Detection and early warning system for fabric production equipment

Abstract

The invention discloses a detection and early warning system for fabric production equipment, and relates to the technical field of mechanical automation and intellectualization. The system comprises the following components: a multi-parameter real-time monitoring module, a big data processing and analysis platform, an intelligent predictive maintenance module, an automatic maintenance plan generation and reminding system and a visual monitoring and reporting system. Through organic combination of the multi-parameter real-time monitoring module, the big data processing and analysis platform and the intelligent predictive maintenance module, comprehensive, real-time and accurate monitoring of the running state of the fabric production equipment is realized, the system can timely discover abnormal modes and potential faults in the running process of the equipment, the occurrence of equipment faults is effectively prevented, and the working efficiency is improved. Therefore, the operation stability and reliability of the equipment are remarkably improved, and the mode of early warning and preventive maintenance in advance not only reduces the production stagnation time caused by equipment faults, but also reduces the maintenance cost and production loss caused by the equipment faults.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical automation and intelligentization, and in particular to a fabric production equipment detection and early warning system. Background Art

[0002] With the rapid development of automation and intelligent technology in textile machinery, the operating efficiency and stability of fabric production equipment are crucial to the production benefits of textile enterprises. With the increasing complexity of equipment and the expansion of production scale, traditional equipment maintenance methods can no longer meet the needs of modern textile enterprises.

[0003] Traditional technology still has many shortcomings in practical applications. It is difficult to input real-time operation data into the optimized prediction model and calculate the prediction results of potential failure points. Therefore, it is particularly important to develop a fabric production equipment detection and early warning system. Summary of the invention

[0004] In order to make up for the shortcomings of the prior art, the purpose of the present invention is to provide a fabric production equipment detection and early warning system, which realizes comprehensive monitoring, accurate prediction and timely maintenance of fabric production equipment through the organic combination of a multi-parameter real-time monitoring module, a big data processing and analysis platform, an intelligent predictive maintenance module, an automatic maintenance plan generation and reminder system, and a visual monitoring and reporting system, thereby providing a strong guarantee for the production efficiency of the enterprise.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a fabric production equipment detection and early warning system, which includes a multi-parameter real-time monitoring module, a big data processing and analysis platform, an intelligent predictive maintenance module, an automatic maintenance plan generation and reminder system, and a visual monitoring and reporting system;

[0006] The multi-parameter real-time monitoring module: install vibration, temperature, current, pressure and other types of high-precision sensors at key parts of fabric production equipment to collect equipment operation data in real time and form a comprehensive equipment operation parameter library;

[0007] The big data processing and analysis platform: builds a cloud computing-based data processing center to clean, integrate and store the collected massive data, uses big data analysis tools to conduct in-depth data mining, and identifies abnormal patterns and trends in equipment operation;

[0008] The intelligent predictive maintenance module: collects multi-dimensional operation data through sensors, extracts features related to equipment status after cleaning and standardization, selects key feature sets, establishes an initial prediction model based on historical fault and operation data, and after training and cross-validation optimization, it iteratively optimizes the model in combination with new data, dynamically adjusts the structure, inputs real-time data into the optimization model, calculates the prediction results of potential fault points, evaluates the time and probability of fault occurrence, sets warning thresholds, and automatically sends warning information to mobile or PC operators when the warning conditions are met. The operators feedback data after maintenance, optimize the model, and form a closed loop;

[0009] The automatic maintenance plan generation and reminder system: Based on the prediction results, the system automatically generates a personalized maintenance plan and sends early warning information and maintenance suggestions to operators through a mobile or PC interface. Operators can arrange preventive maintenance work in a timely manner according to the prompts, effectively avoiding production stagnation caused by equipment failure;

[0010] The visual monitoring and reporting system provides an intuitive visual interface for equipment operation status, allowing managers to view equipment operation status, fault prediction results and maintenance execution status in real time. At the same time, it regularly generates equipment health reports and maintenance effect evaluations to provide strong support for production decisions.

[0011] Furthermore, the high-precision sensors in the multi-parameter real-time monitoring module are used to collect equipment operation data to build a parameter library.

[0012] Furthermore, the cloud computing data processing center of the big data processing and analysis platform is used to clean, integrate and store massive data and mine abnormal patterns and trends.

[0013] Furthermore, the intelligent predictive maintenance module establishes an initial prediction model based on historical fault data and equipment operation data: Among them, σ is the activation function, w i For feature X i The weight coefficient of , b is the bias term.

