A system and method for intelligent fault prediction and maintenance

Abstract

An intelligent fault prediction and maintenance system and method, which includes: a sensor information acquisition module that collects device operation data and real-time detects the short-circuit signal of the sensor; a fault prediction and identification module that analyzes according to the sequence of sensor short circuits and historical data to obtain the fault area and output potential fault information; a fault warning and transmission module that automatically generates a fault warning and notifies the operator after receiving the potential fault information; a fault response and scanning module that, after the operator receives the fault warning information, supplements the fault description and uploads the fault information by scanning the device QR code; a fault integration and distribution module that integrates the fault information received within the system into a fault detail and assigns it to a dedicated maintenance personnel according to the type and area of the fault; a task tracking and feedback module that, after the maintenance personnel complete the task, timely feeds back the task completion information to the system through scanning the QR code, so as to ensure that all tasks are effectively tracked and managed.

Description

Technical Field

[0001] The present invention is a system and method for intelligent fault prediction and maintenance, belonging to the fields of Internet of Things device monitoring, intelligent maintenance, and automation management. Background Art

[0002] In modern industrial production, the stable operation of equipment is crucial for ensuring product quality, improving production efficiency, and guaranteeing the safety of operators.

[0003] Currently, many advanced equipment monitoring systems have begun to rely on sensor networks to collect real-time data for condition monitoring and fault diagnosis. These sensors enable engineers to remotely monitor the health of equipment and early warn of potential problems. However, this often depends on the stability of the sensors themselves. When a sensor fails, such as a short circuit, the data it provides may become unreliable or completely invalid, thus affecting the accuracy of the entire system's fault diagnosis.

[0004] In addition, traditional fault warning systems usually require operators to invest a large amount of time and effort to identify the fault source. From problem discovery to the issuance of repair tasks and finally to the completion of repairs, most of the communication and coordination in the entire process rely on manual intervention or through complex operation interfaces, which not only reduces the speed of responding to faults but also increases the risk of misjudgment to a certain extent. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a system and method for intelligent fault prediction and maintenance to solve the problems that the existing fault warning system takes a lot of time to identify faults and cannot accurately warn when sensors malfunction.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A system and method for intelligent fault prediction and maintenance, the system includes:

[0007] A sensor information acquisition module, configured to collect the operation data of the target device and detect the short-circuit signal of the sensor in real time, and output the collected device operation data and the detected short-circuit signal of the sensor;

[0008] A fault prediction and identification module, configured to analyze according to the sequence of sensor short circuits and historical data after receiving the short-circuit signal of the sensor output by the sensor information acquisition module, and obtain the areas prone to faults, and output potential fault information;

[0009] A fault warning and transmission module, configured to automatically generate a fault warning message and notify the operator through the system interface or text message after receiving the potential fault information output by the fault prediction and identification module;

[0010] A fault response and scanning module, which is used to conduct a check one by one when an operator receives a fault warning message. By scanning the QR code on the target device, the operator supplements a complete fault description and sends the complete fault description to the system interior;

[0011] A fault integration and distribution module, which is used to integrate the fault information received within the system into fault details and dispatch them to specialized maintenance personnel according to the type of the fault details and the area of the target device;

[0012] A task tracking and feedback module, which is used to, after the maintenance personnel complete the task, feedback the information on the completion of the task filled in by the maintenance personnel scanning the QR code to the system;

[0013] Among them, the fault prediction and identification module combines a prediction algorithm and a short - circuit sequence to propose multiple possibilities of fault prediction, and outputs different potential fault information successively according to the magnitudes of the possibilities;

[0014] Among them, the task tracking and feedback module is not only used for the maintenance personnel to feedback task information, but also updates the task status in a timely manner and sorts them according to the task priorities, so as to improve the overall work efficiency.

[0015] Furthermore, the operation data collected in real - time by the sensor information collection module includes temperature, pressure, vibration, current and voltage.

[0016] Furthermore, the prediction algorithm adopted by the fault prediction and identification module is based on the time - series analysis of sensor data and short - circuit signals. The methods adopted by the algorithm are sensor short - circuit signal identification, rule - based fault diagnosis and fault mode identification, which are used to output potential fault information.

