Fault early warning method and system based on robot

Abstract

The invention discloses a fault early warning method and system based on a robot, and the system comprises a signal processing module, a signal processed by the signal processing module comes from a sensor in a robot system and an operation data of a control system, and the signal processing module carries out the processing of the operation data of the sensor in the robot system and the operation data of the control system. The state detection module is used for testing and reporting the physical state or performance of a certain component or subsystem; the early warning evaluation module diagnoses and reports the health state of each assigned component or subsystem; the decision module re-plans the next task of the system according to the future health state of the robot system to generate a maintenance scheme of the system; compared with the prior art, the method has the advantages that a large amount of historical data are learned and analyzed, the potential faults of the robot are accurately predicted, and the different causes are found and solved in time in the operation process through statistical process control, so that fault prediction of the system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot fault warning, and specifically to a robot-based fault warning method and system. Background Art

[0002] As the core of automated equipment, the stability and reliability of robots are directly related to production efficiency and product quality. However, due to the complexity of the robot working environment, the continuous progress of technology, and the increasing usage requirements, the robot system faces increasingly severe fault challenges. These faults may not only cause production line stoppages but also trigger safety accidents, resulting in losses to personnel and property. Therefore, researching and implementing an effective intelligent robot fault prediction mechanism is of great significance for improving the reliability, safety, and economic benefits of the robot system.

[0003] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of implication that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the above technical defects and provide a robot-based fault warning method and system that can accurately predict potential robot faults by learning and analyzing a large amount of historical data. At the same time, the research will also explore how to optimize the algorithm to improve the accuracy and efficiency of prediction, and how to apply the prediction results to actual robot maintenance and management; the main role of using statistical process control is pre-detection, discovering abnormal causes during operation and resolving them in a timely manner to achieve fault prediction for the system.

[0005] To solve the above problems, the technical solution of the present invention is a robot-based fault warning method and system, including a robot fault type database, where the robot fault types include hardware faults, software faults, environmental adaptability problems, communication faults, energy management faults, and safety risk problems; A signal processing module, where the signals processed by the signal processing module come from the operation data of sensors and control systems inside the robot system, and the signal processing module processes these data; A status monitoring module, where the status detection module performs tests and reports the physical status or performance of a certain component or subsystem, and the data detected by the status monitoring module is transmitted to the signal processing module; A warning evaluation module, where the warning evaluation module diagnoses and reports the health status of each assigned component or subsystem; this layer generates the remaining available life of a component or subsystem in a given usage state for fault prediction; A decision-making module that, based on the future health status of the robot system, re-plans the next task of the system and generates a maintenance plan for the system; An interaction module that supports point-to-point communication with other levels to facilitate operators to directly obtain the original information of the robot system and control various parts of the system.

[0006] Furthermore, the early warning and assessment module performs fault prediction based on data fusion by combining data from different sources, including sensor data, historical log file data, and expert system data.

[0007] Furthermore, the data processing in the signal processing module includes data cleaning, denoising, and normalization steps to ensure the quality of data by removing duplicate records, filling in missing values, correcting errors and outliers.

[0008] Furthermore, the early warning and assessment module performs fault prediction based on statistical process control, predicts and manages the health of the robot system during operation, evaluates which health state the current system is in during the health degradation process; determines the fault cause of the system that causes the decline in health level, and takes measures for preventive maintenance. Furthermore, the early warning and assessment module needs to evaluate the health state of the current robot system during the health degradation process, and the health state is one of the normal state, the performance degradation state, or the function failure state.

[0009] Furthermore, when the robot system is in the performance degradation state, determine the fault cause of the robot system that causes the decline in health level, and evaluate the degree of deviation of the current health state from its normal state; predict the future health state of the robot system, including studying whether the robot system can normally complete the function requirements within the next task and studying the remaining life of the robot system.

[0010] Furthermore, the statistical process control performs statistical analysis on various parameters of the robot system to understand the operating conditions of the system, collects parameter data related to the running state of the robot in real time, analyzes using the control chart of SPC theory, and judges the distribution of data characteristic points through the judgment criterion of Shewhart to monitor the running state of the robot.