[0014] Furthermore, the intelligent predictive maintenance module inputs the real-time operation data into the optimized prediction model to calculate the prediction results of potential failure points: in, It is a novel activation function. The value range of P is between [0, 1], which represents the probability of potential failure. When P is close to 0, it means that the possibility of device failure is very low. When P is close to 1, it means that the possibility of device failure is very high. B is the bias term.

[0015] Furthermore, when the intelligent predictive maintenance module predicts a potential failure and meets the warning conditions, it automatically generates warning information: Among them, when W = 1, it means that the warning conditions are met and the warning information is automatically generated. When W = 0, it means that the warning conditions are not met. The time impact factor F t It can be dynamically adjusted according to the passage of time and the length of time the device is used. t =1+α×t, where α is a parameter determined based on device characteristics and historical data, and t is the interval between the current time and the last detection time. The data stability factor S can be determined by calculating the ratio of the standard deviation of real-time data to the mean. Where σ is the standard deviation of real-time data and μ is the mean. When the data stability is poor, S is large, which will affect the judgment of early warning.

[0016] Furthermore, the automatic maintenance plan generation and reminder system automatically generates a personalized maintenance plan.

[0017] Furthermore, managers of the visual monitoring and reporting system can view equipment operating conditions, fault prediction results, and maintenance execution status in real time, and regularly generate equipment health reports and maintenance effect evaluations.

[0018] Compared with the existing technology, a fabric production equipment detection and early warning system has the following beneficial effects:

[0019] 1. The present invention realizes comprehensive, real-time and accurate monitoring of the operating status of fabric production equipment through the organic combination of a multi-parameter real-time monitoring module, a big data processing and analysis platform and an intelligent predictive maintenance module. The system can timely discover potential faults in equipment operation and effectively prevent the occurrence of equipment failures, thereby significantly improving the operating stability and reliability of the equipment. This early warning and preventive maintenance method not only reduces the production downtime caused by equipment failure, but also reduces the maintenance cost and production loss caused by equipment failure, providing a strong guarantee for the production efficiency of textile enterprises.

[0020] 2. The present invention further improves the intelligence level and efficiency of equipment maintenance by introducing an automatic maintenance plan generation and reminder system and a visual monitoring and reporting system. The system can automatically generate personalized maintenance plans based on prediction results, and send early warning information and maintenance suggestions to operators through mobile or PC interfaces, so that operators can respond quickly and arrange preventive maintenance work. At the same time, the intuitive equipment operation status interface and regularly generated equipment health reports and maintenance effect evaluations provided by the visual monitoring and reporting system provide managers with comprehensive equipment maintenance information, which helps them better understand the equipment status, optimize production decisions, and further improve the company's production efficiency and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 An operation flow chart of a fabric production equipment detection and early warning system. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Embodiment 1

[0025] Determine which parts of the fabric production equipment are the key parts to be monitored. These parts are usually vulnerable points, important working points or parts that may affect the overall operation of the equipment, including but not limited to the structural characteristics of the equipment, the stress conditions during operation, temperature changes, and the stability of current and voltage. Install vibration, temperature, current, and pressure sensors of various types at the selected key parts. These sensors should be able to capture various parameters of the equipment in real time and accurately. They are used to monitor the vibration of the equipment, identify vibration anomalies, and prevent equipment damage caused by excessive vibration. They measure the temperature of the equipment during operation to prevent performance degradation or failure caused by overheating of the equipment. They monitor the current of the equipment circuit, detect current anomalies in time, and prevent circuit failures. They measure the pressure inside or outside the equipment to ensure that the equipment operates within the appropriate pressure range. They ensure that the sensors are firmly installed, not affected by the external environment, and can accurately reflect the actual conditions of the monitoring points.

[0026] The sensor collects vibration, temperature, current, and pressure data during the operation of the equipment in real time, and transmits the data to the system for processing in real time. The data can be transmitted by wire or wirelessly to ensure the real-time and accuracy of the data. The collected data is initially processed to form a comprehensive equipment operation parameter library to remove noise, outliers, and incomplete data to ensure data quality; the data is standardized so that data of different dimensions can be compared and analyzed on the same scale, and the processed data is stored in the database to facilitate subsequent data analysis and processing. The system monitors the real-time collected data in real time and determines whether the equipment is operating normally by setting thresholds. Once abnormal data is found, the alarm mechanism is immediately triggered and a notification is sent to the operator through the mobile or PC interface to ensure that the operator can understand the equipment status in a timely manner and take corresponding measures. The operator performs preventive maintenance tasks based on the early warning information and records the maintenance results. The equipment operation data after maintenance is fed back to the system for further verification and optimization of the prediction model, forming a closed-loop maintenance and feedback mechanism, and continuously improving the efficiency and accuracy of predictive maintenance.

