CN111487950A - 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis - Google Patents
'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis Download PDFInfo
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Abstract
A 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis comprises an online early warning and offline diagnosis closed-loop system; the online early warning closed-loop system comprises online prediction, verification, feedback and optimization, the online prediction realizes fault type prediction, the online verification judges whether the online prediction is correct or not, the online feedback feeds fault information back to the fault early warning system, and the online optimization optimizes an online error self-checking algorithm to perfect a fault case base; the off-line diagnosis closed-loop system comprises off-line prediction, verification, feedback and optimization, wherein the off-line prediction establishes a precision degradation model to realize the off-line prediction of the health condition of the robot, the off-line verification of the accuracy of the off-line prediction, the off-line feedback feeds off-line prediction parameters back to the precision degradation model, and the off-line optimization analyzes the parameter influence and the evolution rule of the precision degradation model, improves the digital-analog linkage algorithm and optimizes the parameters; the invention realizes accurate fault early warning under complex working conditions and more accurate predictive maintenance.
Description
Technical Field
The invention relates to the technical field of industrial robots, in particular to a 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis.
Background
With the rapid development of industrial upgrading and intelligent manufacturing in China, industrial robots have become key supporting equipment for promoting and manufacturing strong country construction. Although China has become a big country for industrial robot application for 5 years, the whole level has a significant gap compared with the international level, and the reason for this is: domestic robots lack effective fault diagnosis and predictive maintenance support in their safe and reliable operation. Different from the traditional diagnosis object, the industrial robot has the characteristics of real-time driving and controlling, continuous operation, electromechanical control and inductive coupling and the like, and has the defects of obvious individual difference and complex and changeable working conditions, so that the reliable operation of the industrial robot is difficult to ensure in the conventional regular maintenance mode. In addition, the industrial robot has failure modes of a structure body, an execution part and a control circuit, and has the problems of accuracy loss, performance decline and the like, so that the development of health assessment and performance prediction on the industrial robot is difficult.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis, and a method such as big data and a neural network are cooperated to realize accurate fault early warning of an industrial robot under a complex working condition and realize more accurate predictive maintenance.
In order to achieve the purpose, the invention adopts the following technical scheme:
a 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis comprises an online early warning closed-loop system and an offline diagnosis closed-loop system; the online early warning closed-loop system comprises four links of online prediction, online verification, online feedback and online optimization, wherein the online prediction link realizes fault type prediction, the online verification link judges whether the online prediction is correct or not, the online feedback link feeds fault information back to the fault early warning system, and the online optimization link optimizes an online error self-checking algorithm to perfect a fault case base; the offline diagnosis closed-loop system comprises four links of offline prediction, offline verification, offline feedback and offline optimization, wherein the offline prediction link establishes a precision degradation model through a digital-analog linkage algorithm to realize offline prediction of the health condition of the robot at a certain time node, the offline verification link verifies the accuracy of the offline prediction, the offline feedback link feeds off offline prediction parameters to the precision degradation model, and the offline optimization link analyzes parameter influence and the evolution rule of the precision degradation model, so that the digital-analog linkage algorithm is improved and optimized.
The fault early warning system of the online early warning closed-loop system is carried on an edge end server and comprises an online error self-checking algorithm, a fault case library and a warning module; the online error self-checking algorithm extracts fault information, the fault case library provides characteristic information of different faults, and the alarm module alarms the faults.
The online prediction link in the online early warning closed-loop system comprises a fault early warning system which compares fault information extracted by an error self-checking algorithm with a fault case library, identifies the fault type and simultaneously utilizes a warning module to give a warning.
The online verification link in the online early warning closed-loop system comprises the steps of comparing the online predicted fault type with the actual fault type, judging the online predicted result, if the online predicted result is accurate, continuing to monitor the running state of the robot, and collecting fault information; and if the online prediction result is wrong, continuing to perform online feedback and online optimization links.
The online feedback link in the online early warning closed-loop system is to feed back fault information extracted by the online error self-checking algorithm to the fault early warning system.
The online optimization link in the online early warning closed-loop system is to optimize an online error self-checking algorithm according to fault information and improve the algorithm; if a novel fault occurs, the fault information is recorded into a fault case library, and the alarm accuracy of the system is improved.
The off-line prediction link in the off-line diagnosis closed-loop system comprises the steps of establishing an industrial robot precision degradation model by using a digital-analog linkage algorithm according to the historical working condition of the industrial robot, wherein the historical working condition comprises working time and working load parameters, and calculating the operation characteristic parameters of the industrial robot at a certain time node through the precision degradation model, so that the health state of the industrial robot is predicted off-line.
The off-line verification link in the off-line diagnosis closed-loop system comprises the steps of calculating an operation characteristic parameter of the industrial robot at a certain time node by using the precision degradation model, comparing the operation characteristic parameter with an actual characteristic parameter of the industrial robot when the industrial robot operates to the time node, and judging that the result comprises the accurate off-line prediction result and the deviation of the off-line prediction result; if the off-line prediction result is accurate, the health state of the industrial robot is continuously predicted by using the precision degradation model; and if the off-line prediction result has deviation, implementing off-line feedback and off-line optimization links.