[0011] Furthermore, an electronic device includes at least one processor; and a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor.

[0012] Furthermore, it also includes a non-transitory computer-readable storage medium storing computer instructions.

[0013] Furthermore, a computer program product includes a computer program.

[0014] The advantages of the present invention compared with the existing technologies are as follows: Through learning and analyzing a large amount of historical data, the present invention realizes accurate prediction of potential faults of robots; meanwhile, the algorithm is optimized to improve the accuracy and efficiency of prediction, and how to apply the prediction results to actual robot maintenance and management; the main function of adopting statistical process control is pre-detection, finding abnormal causes during operation and solving them in time to realize fault prediction of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system framework diagram of the present invention.

[0016] Figure 2 is the robot fault classification diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the content of the present invention be more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0018] A fault warning system based on a robot includes: A robot fault type database, where the robot fault types include hardware faults, software faults, environmental adaptability problems, communication faults, energy management faults, and safety risk problems.

[0019] A signal processing module, the signals processed by the signal processing module come from the operation data of sensors and control systems inside the robot system, and the signal processing module processes these data; the data processing in the signal processing module includes data cleaning, denoising, and normalization steps, and the quality of the data is ensured by removing duplicate records, filling in missing values, correcting errors and outliers.

[0020] A status monitoring module, the status detection module executes tests and reports the physical status or performance of a certain component or subsystem, and the data detected by the status monitoring module is transmitted to the signal processing module; A warning evaluation module, the warning evaluation module diagnoses and reports the health status of each assigned component or subsystem; this layer generates the remaining available life of a component or subsystem in a given usage state for fault prediction; the warning evaluation module performs fault prediction based on data fusion by combining data from different sources, and the data includes sensor data, historical log file data, and expert system data.

[0021] The warning and assessment module conducts fault prediction based on statistical process control, performs prediction and health management on the robot system during operation, evaluates which health state the current system is in during the health degradation process, determines the fault causes for the system to decline in health level, and takes measures for preventive maintenance.

[0022] The warning and assessment module needs to evaluate the health state of the current robot system during the health degradation process, and the health state is one of the normal state, the performance degradation state, or the function failure state. When the robot system is in the performance degradation state, determine the fault causes for the robot system to decline in health level, and evaluate the degree of deviation of the current health state from its normal state. Predict the future health state of the robot system, including studying whether the robot system can normally complete the function requirements within the next task and studying the remaining life of the robot system.

[0023] Statistical process control conducts statistical analysis on various parameters of the robot system to understand the operating conditions of the system, collects parameter data related to the operating state of the robot in real time, analyzes using the control chart of SPC theory, and determines the distribution of data characteristic points through the judgment criteria of Shewhart to monitor the operating state of the robot.

[0024] Since the most important function of statistical process control is pre-detection, detecting abnormal causes during operation and resolving them in a timely manner to achieve fault prediction for the system. Calculate the Cpk value according to the above calculation method of process capability index, determine whether the operating state of the robot system reaches the critical value of the health state through the Cpk index, and analyze the Cpk value to predict the fault points and fault causes of the system. Call the corresponding dedicated robot system maintenance expert knowledge base according to the Cpk index to take necessary maintenance and repair measures to appropriately repair the system.

[0025] A decision-making module, which re-plans the next task of the system according to the future health state of the robot system and generates a maintenance plan for the system; An interaction module, which supports point-to-point communication with other levels to facilitate the operator to directly obtain the original information of the robot system and control various parts of the system.

[0026] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0027] The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided in the above embodiments.

[0028] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method provided in the above embodiments.

[0029] In an exemplary embodiment, a computer program product includes a computer program which, when executed by a processor, implements the method provided in the above embodiments.