[0027] Build a data processing center based on cloud computing. This center needs to have strong computing and storage capabilities to support the processing and analysis of massive data and ensure the security of the data processing center. The equipment operation data collected by the multi-parameter real-time monitoring module is transmitted to the data processing center. The data transmission protocol is used to ensure the real-time and accuracy of the data. Before the data enters the processing center, preliminary data verification is carried out to clean the collected massive data to remove noise, outliers and incomplete data. Data cleaning algorithms and tools are used to clean and pre-process the data. The cleaned data is quality checked to ensure the accuracy and availability of the data.

[0028] Integrate the cleaned data and store it according to a certain data structure. Select a database system, design a reasonable data table structure, store the cleaned data, create indexes for the data, improve the efficiency of data query, use big data analysis tools to deeply mine the stored data, identify abnormal patterns and trends in equipment operation, select big data analysis tools, process and analyze the data, use data analysis algorithms to deeply mine the data, identify abnormal patterns and trends in equipment operation based on the results of data analysis, use machine learning algorithms to classify and predict the operating status of the equipment, identify abnormal patterns, and verify the identified abnormal patterns to ensure the accuracy and reliability of the results.

[0029] Through the sensors deployed on the fabric production equipment, multi-dimensional operation data of vibration, temperature, current, and pressure are collected in real time. The collected raw data is cleaned to remove noise, outliers, and incomplete data to ensure data quality. The cleaned data is standardized so that data of different dimensions can be compared and analyzed on the same scale. Features related to the equipment status are extracted from the preprocessed data. The feature selection technology is used to screen out the most valuable feature set for fault prediction. The initial prediction model is established based on historical fault data and equipment operation data. Among them, σ is the activation function, w i For feature X i The weight coefficient is b, and b is the bias term. The extracted feature set is divided into a training set and a test set. The model is trained using the training set data. The model performance is evaluated through the cross-validation method. The model parameters are adjusted to optimize the prediction accuracy. According to the performance of the model on the test set, the model is iteratively optimized in combination with domain knowledge. The newly collected equipment operation data is monitored in real time.

[0030] Use new data to incrementally learn the model to adapt to changes in equipment status. Dynamically adjust the model structure based on the operating characteristics and historical fault data of different equipment to improve the pertinence and accuracy of predictions. Input real-time operating data into the optimized prediction model to calculate the prediction results of potential fault points. in, It is a novel activation function. The value range of P is between [0, 1], which represents the probability of potential failure. When P is close to 0, it means that the probability of device failure is very low. When P is close to 1, it means that the probability of device failure is very high. B is the bias term;

[0031] According to the prediction results, the time and probability of failure are evaluated, and the warning threshold is set. When a potential failure is predicted and the warning conditions are met, the warning information is automatically generated. Among them, when W = 1, it means that the warning conditions are met and the warning information is automatically generated. When W = 0, it means that the warning conditions are not met. The time impact factor F t It can be dynamically adjusted according to the passage of time and the length of time the device is used. t =1+α×t, where α is a parameter determined based on device characteristics and historical data, and t is the interval between the current time and the last detection time. The data stability factor S can be determined by calculating the ratio of the standard deviation of real-time data to the mean. Where σ is the standard deviation of real-time data, and μ is the mean. When the data stability is poor, S is large, which will affect the judgment of the early warning and send notifications to the operator through the mobile or PC interface. The operator performs preventive maintenance tasks according to the early warning information, records the maintenance results, and feeds back the equipment operation data after maintenance to the system for further verification and optimization of the prediction model, forming a closed-loop maintenance and feedback mechanism, and continuously improving the efficiency and accuracy of predictive maintenance.

