Intelligent fault prediction and maintenance system and method

Abstract

The invention discloses an intelligent fault prediction and maintenance system and method, and the method comprises the steps: a sensor information collection module collects equipment operation data and detects a short-circuit signal of a sensor in real time, and a fault prediction and recognition module carries out the analysis according to the short-circuit sequence of the sensor and historical data, obtains a fault region, and outputs potential fault information. The fault early warning and transmission module automatically generates fault early warning and notifies an operator after receiving the potential fault information, and the fault response and scanning module is used for supplementing fault description and uploading fault information by scanning a two-dimensional code of equipment after the operator receives the fault early warning information. The fault integration and distribution module integrates fault information received in the system into fault details and distributes the fault details to special maintenance personnel according to fault types and areas, and the task tracking and feedback module feeds back task completion information to the system in time by scanning a two-dimensional code after the maintenance personnel completes a task. And all tasks can be effectively tracked and managed.

Description

Technical Field

[0001] The present invention relates to a system and method for intelligent fault prediction and maintenance, and belongs to the field of Internet of Things equipment monitoring, intelligent maintenance and automated management. Background Art

[0002] In modern industrial production, the stable operation of equipment is crucial to ensuring product quality, improving production efficiency and protecting 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 monitor the health of equipment remotely and provide early warning of potential problems. However, this often depends on the stability of the sensor itself. When a sensor fails, such as a short circuit, the data it provides may become unreliable or completely ineffective, thereby affecting the accuracy of fault diagnosis of the entire system.

[0004] In addition, traditional fault warning systems usually require operators to invest a lot of time and energy to investigate the source of the fault. From discovering the problem to issuing maintenance tasks and finally completing the repair, the communication and coordination in the entire process mostly rely on manual intervention or through complex operating 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] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a system and method for intelligent fault prediction and maintenance to solve the problem that the existing fault warning system takes a lot of time to troubleshoot faults and cannot accurately warn when a sensor fails.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: a system and method for intelligent fault prediction and maintenance, the system comprising: The sensor information acquisition module is used to collect the operating data of the target device and detect the short-circuit signal of the sensor in real time, and output the collected device operating data and the detected short-circuit signal of the sensor; A fault prediction and identification module is used to analyze the sensor short circuit sequence and historical data after receiving the sensor short circuit signal output by the sensor information acquisition module, obtain the area prone to fault, and output potential fault information; The fault warning and transmission module is used to automatically generate fault warning information and notify the operator through the system interface or SMS after receiving the potential fault information output by the fault prediction and identification module; The fault response and scanning module is used for the operator to check the fault warning information one by one after receiving it. By scanning the QR code on the target device, the operator can complete the fault description and send it to the system. The fault integration and distribution module 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 equipment; The task tracking and feedback module 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 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 is not only used by maintenance personnel to feedback task information, but also timely updates the task status and sorts it according to task priority to improve overall work efficiency.

[0007] Furthermore, the operating data collected in real time in the sensor information collection module includes temperature, pressure, vibration, current and voltage.

[0008] Furthermore, the prediction algorithm adopted by the fault prediction and identification module is based on the timing analysis of sensor data and short-circuit signals. The algorithm adopts the methods of sensor short-circuit signal recognition, rule-based fault diagnosis and fault pattern recognition, which is used to output potential fault information.

[0009] Furthermore, the potential fault information output by the fault prediction and identification module includes the predicted fault type, predicted fault area, probability of fault occurrence and operation suggestions, so that the operator can check the equipment first.

[0010] Furthermore, 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.

[0011] Furthermore, the equipment basic information includes 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 equipment real-time operation status, equipment operation parameters and equipment production data; the task management information includes maintenance task number, maintenance task information, maintenance personnel information and task status and priority.

[0012] Furthermore, the fault information that the operator in the fault response and scanning module needs to supplement after scanning the QR code 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.

