduce failures and downtime, and has broad market application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Unless otherwise stated, the pictures in the drawings do not constitute proportional limitations.

[0021] Figure 1 It is a structural diagram of a neural network-based equipment life prediction Web application provided in an embodiment of the present application.

[0022] Figure 2 This is an architecture diagram of a neural network-based equipment life prediction Web application provided in an embodiment of the present application.

[0023] Figure 3 It is a flow chart of a method for constructing a neural network-based equipment life prediction Web application system provided in an embodiment of the present application.

[0024] Figure 4 It is a schematic diagram of the technical development system architecture provided in the embodiment of the present application.

[0025] Figure 5 It is a schematic diagram of the user login interface provided in an embodiment of the present application.

[0026] Figure 6 It is a schematic diagram of the main interface of the system provided in the embodiment of the present application.

[0027] Figure 7 It is a schematic diagram of the model center provided in the embodiment of the present application.

[0028] Figure 8 It is a schematic diagram of a data center provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] Traditional equipment management and maintenance often rely on experience and regular maintenance plans, and lack the ability to accurately predict equipment life. Due to the inflexibility and inefficiency of maintenance plans, equipment failures and downtime increase. Therefore, a method and system that can accurately predict equipment life is needed so that maintenance measures can be taken in advance and equipment management and maintenance work can be optimized.

[0030] In order to solve the above technical problems, the embodiment of the present application provides a neural network-based equipment life prediction Web application and system construction method, the application adopts a distributed platform architecture, including: a data acquisition layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer connected in sequence; wherein the data acquisition layer is used to collect data related to equipment life from various types of industrial equipment in real time, and transmit the original data to the data processing layer for processing; the data processing layer is used to clean, organize and convert the original data collected by the data acquisition layer, and perform data preprocessing and feature engineering operations to improve data quality and availability; the prediction model layer is used to input the data processed by the data processing layer into a neural network model, and use the neural network model to predict equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict equipment life based on the input data; the application service layer is used to process business logic and provide service interface; the business logic includes algorithm logic, data storage and retrieval, model call and management functions of equipment life prediction; the user interaction layer is used to provide a user interface, allowing users to access the system, view equipment life prediction results by inputting equipment-related data, and perform system configuration operations; the modules of the back-end service and the front-end service communicate through a RESTful API. The application provides a neural network-based equipment life prediction Web application and system construction method to achieve life prediction of various industrial equipment, improve equipment maintenance efficiency, reduce maintenance costs, and provide a scientific basis for equipment maintenance and replacement, thereby providing intelligent solutions for equipment management and maintenance work of industrial enterprises.

[0031] The following will describe the various embodiments of the present application in detail with reference to the accompanying drawings. However, it will be appreciated by those skilled in the art that in the various embodiments of the present application, many technical details are provided in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solution claimed in the present application can be implemented.

[0032] See also Figure 1 The embodiment of the present application provides a device life prediction Web application based on a neural network. The Web application adopts a distributed platform architecture, including: a data acquisition layer 101, a data processing layer 102, a prediction model layer 103, an application service layer 104 and a user interaction layer 105 connected in sequence.

[0033] The system architecture of this embodiment includes five levels: data collection, processing, prediction, service and user interaction. Among them, the data collection layer 101 is used to collect data related to equipment life from various industrial equipment in real time, and transmit the original data to the data processing layer 102 for processing; the data processing layer 102 is used to clean, organize and convert the original data collected by the data collection layer 101, and perform data preprocessing and feature engineering operations to improve data quality and availability; the prediction model layer 103 is used to input the data processed by the data processing layer 102 into the neural network model, and use the neural network model to predict the equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict the equipment life based on the input data; the application service layer 104 is used to process business logic and provide service interfaces; the business logic includes the algorithm logic of equipment life prediction, data storage and retrieval, model call and management functions; the user interaction layer 105 is used to provide a user interface, allowing users to access the system, view the equipment life prediction results by inputting equipment-related data, and perform system configuration operations; the various modules of the back-end service and the front-end service communicate through RESTful API.