[0030] A method of using a robot-based fault warning system includes the following steps: Step 1: Execute tests and report the physical state or performance of a certain component or subsystem through a state detection module, and transmit the data detected by the state monitoring module to a signal processing module; Step 2: The signal processing module processes the operation data from sensors and control systems within the robot system; Step 3: An early warning evaluation module that diagnoses and reports the health status of each assigned component or subsystem; generates the remaining available life of a component or subsystem in a given usage state for fault prediction; the early warning evaluation module performs fault prediction based on data fusion by combining data from different sources.

[0031] The early warning evaluation module performs fault prediction based on statistical process control, predicts and manages the health of the robot system during operation, evaluates which health state the current system is in during the health degradation process; determines the fault cause for the system to cause a decline in health level, and takes measures for preventive maintenance.

[0032] Statistical process control performs statistical analysis on various parameters of the robot system, understands the operation status of the system, real-time collects parameter data related to the operation state of the robot, analyzes using control charts of SPC theory, and judges the distribution of data characteristic points through the judgment criteria of Shewhart to monitor the operation state of the robot.

[0033] Step 4: The decision-making module re-plans the next task of the system according to the future health state of the robot system and generates a maintenance plan for the system.

[0034] The above describes the present invention and its implementation manners, and this description is not restrictive. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural manners and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A robot-based fault warning method and system, characterized in that Comprising: A robot fault type database, where the robot fault types include hardware faults, software faults, environmental adaptability problems, communication faults, energy management faults, and safety risk problems; A signal processing module, where the signals processed by the signal processing module come from the operation data of sensors and control systems inside the robot system, and the signal processing module processes these data; A status monitoring module, where the status detection module executes tests and reports the physical status or performance of a certain component or subsystem, and the data detected by the status monitoring module is transmitted to the signal processing module; A warning evaluation module, where the warning evaluation module diagnoses and reports the health status of each assigned component or subsystem; generates the remaining available life of a component or subsystem in a given usage state for fault prediction; A decision-making module, where the decision-making module re-plans the next task of the system according to the future health status of the robot system and generates a maintenance plan for the system; An interaction module, where the interaction module supports point-to-point communication with other levels to facilitate the operator to directly obtain the original information of the robot system and control each part of the system.

2. The fault warning method and system based on a robot according to claim 1, characterized in that: The warning evaluation module performs fault prediction based on data fusion, by combining data from different sources, and the data includes sensor data, historical log file data, and expert system data.

3. A robot-based fault warning method and system according to claim 1, characterized in that: The data processing in the signal processing module includes data cleaning, denoising, and normalization steps, by removing duplicate records, filling in missing values, correcting errors and outliers to ensure the quality of the data.

4. A robot-based fault warning method and system according to claim 1, characterized in that: The warning evaluation module performs fault prediction based on statistical process control, predicts and manages the health of the robot system during operation, and evaluates which health state the current system is in during the health degradation process; Judge the fault cause of the system that causes the decline in health level and take measures for preventive maintenance.

5. The method and system for fault early warning based on a robot according to claim 4, characterized in that: The warning evaluation module needs to evaluate the health state of the current robot system during the health degradation process, and the health state is one of the normal state, performance decline state, or function failure state.

6. The method and system for fault early warning based on a robot according to claim 5, characterized in that: When the robot system is in the performance decline state, judge the fault cause of the robot system that causes the decline in health level, and evaluate the degree of deviation of the current health state from its normal state; predict the future health state of the robot system, including studying whether the robot system can normally complete the function requirements within the next task and studying the remaining life of the robot system.

7. A robot-based fault warning method and system according to claim 4, characterized in that: The statistical process control performs statistical analysis on various parameters of the robot system, understands the operation status of the system, collects parameter data related to the operation state of the robot in real time, analyzes using the control chart of SPC theory, and judges the distribution of data characteristic points through the judgment criterion of Shewhart to monitor the operation state of the robot.

8. An electronic device, characterized in that, Including at least one processor; and a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-7.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are for causing the computer to perform the method according to any one of claims 1-7.

10. A computer program product, characterized in that, A computer program is included, which implements the method according to any one of claims 1-7 when executed by a processor.

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