The off-line feedback link in the off-line diagnosis closed-loop system is to feed back the operation characteristic parameters predicted by the digital-analog linkage algorithm to the precision degradation model.
The off-line optimization link in the off-line diagnosis closed-loop system comprises the steps of analyzing parameter influence and model evolution rules, improving a digital-analog linkage algorithm and optimizing parameters, and improving the accuracy of the precision degradation model on the health state prediction of the industrial robot.
Compared with the prior art, the invention has the following beneficial technical effects:
the online early warning closed-loop system compares the fault information extracted by an online error self-checking algorithm in the fault early warning system with a fault case library, identifies the fault type and gives an alarm, compares the fault type predicted online with the actual fault type, judges whether the alarm result is correct or not, feeds back the fault information extracted by the algorithm online, optimizes the algorithm, inputs the fault information into the fault case library if a novel fault occurs, and improves the alarm accuracy of the system; the offline diagnosis closed-loop system utilizes a digital-analog linkage algorithm to establish a precision degradation model according to historical working conditions, utilizes the precision degradation model to offline predict the operation characteristic parameters of the industrial robot at a certain time node, compares the offline prediction parameters with actual operation parameters, feeds the offline prediction parameters back to the precision degradation model if the offline prediction parameters are deviated from the actual operation parameters, analyzes the parameter influence and the model evolution rule, performs parameter optimization on the precision degradation model, and improves the health state prediction accuracy. The method realizes closed-loop feedback of online early warning and offline diagnosis, and has important significance for realizing accurate fault early warning of the industrial robot under complex working conditions and realizing more accurate predictive maintenance.
Drawings
FIG. 1 is a system configuration of the present invention.
Fig. 2 is a framework diagram of the operation of the on-line early warning closed-loop system of the present invention.
FIG. 3 is a block diagram of the operation of the off-line diagnostic closed loop system of the present invention.
Detailed Description
The present invention will be described in detail below with reference to examples and the accompanying drawings.
Referring to fig. 1, a 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis comprises an online early warning closed-loop system and an offline diagnosis closed-loop system; the online early warning closed-loop system comprises four links of online prediction, online verification, online feedback and online optimization, wherein the online prediction link realizes fault type prediction, the online verification link judges whether the online prediction is correct or not, the online feedback link feeds fault information back to the fault early warning system, and the online optimization link optimizes an online error self-checking algorithm to perfect a fault case base; the offline diagnosis closed-loop system comprises four links of offline prediction, offline verification, offline feedback and offline optimization, wherein the offline prediction link establishes a precision degradation model through a digital-analog linkage algorithm to realize offline prediction of the health condition of the robot at a certain time node, the offline verification link verifies the accuracy of the offline prediction, the offline feedback link feeds off offline prediction parameters to the precision degradation model, and the offline optimization link analyzes parameter influence and the evolution rule of the precision degradation model, so that the digital-analog linkage algorithm is improved and optimized.
Referring to fig. 2, the fault early warning system of the online early warning closed-loop system is mounted on an edge server, and comprises an online error self-checking algorithm, a fault case library and an alarm module; the online error self-checking algorithm extracts fault information, the fault case library provides characteristic information of different faults, and the alarm module alarms the faults.
The online prediction link in the online early warning closed-loop system comprises a fault early warning system which compares fault information extracted by an error self-checking algorithm with a fault case library, identifies the fault type and simultaneously utilizes a warning module to give a warning.
The online verification link in the online early warning closed-loop system comprises the steps of comparing the online predicted fault type with the actual fault type, judging the online predicted result, if the online predicted result is accurate, continuing to monitor the running state of the robot, and collecting fault information; and if the online prediction result is wrong, continuing to perform online feedback and online optimization links.
The online feedback link in the online early warning closed-loop system is to feed back fault information extracted by the online error self-checking algorithm to the fault early warning system.
The online optimization link in the online early warning closed-loop system is to optimize an online error self-checking algorithm according to fault information and improve the algorithm; if a novel fault occurs, the fault information is recorded into a fault case library, and the alarm accuracy of the system is improved.
Referring to fig. 3, the off-line prediction link in the off-line diagnosis closed-loop system includes that a precision degradation model of the industrial robot is established by using a digital-analog linkage algorithm according to the historical working condition of the industrial robot, the historical working condition includes parameters such as working time and working load, and the running characteristic parameters of the industrial robot at a certain time node are calculated through the precision degradation model, so that the health state of the industrial robot is predicted off-line.
The off-line verification link in the off-line diagnosis closed-loop system comprises the steps of calculating an operation characteristic parameter of the industrial robot at a certain time node by using the precision degradation model, comparing the operation characteristic parameter with an actual characteristic parameter of the industrial robot when the industrial robot operates to the time node, and judging that the result comprises the accurate off-line prediction result and the deviation of the off-line prediction result; if the off-line prediction result is accurate, the health state of the industrial robot is continuously predicted by using the precision degradation model; and if the off-line prediction result has deviation, implementing off-line feedback and off-line optimization links.