[0034] The equipment life prediction Web application based on neural network provided by the present application includes the following functional layers: data processing layer 102, prediction model layer 103, application service layer 104 and user interaction layer 105. Among them, the data processing layer 102 is located after the data acquisition layer 101, and is mainly responsible for cleaning, sorting and converting the collected raw data for subsequent analysis and modeling; the prediction model layer 103 is the core part of the system, and uses a neural network-based model to predict the equipment life; the application service layer 104 is responsible for business logic processing and service interface provision functions; the user interaction layer 105 is a user-oriented front-end service component, responsible for providing interactive interface and operation functions.

[0035] In some embodiments, the neural network-based equipment life prediction Web application provided by the present application also includes: a physical device layer 106 connected to the data acquisition layer 101; the physical device layer 106 includes an industrial equipment module, a sensor module, a wireless transmission module and an edge computing module.

[0036] like Figure 2As shown, an embodiment of the present application provides a module system architecture of a device life prediction Web application based on a neural network. In actual application, this system first collects device operation data through the sensors of the physical device layer 106, and transmits the data to the data acquisition layer 101 through the wireless transmission module. The data acquisition layer 101 performs preliminary processing on the data and uploads it to the data processing layer 102. The data processing layer 102 then completes data cleaning, feature extraction, anomaly detection and formatting in sequence. Subsequently, the application service layer 104 calls the corresponding prediction model to analyze the data and generates a prediction result for the device life. Finally, the user interaction layer 105 displays the prediction results to the user through a visual interface, and generates relevant analysis reports for the user to download and refer to.

[0037] In some embodiments, the data collection layer 101 includes a data collection terminal, an industrial equipment interface, and a data upload module; the data collection terminal is used to collect equipment operating parameters, including temperature, pressure, humidity, and vibration parameters; the industrial equipment interface is used to connect to various types of industrial equipment; the data upload module is used to upload real-time data and historical data respectively. The data upload module supports uploading equipment data files in CSV and JSON formats, and provides a data analysis portal.

[0038] In some embodiments, the data processing layer 102 includes a data cleaning module, a feature extraction module, a data formatting module and an anomaly detection module; the data processing layer 102 uses data cleaning, feature engineering and anomaly detection techniques to process the original data to improve the accuracy and reliability of the prediction model.

[0039] The data processing layer 102 is used to pre-process the collected raw data to improve data quality and availability. Specifically, the data processing layer 102 includes modules such as data cleaning, feature engineering, and anomaly detection, which are used to remove noise and outliers in the raw data, extract key features, and convert the data into a format acceptable to the prediction model, providing reliable data support for subsequent analysis and modeling.

[0040] In some embodiments, the prediction model layer 103 includes an artificial neural network deep learning model and a long short-term memory network deep learning model.

[0041] The prediction model layer 103 is the core part of the system, responsible for designing, training and optimizing the equipment life prediction model. This layer uses neural network-based algorithms, including artificial neural networks (ANN), convolutional neural networks (CNN) and long short-term memory networks (LSTM), etc., which can learn patterns and trends from historical data of equipment operation. Through continuous optimization of the model, this layer can achieve high-precision equipment life prediction based on current input data, providing technical support for predictive maintenance.

[0042] In some embodiments, the application service layer 104 includes a model management module, a prediction service module, a task management module and a report generation module; the prediction service module includes an equipment life prediction algorithm module, and the application service layer 104 can support integration with other systems or modules to realize business processes related to equipment management and maintenance.

[0043] As the backend service component of the system, the application service layer 104 is responsible for implementing the algorithm logic, data storage and retrieval, model calling and management functions of equipment life prediction. The application service layer 104 completes the equipment life prediction task by cooperating with the data processing layer and the prediction model layer, and provides the prediction results to the external system or module in the form of an interface. In addition, the application service layer 104 can also be integrated with the equipment management system to support business processes related to equipment maintenance.

[0044] In some embodiments, the user interaction layer 105 includes a user interface module, a permission management module, a configuration management module, and a prediction result display module; the prediction result display module includes a key performance indicator display module, through which the user can view the device status, access the trained model and view related data.

[0045] The user interaction layer 105 is a user-oriented front-end service component, which is mainly used to provide functions such as system configuration, data input and result display. Through the user interaction layer, users can upload equipment operation data, select prediction models, view life prediction results, and perform necessary system configuration operations. This layer usually includes user interface design, front-end development, and interaction interfaces with back-end services, aiming to provide users with a friendly user experience.

[0046] See also Figure 3 The present application embodiment also provides a method for constructing a neural network-based equipment life prediction Web application system, which is used for the neural network-based equipment life prediction Web application described in the above embodiment, and includes the following steps:

[0047] Step S1, collecting real-time operation data of the equipment.