[0032] Receive prediction results from the intelligent predictive maintenance module, including the location of potential fault points, predicted time and probability of fault occurrence, and obtain prediction result data in real time through API interface or message queue. Based on the prediction results, the system automatically generates a personalized maintenance plan. The plan should include the specific content of maintenance, required materials, estimated time consumption, and priority information. The algorithm is used to intelligently generate maintenance plans based on equipment type, historical maintenance records, and current production demand factors. Based on the maintenance plan, the system automatically generates early warning information and maintenance suggestions. The early warning information should clearly inform the operator of the risks and possible impacts of potential faults, and the maintenance suggestions should specifically guide the operator on how to perform preventive maintenance;

[0033] Through template engines or natural language processing technology, the maintenance plan is converted into easy-to-understand warning information and maintenance suggestions. Warning information and maintenance suggestions are sent to operators through mobile or PC interfaces. Notification methods include but are not limited to SMS, email, and APP push. Third-party notification services are integrated to ensure that notifications can be delivered to operators in a timely and accurate manner. After receiving the warning information, the operator arranges preventive maintenance work in a timely manner according to the maintenance suggestions. A convenient feedback mechanism should be provided to allow operators to record maintenance results. An operation interface should be provided on the mobile or PC interface to allow operators to view warning information, perform maintenance tasks and record maintenance results. Operators feed back the equipment operation data after maintenance to the system. The system uses these data to further verify and optimize the prediction model to form a closed-loop maintenance and feedback mechanism.

[0034] Through the data interface or API, the equipment operation data after maintenance is transmitted to the system in real time, and the predictive model is incrementally learned and optimized using these data. The system should also consider the coordination between maintenance and production plans to ensure that normal production activities are not interfered with when scheduling maintenance tasks. The system should integrate the production planning management system to intelligently adjust the priority and schedule of maintenance tasks according to the production plan and equipment status.

[0035] Collect the latest equipment operation data, fault prediction results and maintenance execution status from the multi-parameter real-time monitoring module and the intelligent predictive maintenance module, integrate various types of data into a unified data set through data interface or database query for subsequent processing and display, design an intuitive and easy-to-understand equipment operation status visualization interface, including but not limited to real-time data charts, fault prediction trend charts, and maintenance execution status lists, and use the chart library to draw various graphs and charts to ensure that managers can understand the equipment status at a glance;

[0036] The collected equipment operation data is displayed in real time on the visual interface, and the front-end technology is used to dynamically update the data to ensure that the management personnel can view the operation status of the equipment in real time. The fault occurrence time and probability results predicted by the intelligent predictive maintenance module are displayed on the visual interface, and the fault prediction results are displayed in the form of charts or lists. At the same time, the warning threshold setting function is provided so that the management personnel can understand the risk of potential failures. The execution status of equipment maintenance is recorded and displayed. The maintenance records are stored in the database or file system, and the query and display functions are provided on the visual interface so that the management personnel can understand the maintenance history of the equipment.

[0037] According to the preset time period, the system automatically generates equipment health reports and maintenance effect evaluation reports. It uses data processing and report generation tools to integrate equipment operation data, fault prediction results and maintenance execution status into report files, and provides download or viewing functions. Managers analyze the generated reports, evaluate the health status and maintenance effects of the equipment, and feed back the analysis results to relevant departments or personnel. Report analysis tools and feedback channels are provided on the visual interface so that managers can communicate and handle problems in a timely manner.

[0038] Embodiment 2

[0039] According to the characteristics of different parts of the loom, vibration, temperature, current and pressure sensors are selected to ensure the accuracy of the monitoring data. Technical personnel with professional knowledge are hired to strictly follow the installation specifications to ensure that the sensors are stably and reliably fixed on the equipment. After the installation is completed, the sensors are preliminarily debugged and data verified to ensure that the data can be uploaded to the data processing center normally.

[0040] Advanced data cleaning technology is used to automatically remove invalid, redundant or abnormal data to improve data quality. Big data analysis tools are used to conduct in-depth mining of cleaned data to identify subtle anomalies and potential risks in equipment operation, maintain the real-time nature of the data processing center, and ensure that the latest data can be immediately used for analysis and prediction.

[0041] Build a prediction model based on historical fault data and real-time operation data. Among them, σ is the activation function, wi For feature X i is the weight coefficient, b is the bias term, and the accuracy and robustness of the prediction model are continuously optimized through continuous model training and incremental learning. According to the equipment characteristics and actual needs, the warning threshold is reasonably set to ensure the accuracy and timeliness of the warning information.

[0042] The system automatically generates a personalized maintenance plan based on the specific conditions and prediction results of each device, sends early warning information and maintenance suggestions to operators through multiple channels such as mobile and PC, ensures the instant transmission of information, automatically tracks the execution of the maintenance plan, and provides adjustment suggestions when necessary.