[0013] Furthermore, 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. A method for intelligent fault prediction and maintenance, the method comprising the following steps: S1. When the equipment starts to run, the sensor information acquisition module 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; S2. Based on the sequence of sensor short circuits and historical data, the fault prediction and identification module analyzes the data and proposes multiple fault prediction possibilities, and then outputs different potential fault information according to the size of the possibility; S3, after receiving the potential fault information, the fault warning and transmission module will sort out and summarize 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 allocation module 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, the task information will be promptly fed back to the system by scanning the QR code. At this time, the task tracking and feedback module records the completion status of the maintenance task, updates the task status and priority, to ensure that the task is effectively tracked and managed.

[0014] The beneficial effects of the present invention are: When the user uses this kind of intelligent fault prediction and maintenance system, the sensors in the system can timely collect the operation data of the equipment and monitor the short-circuit signal of the sensor. Then the fault prediction and identification module will combine the sequence of sensor short circuits and the historical data of the equipment to perform time series analysis and propose multiple possibilities of fault prediction, and then output different potential fault information according to the size of the possibility. The system will organize and summarize the potential fault information to form fault warning information to notify the operator. After receiving the information, the operator will check one by one and can use a long-distance QR code scanning device to supplement the complete fault description in the interface and upload it to the system. The fault integration and allocation module in the system will integrate the fault description uploaded by the operator into fault details, and then assign it to special maintenance personnel according to the type and area of ​​the fault. After receiving the corresponding task, the maintenance personnel will perform maintenance and feedback in time. The present invention combines sensor short-circuit information, intelligent task allocation and QR code scanning technology to achieve rapid warning and intelligent task management of equipment failures, improve fault handling efficiency, reduce production downtime, and improve the overall operation and maintenance efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 A schematic diagram of the structure of an intelligent fault prediction and maintenance system of the present invention; Figure 2 A method schematic diagram of a prediction algorithm of a fault prediction and identification module of the present invention; Figure 3 A schematic diagram of the content of potential fault information output by the fault prediction and identification module of the present invention; Figure 4 A schematic diagram of the contents contained in the QR code of the device of the present invention; Figure 5 A schematic diagram of the steps of a method for intelligent fault prediction and maintenance of the present invention.

[0016] The reference numerals are: 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 allocation module; 6. task tracking and feedback module. DETAILED DESCRIPTION

[0017] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0018] Figure 1 This is a schematic diagram of the structure of an intelligent fault prediction and maintenance system of the present invention. Figure 2A schematic diagram of a method for predicting an algorithm of a fault prediction and identification module of the present invention; Figure 3 A schematic diagram of the content of potential fault information output by the fault prediction and identification module of the present invention.

[0019] like Figure 1 , Figure 2 and Figure 3 As shown, the present invention provides a system technical solution for intelligent fault prediction and maintenance: the system includes: The sensor information acquisition module 1 is used 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; The fault prediction and identification module 2 is used to analyze the sensor short circuit sequence and historical data after receiving the sensor short circuit signal output by the sensor information acquisition module 1, obtain the area prone to fault, and output potential fault information; Fault warning and transmission module 3, used to automatically generate fault warning information and notify the operator through the system interface or SMS after receiving the potential fault information output by the fault prediction and identification module 2; The fault response and scanning module 4 is used for the operator to check the fault warning information one by one 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; The fault integration and distribution module 5 is used to integrate the fault information received in the system into fault details and distribute them to special maintenance personnel according to the type of fault details and the area of ​​the target equipment; Task tracking and feedback module 6, used to feed back task completion information filled in by maintenance personnel by scanning a 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 to timely update the task status and sort it according to task priority, so as to improve the overall work efficiency.

[0020] In order to achieve accurate monitoring of the equipment, the operating data collected in real time in the sensor information collection module 1 include temperature, pressure, vibration, current and voltage.

[0021] In order to achieve accurate fault prediction, 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 algorithm adopts the method of sensor short-circuit signal recognition, rule-based fault diagnosis and fault pattern recognition, which is used to output potential fault information.

[0022] In order to facilitate the operator to check, 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 that the operator can check the equipment first.