[0048] Step S2: Clean, organize, and convert the collected data, and perform data preprocessing, feature engineering, and anomaly detection.

[0049] Step S3: using a neural network model to predict the life of the equipment; the neural network model includes an artificial neural network deep learning model and a long short-term memory network deep learning model.

[0050] Step S4: Provide an equipment life prediction report, which includes the equipment's remaining life, health status, risk level, and maintenance recommendations.

[0051] In some embodiments, a neural network model is used in step S3 to predict the equipment life, including: the neural network model learns the patterns and trends of the equipment life from historical data, and predicts the equipment life based on the input data.

[0052] Specifically, the present application provides a method for constructing a neural network-based equipment life prediction Web application system, comprising the following steps:

[0053] Step S1: The system obtains the equipment operation data through the data acquisition layer, uses sensors or industrial equipment interfaces to collect key parameters of the equipment in real time, such as temperature, vibration, pressure, load and other information, and uploads the data to the system server through a wireless transmission module or a data acquisition terminal. Users can also manually upload the historical operation data file of the equipment through the system's front-end interface or RESTful API, supporting multiple data formats such as CSV and JSON, so that the system can perform subsequent processing on the data.

[0054] Step S2: The uploaded data enters the data processing layer, and the system cleans and preprocesses it. Specifically, the data cleaning module detects and removes outliers, noise or invalid data, and fills in missing values ​​to ensure the integrity and consistency of the data. Subsequently, the feature extraction module extracts key features based on the equipment operation data, such as the frequency of the vibration signal, the temperature fluctuation rate, etc., and generates a feature vector describing the equipment status. The data formatting module converts the processed data into a unified structured format that can be directly used by the prediction model, and stores it in the intermediate data table for subsequent calls.

[0055] Step S3: After the data processing is completed, the system calls the neural network model deployed by the prediction model layer to analyze and predict the data. The model calculates the input feature data through the trained deep learning algorithm (such as long short-term memory network LSTM or convolutional neural network CNN), and outputs the remaining life prediction value, health status score and failure risk probability of the equipment. During the operation of the model, the system will automatically identify the validity of the input data. If an exception occurs in the prediction (such as inconsistent data format), the system will generate an exception report in time and notify the user to make corrections.

[0056] Step S4: The system passes the output results of the prediction model to the application service layer, integrates the analysis results and generates an equipment prediction report for user reference. The report content includes the equipment remaining life prediction curve, health status score change trend, potential failure risk analysis and targeted maintenance suggestions, such as replacement of parts, lubrication of equipment or cleaning operations. At the same time, the system provides a visualization function for the report, which intuitively displays the operating status and future trends of the equipment in the form of charts. Users can export the report in PDF or Excel format, or send the report to relevant personnel through the system's sharing function.

[0057] Step S5, the user accesses the system function through the user interaction layer to view the prediction results or perform related operations. The system provides a Web-based user interface that supports multi-terminal access (such as PC and mobile terminals). Users can log in to the system at any time to view the equipment status, prediction results and historical reports. Dynamic visualization tools (such as ECharts or D3.js) are used to display the prediction results. Users can interactively view the equipment status trend chart and life prediction chart. In addition, the system supports users to adjust parameters (such as prediction range or data collection frequency) as needed and resubmit the prediction task. At the same time, the system also provides an abnormal notification function. If the prediction results show that the equipment has a high risk of failure or the life is lower than the set threshold, the system will promptly remind the user through SMS, email or system notification to ensure that maintenance measures can be implemented in time.

[0058] like Figure 4 As shown, this application provides a technical development and construction method for a device life prediction Web application based on a neural network. The method completes the deployment and operation of the entire system from the front-end architecture design of the system to the back-end data processing, and then to the database design and artificial intelligence (AI) model integration, ensuring that the Web application can provide services efficiently and stably.

[0059] First, in the front-end architecture design and development, the system uses Vue.js as the front-end development framework, and combines the Element-plus component library to build the user interaction interface. By modularizing the system functions, users can complete core functions such as user login and registration, data management, model selection and calling, and prediction result display in the browser. The front-end interacts with the back-end through Axios, and uses ECharts to provide data visualization support, presenting the prediction results in an intuitive chart form. After the development is completed, the front-end static resources are built and deployed to the Nginx server through Vue CLI, providing users with fast access capabilities.