[0017] Furthermore, the potential fault information output by the fault prediction and identification module includes the predicted fault type, the predicted fault area, the probability of fault occurrence and operation suggestions, which are convenient for the operator to conduct a preliminary check on the equipment.

[0018] Furthermore, each of the devices is equipped with a unique QR code, and the QR code contains device basic information, device maintenance information, device operation data and task management information.

[0019] Furthermore, the device basic information includes device serial number, device specification, device supplier, device technical parameters and device location. The device maintenance information includes the most recent maintenance date, maintenance record history, device fault record and device calibration information. The device operation data includes the device real - time operation status, device operation parameters and device production data. The task management information includes maintenance task number, maintenance task information, maintenance personnel information and task status and priority.

[0020] Further, the fault information that the operator needs to supplement after scanning the QR code in the fault response and scanning module includes the fault manifestation and cause of generation, explains the scope of the fault impact, and indicates the fault urgency, which is convenient for the system to assign tasks to maintenance personnel.

[0021] Further, the operator and the maintenance personnel use a long-distance mobile QR code scanning device, which is convenient for scanning equipment that cannot be contacted closely.

[0022] A method for intelligent fault prediction and maintenance, the method comprising the following steps:

[0023] S1. When the equipment starts to run, the sensor information acquisition module collects the equipment operation data in real time and detects the short-circuit signal of the sensor, and then transmits the collected signal to the fault prediction and identification module;

[0024] S2. According to the chronological analysis of the sequence and historical data of the sensor short circuit, the fault prediction and identification module analyzes the data in combination and proposes various possibilities of fault prediction, and then outputs different potential fault information according to the size of the possibility;

[0025] S3. After receiving the potential fault information, the fault warning and transmission module will sort and summarize it, and automatically generate a fault warning information, which is notified to the operator through the system interface or text message, etc.;

[0026] S4. After receiving the fault warning information, the operator uses a long-distance mobile QR code scanning device to scan the QR code on the equipment, supplement a complete and detailed fault description, and upload the fault information to the system;

[0027] S5. The fault integration and assignment module in the system integrates the received fault information into fault details, and distributes it to specialized maintenance personnel according to the type and area of the fault;

[0028] S6. After receiving the task, the maintenance personnel carry out maintenance work according to the fault details. After the task is completed, the task information is fed back to the system in time by scanning the QR code. At this time, the task tracking and feedback module records the completion of the maintenance task, updates the task status and priority to ensure that the task is effectively tracked and managed.

[0029] The beneficial effects of the present invention are:

[0030] When a user uses this intelligent fault prediction and maintenance system, the sensors in the system can collect the operation data of the equipment in a timely manner and monitor the short - circuit signals of the sensors. Subsequently, the fault prediction and identification module will conduct a time - series analysis by combining the sequence of sensor short - circuits and the historical data of the equipment and propose various possibilities of fault prediction. Then, according to the magnitude of the possibilities, different potential fault information will be output. The system will organize and summarize the potential fault information to form fault warning information and notify the operator. After receiving this information, the operator will conduct inspections one by one. The operator can use a long - distance QR - code scanning device to supplement a complete fault description in the interface and then upload it to the system. The fault integration and assignment module in the system will integrate the fault description uploaded by the operator into fault details and then assign them to specialized maintenance personnel according to the type and area of the fault. After receiving the corresponding tasks, the maintenance personnel will carry out repairs in a timely manner and give feedback. Through the combination of sensor short - circuit information, intelligent task assignment, and QR - code scanning technology, the present invention realizes the rapid warning of equipment faults and intelligent task management, improves the fault handling efficiency, reduces the production downtime, and enhances the overall operation and maintenance efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non - restrictive embodiments with reference to the accompanying drawings:

[0032] Figure 1 It is a schematic structural diagram of an intelligent fault prediction and maintenance system of the present invention;

[0033] Figure 2 It is a schematic method diagram of the prediction algorithm of the fault prediction and identification module of the present invention;

[0034] Figure 3 It is a schematic content diagram of the potential fault information output by the fault prediction and identification module of the present invention;

[0035] Figure 4 It is a schematic content diagram of the content contained in the QR - code of the equipment of the present invention;

[0036] Figure 5 It is a schematic step diagram of an intelligent fault prediction and maintenance method of the present invention.