[0043] Design an easy-to-understand operation interface to display real-time monitoring data of equipment operation status, fault prediction results and maintenance plan information, and regularly generate equipment health reports and maintenance effect evaluation reports, covering equipment operation overview, fault warning analysis, and maintenance measures execution effect content. Provide in-depth data analysis tools to help managers gain insight into equipment operation patterns and potential problems, and provide support for scientific decision-making.

[0044] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A fabric production equipment detection and early warning system, characterized in that: The system includes a multi-parameter real-time monitoring module, a big data processing and analysis platform, an intelligent predictive maintenance module, an automatic maintenance plan generation and reminder system, and a visual monitoring and reporting system; The multi-parameter real-time monitoring module: install vibration, temperature, current, pressure and other types of high-precision sensors at key parts of fabric production equipment to collect equipment operation data in real time and form a comprehensive equipment operation parameter library; The big data processing and analysis platform: builds a cloud computing-based data processing center to clean, integrate and store the collected massive data, uses big data analysis tools to conduct in-depth data mining, and identifies abnormal patterns and trends in equipment operation; The intelligent predictive maintenance module: collects multi-dimensional operation data through sensors, extracts features related to equipment status after cleaning and standardization, selects key feature sets, establishes an initial prediction model based on historical fault and operation data, and after training and cross-validation optimization, it iteratively optimizes the model in combination with new data, dynamically adjusts the structure, inputs real-time data into the optimization model, calculates the prediction results of potential fault points, evaluates the time and probability of fault occurrence, sets warning thresholds, and automatically sends warning information to mobile or PC operators when the warning conditions are met. The operators feedback data after maintenance, optimize the model, and form a closed loop; The automatic maintenance plan generation and reminder system: Based on the prediction results, the system automatically generates a personalized maintenance plan and sends early warning information and maintenance suggestions to operators through a mobile or PC interface. Operators can arrange preventive maintenance work in a timely manner according to the prompts, effectively avoiding production stagnation caused by equipment failure; The visual monitoring and reporting system provides an intuitive visual interface for equipment operation status, allowing managers to view equipment operation status, fault prediction results and maintenance execution status in real time. At the same time, it regularly generates equipment health reports and maintenance effect evaluations to provide strong support for production decisions.

2. A fabric production equipment detection and early warning system according to claim 1, characterized in that: The high-precision sensors in the multi-parameter real-time monitoring module are used to collect equipment operation data to build a parameter library.

3. A fabric production equipment detection and early warning system according to claim 1, characterized in that: The cloud computing data processing center of the big data processing and analysis platform is used to clean, integrate and store massive data and mine abnormal patterns and trends.

4. A fabric production equipment detection and early warning system according to claim 1, characterized in that: The intelligent predictive maintenance module establishes an initial prediction model based on historical fault data and equipment operation data: Among them, σ is the activation function, w i For feature X i The weight coefficient of is, and b is the bias term.

5. According to claim 1, a fabric production equipment detection and early warning system is characterized in that: The intelligent predictive maintenance module inputs real-time operation data into the optimized prediction model to calculate the prediction results of potential failure points: in, It is a novel activation function. The value range of P is between [0, 1], which represents the probability of potential failure. When P is close to 0, it means that the possibility of device failure is very low. When P is close to 1, it means that the possibility of device failure is very high. B is the bias term.

6. The fabric production equipment detection and early warning system according to claim 1, characterized in that: The intelligent predictive maintenance module automatically generates warning information when a potential fault is predicted and the warning conditions are met: Among them, when W = 1, it means that the warning conditions are met and the warning information is automatically generated. When W = 0, it means that the warning conditions are not met. The time impact factor F t It can be dynamically adjusted according to the passage of time and the length of time the device is used. t =1+α×t, where α is a parameter determined based on device characteristics and historical data, and t is the interval between the current time and the last detection time. The data stability factor S can be determined by calculating the ratio of the standard deviation of real-time data to the mean. Among them, σ is the standard deviation of real-time data, μ is the mean, and when the data stability is poor, S is large, which will affect the judgment of early warning.

7. The fabric production equipment detection and early warning system according to claim 1 is characterized in that: The automatic maintenance plan generation and reminder system automatically generates a personalized maintenance plan.

8. The fabric production equipment detection and early warning system according to claim 1, characterized in that: The visual monitoring and reporting system managers can view the equipment operating status, fault prediction results and maintenance execution status in real time, and regularly generate equipment health reports and maintenance effect evaluations.

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