[0060] The backend mainly uses the Express framework to build a RESTful API interface to communicate with the frontend and database. In order to ensure the security of the system, the backend implements user authentication through JWT and provides services after verifying the legitimacy of the user. The backend is also responsible for processing the data uploaded by the user, which is stored in the MySQL database after parsing and preprocessing. In addition, the backend loads the AI ​​model by calling the Python script, uses the model to analyze the uploaded data and returns the prediction results. At the same time, Redis is used as a cache tool to store user session information and hot spot data to further improve the response speed of the system.

[0061] In terms of database design, the system uses MySQL as a relational database to store user information, device information, historical prediction records and other data. By defining the data model through Sequelize, the backend can efficiently operate the database, including adding, deleting, modifying and querying data. In order to ensure the security and reliability of data, the system has designed a backup and recovery mechanism for the database to ensure that key data will not be lost due to accidents.

[0062] In terms of AI model integration and calling, the system interacts with Python scripts through Node.js to load and run AI models. The trained model is stored in the server as a file. The data uploaded by the user is pre-processed by the backend and passed to the Python script. After the model is predicted, the prediction result is generated. The result data is returned to the backend in JSON format, and then passed from the backend to the frontend for display.

[0063] In terms of system deployment, in order to ensure the stability and high availability of the system, the front-end, back-end, database and AI model services are all deployed independently. The front-end static resources are hosted by the Nginx server, the back-end service uses PM2 for process management to ensure reliable operation, the MySQL database is deployed on a dedicated database server, and the AI ​​model service uses the Flask framework to run and provide HTTP interface. In addition, in order to improve deployment efficiency and scalability, each component of the system supports containerized deployment, and flexible service orchestration and management are achieved through Docker and Kubernetes technologies.

[0064] like Figure 5 As shown, the present application provides a user login interface for a neural network-based equipment life prediction Web application. After the user starts the system through the interface, the system starts to collect equipment data. At this time, the status data of the equipment operation will be collected in real time. Figure 5 It can be seen that the user login interface of a neural network-based equipment life prediction Web application provided in the application includes the following functions;

[0065] Equipment status monitoring is used to monitor various parameters of equipment in real time through sensors, such as temperature, vibration, load, working hours, etc. The embedded sensors in the equipment are connected to the data acquisition terminal (such as PLC, data acquisition card) to periodically obtain equipment status data.

[0066] Data upload is used for data acquisition module to transmit data with the system database through the interface, ensuring that key data of equipment operation can be uploaded to the system in time for subsequent processing.

[0067] Historical data import: users can also manually upload historical device data (such as CSV, Excel format) to ensure that the system can perform long-term trend analysis based on the historical status of the device.

[0068] like Figure 6 As shown, the present application provides a main system interface of a neural network-based equipment life prediction Web application, where users can view equipment status, model information, and key performance indicators (KPIs). The data processing layer supports data processing and storage in the background. Figure 6 It can be seen that the main interface of the system of a neural network-based equipment life prediction Web application provided in this application includes the following functional features;

[0069] Data cleaning is used by the system to remove noise from the raw data collected by the equipment and remove data points that do not meet the standards. The missing values ​​are filled by algorithms to ensure the integrity of the data.

[0070] Data standardization and normalization are used to standardize data collected by different devices and sensors to ensure data consistency and avoid poor model prediction results due to dimensional differences.

[0071] Feature engineering is used to extract valuable features from raw equipment data, such as equipment operating cycle, temperature fluctuation, vibration frequency, etc. The purpose of feature engineering is to extract indicators that have a significant impact on equipment life prediction and provide the best input for the prediction model.

[0072] Data storage and management: The processed data will be stored in the system database to ensure that the operating data of all equipment can be efficiently managed and retrieved. The system supports the query and backtracking of historical data, which is convenient for analyzing the long-term operating trend of equipment.

[0073] like Figure 7 As shown, the present application provides a model center interface for a neural network-based equipment life prediction Web application, in which users can view the trained model information in the system or upload a custom prediction model for analysis. Figure 7 It can be seen that the model center interface of a neural network-based equipment life prediction Web application provided in this application includes the following functional features;

[0074] Model selection and training: The system provides a variety of prediction algorithms, such as regression analysis, random forest, support vector machine (SVM), deep neural network, etc. Users can choose the appropriate algorithm according to their specific needs. The system is trained based on the historical data of the device and gradually optimizes the accuracy of the prediction model.