[0037] The reference numerals are respectively: 1, sensor information acquisition module; 2, fault prediction and identification module; 3, fault warning and transmission module; 4, fault response and scanning module; 5, fault integration and assignment module; 6, task tracking and feedback module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the technical means, creative features, achieved purposes, and functions of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0039] Figure 1 Schematic diagram of the structure of a system for intelligent fault prediction and maintenance according to the present invention, Figure 2 Schematic diagram of the prediction algorithm of the fault prediction and identification module according to the present invention, Figure 3 Schematic diagram of the content of the potential fault information output by the fault prediction and identification module according to the present invention.

[0040] As Figure 1 、 Figure 2 and Figure 3 shown, the present invention provides a technical solution for a system for intelligent fault prediction and maintenance: The system includes:

[0041] A sensor information acquisition module 1, configured to collect operation data of a target device and detect a short-circuit signal of a sensor in real time, and output the collected device operation data and the detected short-circuit signal of the sensor;

[0042] A fault prediction and identification module 2, configured to analyze according to the sequence of sensor short circuits and historical data after receiving the short-circuit signal of the sensor output by the sensor information acquisition module 1, and obtain areas prone to faults, and output potential fault information;

[0043] A fault warning and transmission module 3, configured to automatically generate a fault warning message and notify an operator through a system interface or a short message after receiving the potential fault information output by the fault prediction and identification module 2;

[0044] A fault response and scanning module 4, configured to check one by one after the operator receives the fault warning message, and by scanning a two-dimensional code on the target device, the operator supplements a complete fault description and sends the complete fault description to the system internal;

[0045] A fault integration and distribution module 5, configured to integrate the fault information received in the system into fault details, and distribute them to specialized maintenance personnel according to the type of the fault details and the area of the target device;

[0046] A task tracking and feedback module 6, configured to feedback the information on the completion of the task filled in by the maintenance personnel by scanning a two-dimensional code to the system after the maintenance personnel complete the task;

[0047] Among them, the fault prediction and identification module 2 combines a prediction algorithm and a short-circuit sequence to propose multiple possibilities for fault prediction, and outputs different potential fault information according to the magnitude of the possibilities;

[0048] Among them, the task tracking and feedback module 6 is not only used for the maintenance personnel to feedback task information, but also updates the task status in a timely manner and sorts according to the task priority, so as to improve the overall work efficiency.

[0049] To achieve precise monitoring of the device, the operation data collected in real time by the sensor information acquisition module 1 includes temperature, pressure, vibration, current, and voltage.

[0050] To achieve accurate fault prediction, the prediction algorithm adopted by the fault prediction and identification module 2 is based on the time series analysis of sensor data and short - circuit signals. The methods used in the algorithm are sensor short - circuit signal identification, rule - based fault diagnosis, and fault mode identification, which are used to output potential fault information.

[0051] To facilitate the troubleshooting by the operator, the potential fault information output by the fault prediction and identification module 2 includes the predicted fault type, the predicted fault area, the probability of fault occurrence, and operation suggestions, which are convenient for the operator to first troubleshoot the device.

[0052] Figure 4 Schematic diagram of the content contained in the two - dimensional code of the device of the present invention. Refer to Figure 4 As shown, to quickly locate different devices, the present invention also provides a system for intelligent fault prediction and maintenance. Each device is equipped with a unique two - dimensional code, and the two - dimensional code contains device basic information, device maintenance information, device operation data, and task management information.

[0053] To more precisely achieve fault prediction and task allocation, the device basic information includes device serial number, device specification, device supplier, device technical parameters, and device location. The device maintenance information includes the most recent maintenance date, maintenance record history, device fault record, and device calibration information. The device operation data includes the real - time operation status of the device, device operation parameters, and device production data. The task management information includes repair task number, repair task information, repair personnel information, and task status and priority.

[0054] To facilitate the repair by the repair personnel, the fault information that the operator needs to supplement after scanning the two - dimensional code in the fault response and scanning module 4 includes the fault manifestation and generation reason, the scope of the fault impact, and the indication of the fault urgency, which is convenient for the system to assign tasks to the repair personnel.