The off-line feedback link in the off-line diagnosis closed-loop system is to feed back the operation characteristic parameters predicted by the digital-analog linkage algorithm to the precision degradation model.
The off-line optimization link in the off-line diagnosis closed-loop system comprises the steps of analyzing parameter influence and model evolution rules, improving a digital-analog linkage algorithm and optimizing parameters, and improving the accuracy of the precision degradation model on the health state prediction of the industrial robot.
Claims (10)
1. A 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis is characterized in that: the system comprises an online early warning closed-loop system and an offline diagnosis closed-loop system; the online early warning closed-loop system comprises four links of online prediction, online verification, online feedback and online optimization, wherein the online prediction link realizes fault type prediction, the online verification link judges whether the online prediction is correct or not, the online feedback link feeds fault information back to the fault early warning system, and the online optimization link optimizes an online error self-checking algorithm to perfect a fault case base; the offline diagnosis closed-loop system comprises four links of offline prediction, offline verification, offline feedback and offline optimization, wherein the offline prediction link establishes a precision degradation model through a digital-analog linkage algorithm to realize offline prediction of the health condition of the robot at a certain time node, the offline verification link verifies the accuracy of the offline prediction, the offline feedback link feeds off offline prediction parameters to the precision degradation model, and the offline optimization link analyzes parameter influence and the evolution rule of the precision degradation model, so that the digital-analog linkage algorithm is improved and optimized.
2. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the fault early warning system of the online early warning closed-loop system is carried on an edge end server and comprises an online error self-checking algorithm, a fault case library and a warning module; the online error self-checking algorithm extracts fault information, the fault case library provides characteristic information of different faults, and the alarm module alarms the faults.
3. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the online prediction link in the online early warning closed-loop system comprises a fault early warning system which compares fault information extracted by an error self-checking algorithm with a fault case library, identifies the fault type and simultaneously utilizes a warning module to give a warning.
4. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the online verification link in the online early warning closed-loop system comprises the steps of comparing the online predicted fault type with the actual fault type, judging the online predicted result, if the online predicted result is accurate, continuing to monitor the running state of the robot, and collecting fault information; and if the online prediction result is wrong, continuing to perform online feedback and online optimization links.
5. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the online feedback link in the online early warning closed-loop system is to feed back fault information extracted by the online error self-checking algorithm to the fault early warning system.
6. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the online optimization link in the online early warning closed-loop system is to optimize an online error self-checking algorithm according to fault information and improve the algorithm; if a novel fault occurs, the fault information is recorded into a fault case library, and the alarm accuracy of the system is improved.
7. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the off-line prediction link in the off-line diagnosis closed-loop system comprises the steps of establishing an industrial robot precision degradation model by using a digital-analog linkage algorithm according to the historical working condition of the industrial robot, wherein the historical working condition comprises working time and working load parameters, and calculating the operation characteristic parameters of the industrial robot at a certain time node through the precision degradation model, so that the health state of the industrial robot is predicted off-line.
8. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the off-line verification link in the off-line diagnosis closed-loop system comprises the steps of calculating an operation characteristic parameter of the industrial robot at a certain time node by using the precision degradation model, comparing the operation characteristic parameter with an actual characteristic parameter of the industrial robot when the industrial robot operates to the time node, and judging that the result comprises the accurate off-line prediction result and the deviation of the off-line prediction result; if the off-line prediction result is accurate, the health state of the industrial robot is continuously predicted by using the precision degradation model; and if the off-line prediction result has deviation, implementing off-line feedback and off-line optimization links.
9. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the off-line feedback link in the off-line diagnosis closed-loop system is to feed back the operation characteristic parameters predicted by the digital-analog linkage algorithm to the precision degradation model.
10. The "prediction-verification-feedback-optimization" closed-loop system for on-line early warning and off-line diagnosis as claimed in claim 1, wherein: the off-line optimization link in the off-line diagnosis closed-loop system comprises the steps of analyzing parameter influence and model evolution rules, improving a digital-analog linkage algorithm and optimizing parameters, and improving the accuracy of the precision degradation model on the health state prediction of the industrial robot.
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| CN114202093A (en) * | 2021-08-25 | 2022-03-18 | 优合集团有限公司 | Industrial chain optimization platform based on industrial internet |
| CN114925934A (en) * | 2022-06-17 | 2022-08-19 | 西藏众陶联供应链服务有限公司 | Prediction model verification method for building ceramic production |
| CN115809749A (en) * | 2023-02-09 | 2023-03-17 | 四川文韬工程技术有限公司 | Establishment method of sewage treatment comprehensive online prediction model and prediction early warning method |
| CN115809749B (en) * | 2023-02-09 | 2023-09-12 | 四川文韬工程技术有限公司 | Method for establishing comprehensive online prediction model of sewage treatment and prediction and early warning method |
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