[0075] Model evaluation: Each trained model will be evaluated for performance, including indicators such as prediction accuracy and recall. Users can view the evaluation results of each model to help select the most appropriate model.

[0076] Model update and maintenance: users can upload customized models, and the system supports the management and update of different versions of models. The uploaded models will be automatically tested and verified to ensure that they can adapt to changes in the operating status of the equipment.

[0077] Real-time prediction and analysis: After users upload the equipment operation data, the system selects the appropriate prediction model for real-time analysis to generate the equipment’s remaining life, health status, and failure risk level. The system provides corresponding maintenance recommendations based on the prediction results.

[0078] like Figure 8 As shown, the present application provides a data center interface for a neural network-based equipment life prediction Web application, through which users upload equipment data and perform life prediction. This layer is responsible for data processing, analysis, and report generation. Figure 8 It can be seen that the data center interface of a neural network-based equipment life prediction Web application provided in this application includes the following functional features;

[0079] Device information management: users can view the basic information and current status of the device through the main interface, and can view the operating data, health status, historical maintenance records, etc. of each device.

[0080] Data upload and management: users can upload new equipment data, and the system will automatically analyze and generate forecast reports. The system supports classified management of equipment data, which is convenient for users to conduct detailed analysis based on different equipment.

[0081] Report generation and export: After the user completes uploading the device data, the system will call the prediction model for analysis and generate a prediction report.

[0082] Maintenance recommendations and decision support: Based on the model prediction results, the system will automatically generate maintenance recommendations for the equipment and provide decision support information such as equipment usage cycle and maintenance recommendations.

[0083] The following is a detailed introduction and analysis of the neural network-based equipment life prediction Web application and system construction method provided by the present application through specific embodiments.

[0084] Taking a certain enterprise device as an example, the device administrator completes the following operations:

[0085] The first step is to log in to the system;

[0086] The administrator enters the user name and password and enters the main interface after passing the security verification.

[0087] The second step is to enter the equipment information;

[0088] Add basic information and operation data of the equipment in the equipment management module.

[0089] The third step is to select the analysis model;

[0090] In the Model Center, select an AI model suitable for the device, or upload a custom model.

[0091] The fourth step is to upload device data and make predictions;

[0092] The administrator uploads the equipment operation status data, and the system generates a prediction report after analysis, including the remaining life of the equipment, health status and maintenance recommendations.

[0093] In summary, the neural network-based equipment life prediction Web application provided by this application has the following beneficial effects:

[0094] (1) Improve equipment maintenance efficiency. Accurate equipment life prediction enables maintenance personnel to take maintenance measures in a timely manner to avoid equipment failures and increased downtime, thereby improving equipment maintenance efficiency.

[0095] (2) Reduce maintenance costs. By predicting the equipment life in advance, maintenance personnel can carry out targeted maintenance and replacement, avoiding unnecessary maintenance expenses and parts costs, thereby reducing maintenance costs.

[0096] (3) Intelligent solution: This web application combines multiple levels such as data collection, processing and prediction models to provide an intelligent equipment life prediction solution. Users can easily access and use the system through the user interaction layer to achieve intelligent equipment management and maintenance.

[0097] Based on the above technical scheme, the embodiment of the present application provides a neural network-based equipment life prediction Web application and system construction method, the application adopts a distributed platform architecture, and the application includes: a data acquisition layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer connected in sequence; wherein the data acquisition layer is used to collect data related to equipment life from various types of industrial equipment in real time, and transmit the original data to the data processing layer for processing; the data processing layer is used to clean, organize and convert the original data collected by the data acquisition layer, and perform data preprocessing and feature engineering operations to improve data quality and availability; the prediction model layer is used to input the data processed by the data processing layer into the neural network model, and use the neural network model to predict equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict equipment life based on the input data; the application service layer is used to process business logic and provide service interface; the business logic includes algorithm logic, data storage and retrieval, model call and management functions of equipment life prediction; the user interaction layer is used to provide a user interface, allowing users to access the system, view equipment life prediction results by inputting equipment-related data, and perform system configuration operations; the modules of the back-end service and the front-end service are connected through RESTful API for communication.