[0055] To facilitate the staff to scan the two - dimensional code, the operator and the repair personnel use a long - distance mobile two - dimensional code scanning device, which is convenient for scanning devices that cannot be contacted closely.

[0056] Figure 5 Schematic diagram of the steps of a method for intelligent fault prediction and maintenance of the present invention. This method is applied to the aforementioned system for intelligent fault prediction and maintenance. Refer to Figure 5 As shown, to achieve precise and fast fault prediction, the present invention also provides a method for intelligent fault prediction and maintenance. The method includes the following steps:

[0057]

[0057] When the device starts to run, the sensor information acquisition module 1 collects the device operation data in real time and detects the short - circuit signal of the sensor, and then transmits the collected signal to the fault prediction and identification module 2;

[0058] S2. According to the chronological analysis of the sequence of sensor short - circuits and historical data, the fault prediction and identification module 2 analyzes the combined data and proposes various possibilities of fault prediction, and then outputs different potential fault information according to the magnitude of the possibility;

[0059]

[0058] After receiving the potential fault information, the fault warning and transmission module 3 sorts and summarizes it, and automatically generates a fault warning message, which is notified to the operator through the system interface or text message, etc.;

[0060] S4. After receiving the fault warning message, the operator uses a long - distance mobile QR - code scanning device to scan the QR - code on the device, supplement a complete detailed fault description, and upload the fault information to the system;

[0061]

[0059] S5. The fault integration and distribution module 5 in the system integrates the received fault information into fault details, and dispatches them to specialized maintenance personnel according to the type and area of the fault;

[0062] S6. After receiving the task, the maintenance personnel carry out maintenance work according to the fault details. After the task is completed, the task information is fed back to the system in a timely manner by scanning the QR - code. At this time, the task tracking and feedback module 6 records the completion of the maintenance task, updates the task status and priority to ensure effective tracking and management of the task.

[0063] When the user uses this intelligent fault prediction and maintenance system, the sensors in the system can collect the operation data of the device in a timely manner and monitor the short - circuit signal of the sensors. Subsequently, the fault prediction and identification module 2 will conduct chronological analysis by combining the sequence of sensor short - circuits and the historical data of the device and propose various possibilities of fault prediction, and then output different potential fault information according to the magnitude of the possibility. The system will sort and summarize the potential fault information to form a fault warning message and notify the operator. After receiving the information, the operator will conduct a check one by one. The operator can use a long - distance QR - code scanning device to supplement a complete fault description in the interface and then upload it to the system. The fault integration and distribution module 5 in the system will integrate the fault description uploaded by the operator into fault details, and then dispatch them to specialized maintenance personnel according to the type and area of the fault. After receiving the corresponding task, the maintenance personnel will carry out maintenance in a timely manner and give feedback. Through the combination of sensor short - circuit information, intelligent task allocation and QR - code scanning technology, the present invention realizes the rapid warning of equipment faults and intelligent task management, improves the fault handling efficiency, reduces the production downtime, and enhances the overall operation and maintenance efficiency of the equipment.

[0064] The methods adopted by the system fault prediction algorithm are respectively sensor short - circuit signal recognition, rule - based fault diagnosis, and fault mode recognition. That is, sensor short - circuit signal recognition means that when a sensor has a short - circuit or abnormal event, the system can automatically detect and record the time, type, and sequence of the short - circuit. Using these signals, the system can preferentially analyze the fault area related to the sensor; rule - based fault diagnosis means that by combining the health status of the sensor and the working parameters of the device, the system predicts the type of fault according to the set rules, reducing the troubleshooting scope of the operator; fault mode recognition is to identify the potential fault modes of the device through the joint analysis of multi - sensor data. If multiple sensors fail, the system can, according to the order and pattern of the faults, preferentially propose the most likely fault causes and affected areas.