[0023] Figure 4 This is a schematic diagram of the contents contained in the QR code of the device of the present invention, refer to Figure 4 As shown, in order to quickly locate different devices, the present invention also provides an intelligent fault prediction and maintenance system, each device is equipped with a unique QR code, and the QR code contains basic device information, device maintenance information, device operation data and task management information.

[0024] In order to more accurately realize fault prediction and task allocation, the equipment basic information includes 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 maintenance task number, maintenance task information, maintenance personnel information and task status and priority.

[0025] In order to facilitate maintenance personnel, the fault information that the operator in the fault response and scanning module 4 needs to supplement after scanning the QR code includes the fault manifestation and cause, the scope of the fault impact and the urgency of the fault, so that the system can assign tasks to maintenance personnel.

[0026] In order to facilitate the staff to scan the QR code, the operators and maintenance personnel use long-distance mobile QR code scanning equipment to facilitate scanning of equipment that cannot be accessed at close range. Figure 5 This is a schematic diagram of the steps of a method for intelligent fault prediction and maintenance of the present invention, which is applied to the aforementioned intelligent fault prediction and maintenance system, referring to Figure 5 As shown, in order to achieve accurate and fast fault prediction, the present invention also provides a method for intelligent fault prediction and maintenance, which comprises the following steps: S1, when the equipment starts to run, the sensor information acquisition module 1 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 2; S2, based on the sequence of sensor short circuits and historical data, the fault prediction and identification module 2 analyzes the data and proposes multiple fault prediction possibilities, and then outputs different potential fault information according to the size of the possibility; S3, after receiving the potential fault information, the fault warning and transmission module 3 will sort out and summarize it, and automatically generate fault warning information, and notify 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 allocation 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, the task information will be promptly fed back to the system by scanning the QR code. 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.

[0027] When the user uses this kind of intelligent fault prediction and maintenance system, the sensors in the system can timely collect the operation data of the equipment and monitor the short-circuit signal of the sensor. Then the fault prediction and identification module 2 will combine the sequence of sensor short circuits and the historical data of the equipment to perform time series analysis and propose multiple fault prediction possibilities, and then output different potential fault information according to the size of the possibility. The system will organize and summarize the potential fault information to form fault warning information to notify the operator. After receiving the information, the operator will check one by one and can use a long-distance QR code scanning device to supplement the complete fault description in the interface and upload it to the system. The fault integration and allocation module 5 in the system will integrate the fault description uploaded by the operator into fault details, and then assign it to special maintenance personnel according to the type and area of ​​the fault. After receiving the corresponding task, the maintenance personnel will perform maintenance and feedback in time. The present invention combines sensor short-circuit information, intelligent task allocation and QR code scanning technology to achieve rapid warning and intelligent task management of equipment failures, improve fault handling efficiency, reduce production downtime, and improve the overall operation and maintenance efficiency of the equipment.

[0028] The methods adopted by the fault prediction algorithm of this system are sensor short-circuit signal recognition, rule-based fault diagnosis and fault pattern recognition, that is, sensor short-circuit signal recognition means that when a short circuit or abnormality occurs in the sensor, the system can automatically detect and record the time, type and sequence of the short circuit. The system uses these signals to prioritize the analysis of the fault area related to the sensor; rule-based fault diagnosis means that the system predicts the fault type according to the set rules based on the health status of the sensor and the working parameters of the equipment, thereby reducing the scope of operator investigation; fault pattern recognition is to identify the potential fault mode of the equipment through the joint analysis of multi-sensor data. If multiple sensors fail, the system can prioritize the most likely fault cause and affected area according to the order and law of the fault occurrence.

[0029] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes 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 to timely update the task status and sort the tasks according to their priority, so as to improve the overall work efficiency.

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 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 algorithm adopts the methods of sensor short-circuit signal identification, rule-based fault diagnosis and fault pattern identification, and is used to output potential fault information.

4. The intelligent fault prediction and maintenance system according to claim 3, 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.

5. The intelligent fault prediction and maintenance system according to claim 1, characterized in that: 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.

6. The intelligent fault prediction and maintenance system according to claim 5, 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.

7. 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.

8. 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.

9. 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 8, 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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