[0098] The present application relates to a neural network-based equipment remaining life prediction Web application and system construction method, which aims to improve equipment maintenance efficiency, reduce maintenance costs, and provide a scientific basis for equipment management. The application includes five levels: data collection, processing, prediction, application, and interaction. The data collection layer is responsible for collecting equipment operation data, the data processing layer cleans and preprocesses the data, the prediction model layer realizes equipment life prediction through neural networks, and the application service layer integrates data processing and model analysis results, providing model management, life prediction, task scheduling, report generation, and notification push functions. The user interaction layer supports users to upload equipment data through the interface or RESTful API and generates a prediction report containing the remaining life, health status, and maintenance recommendations of the equipment, providing decision support for equipment managers. The application can intelligently manage equipment, optimize service life, reduce failures and downtime, and has broad market application prospects.

[0099] Those skilled in the art can understand that the above-mentioned embodiments are specific examples for implementing the present application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present application. Any person skilled in the art can make their own changes and modifications without departing from the spirit and scope of the present application, so the scope of protection of the present application shall be based on the scope defined in the claims.

Claims

1. A neural network-based equipment life prediction Web application, characterized in that: The application adopts a distributed platform architecture, including: a data collection layer, a data processing layer, a prediction model layer, an application service layer and a user interaction layer connected in sequence; wherein, The data acquisition layer is used to collect data related to equipment life from various industrial equipment in real time, and transmit the original data to the data processing layer for processing; The data processing layer is used to clean, organize and convert the raw data collected by the data collection layer, and perform data preprocessing and feature engineering operations to improve data quality and availability; The prediction model layer is used to input the data processed by the data processing layer into the neural network model, and use the neural network model to predict the equipment life; the neural network model can learn the pattern and trend of equipment life from historical data, and predict the equipment life based on the input data; The application service layer is used to process business logic and provide service interfaces; the business logic includes algorithm logic, data storage and retrieval, model calling and management functions for equipment life prediction; The user interaction layer is used to provide a user interface, allowing the user to access the system, input device-related data, view device life prediction results, and perform system configuration operations; Each module communicates with each other through RESTful API.

2. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: The data acquisition layer includes a data acquisition terminal, an industrial equipment interface and a data upload module; The data acquisition terminal is used to collect equipment operating parameters, which include temperature, pressure, humidity and vibration parameters; The industrial equipment interface is used to connect with various types of industrial equipment; The data upload module is used to upload real-time data and historical data respectively.

3. The neural network-based equipment life prediction Web application according to claim 2, characterized in that: The data upload module supports uploading device data files in CSV and JSON formats, and provides a data analysis portal.

4. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: The data processing layer includes a data cleaning module, a feature extraction module, a data formatting module and an anomaly detection module; The data processing layer uses data cleaning, feature engineering and anomaly detection techniques to process the raw data to improve the accuracy and reliability of the prediction model.

5. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: The prediction model layer includes an artificial neural network deep learning model and a long short-term memory network deep learning model.

6. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: The application service layer includes a model management module, a prediction service module, a task management module and a report generation module; The prediction service module includes an equipment life prediction algorithm module, and the application service layer can support integration with other systems or modules to implement business processes related to equipment management and maintenance.

7. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: The user interaction layer includes a user interface module, a rights management module, a configuration management module, and a prediction result display module; The prediction result display module includes a key performance indicator display module, through which the user can view the device status, access the trained model and view related data.

8. The neural network-based equipment life prediction Web application according to claim 1, characterized in that: Also includes: A physical device layer connected to the data acquisition layer; The physical device layer includes an industrial equipment module, a sensor module, a wireless transmission module and an edge computing module.

9. A method for constructing a neural network-based equipment life prediction Web application system, used for the neural network-based equipment life prediction Web application as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Collect real-time operation data of equipment; Clean, organize, convert, and perform data preprocessing, feature engineering, and anomaly detection on the collected data; A neural network model is used to predict the life of the equipment; the neural network model includes an artificial neural network deep learning model and a long short-term memory network deep learning model; Provide equipment life prediction reports, including equipment remaining life, health status, risk level and maintenance recommendations.

10. The method for constructing a equipment life prediction Web application system based on a neural network according to claim 9, characterized in that: The neural network model is used to predict the equipment life, including: The neural network model learns patterns and trends of equipment life from historical data and makes equipment life predictions based on the input data.

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