[0065] The above has shown and described the basic principles, main features, and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above - mentioned exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0066] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An intelligent fault prediction and maintenance system, characterized in that: The system comprises: A sensor information acquisition module (1) is used to acquire operating data of a target device and detect a short-circuit signal of the sensor in real time, and output the acquired device operating data and the detected short-circuit signal of the sensor; A fault prediction and identification module (2) is used to, after receiving the short-circuit signal of the sensor output by the sensor information acquisition module (1), analyze the sensor short-circuit sequence and historical data, obtain areas prone to faults, and output potential fault information; A fault warning and transmission module (3) is used to automatically generate fault warning information and notify an operator through a system interface or text message after receiving the potential fault information output by the fault prediction and identification module (2); A fault response and scanning module (4) is used for the operator to check each fault warning message after receiving it. By scanning the QR code on the target device, the operator can complete the fault description and send the complete fault description to the system. A fault integration and distribution module (5) is used to integrate the fault information received in the system into fault details and distribute them to specialized maintenance personnel according to the type of fault details and the area of ​​the target device; The task tracking and feedback module (6) is used to feed back the task completion information filled in by the maintenance personnel by scanning the QR code to the system after the maintenance personnel complete the task; Among them, the fault prediction and identification module (2) combines the prediction algorithm and the short circuit sequence to propose multiple fault prediction possibilities, and outputs different potential fault information in sequence according to the size of the possibility; Among them, the task tracking and feedback module (6) is not only used for maintenance personnel to feedback task information, but also timely updates the task status and sorts the tasks according to their priority, so as to improve the overall work efficiency; The prediction algorithm adopted by the fault prediction and identification module (2) is based on the timing analysis of sensor data and short-circuit signals. The prediction algorithm adopts the methods of sensor short-circuit signal recognition, rule-based fault diagnosis and fault pattern recognition, and is used to output potential fault information. Each of the devices is equipped with a unique QR code, which contains basic device information, device maintenance information, device operation data and task management information.

2. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: The operating data collected in real time in the sensor information collection module (1) includes temperature, pressure, vibration, current and voltage.

3. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: The potential fault information output by the fault prediction and identification module (2) includes the predicted fault type, predicted fault area, probability of fault occurrence and operation suggestions, so as to facilitate operators to check the equipment first.

4. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: The equipment basic information includes the equipment serial number, equipment specifications, equipment supplier, equipment technical parameters and equipment location; the equipment maintenance information includes the most recent maintenance date, maintenance record history, equipment failure record and equipment calibration information; the equipment operation data includes the real-time operation status of the equipment, equipment operation parameters and equipment production data; the task management information includes the maintenance task number, maintenance task information, maintenance personnel information and task status and priority.

5. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: The fault information that the operator needs to supplement after scanning the QR code in the fault response and scanning module (4) includes the fault manifestation and cause, the scope of the fault impact and the urgency of the fault, so as to facilitate the system to assign tasks to maintenance personnel.

6. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: The operators and maintenance personnel use long-distance mobile two-dimensional code scanning equipment to facilitate scanning of equipment that cannot be accessed at close range.

7. A method for intelligent fault prediction and maintenance, applicable to an intelligent fault prediction and maintenance system according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: S1, when the equipment starts to operate, the sensor information acquisition module (1) collects equipment operation data in real time and detects the short-circuit signal of the sensor, and then transmits the collected signal to the fault prediction and identification module (2); S2, based on the sequence of sensor short circuits and historical data, the fault prediction and identification module (2) combines the data for analysis and proposes multiple fault prediction possibilities, and then outputs different potential fault information according to the magnitude of the possibility; S3, the fault warning and transmission module (3) will sort out and summarize the potential fault information after receiving it, and automatically generate fault warning information, which will be notified to the operator through the system interface or SMS; S4. After receiving the fault warning information, the operator uses a long-distance mobile QR code scanning device to scan the QR code on the equipment, completes the detailed fault description, and uploads the fault information to the system; S5, the fault integration and distribution module (5) in the system integrates the received fault information into fault details and distributes them to specialized maintenance personnel according to the type and area of ​​the fault; S6. After receiving the task, the maintenance personnel will perform maintenance work according to the fault details. When the task is completed, they will scan the QR code and promptly feedback the task information to the system. At this time, the task tracking and feedback module (6) records the completion status of the maintenance task, updates the task status and priority, to ensure that the task is effectively tracked and managed.

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