CN111487950A - 'prediction-verification-feedback-optimization' closed-loop system for online early warning and offline diagnosis - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及工业机器人技术领域，尤其涉及一种在线预警和离线诊断的“预测-验证-反馈-优化”闭环系统。The invention relates to the technical field of industrial robots, in particular to a "prediction-verification-feedback-optimization" closed-loop system of online early warning and offline diagnosis.
背景技术Background technique
随着我国产业升级和智能制造快速发展，工业机器人已成为推进制造强国建设的关键支撑装备。虽然我国已连续5年成为工业机器人应用大国，但整体水平与国际相比还存在显著差距，究其原因是：国产机器人在其安全可靠运行方面缺乏有效的故障诊断和预测性维护支撑。工业机器人与传统诊断对象不同，不仅有实时驱控、连续作业、机电控感耦合等特点，而且个体差异显著、工况复杂多变，导致现有定期维保方式难以保障工业机器人的可靠运行。此外，工业机器人不仅有结构本体、执行部件和控制电路的失效模式，还存在精度丧失、性能衰退等问题，使得对其开展健康评估与性能预测难上加难。With the rapid development of my country's industrial upgrading and intelligent manufacturing, industrial robots have become the key supporting equipment to promote the construction of a strong manufacturing country. Although my country has become a major country in the application of industrial robots for five consecutive years, there is still a significant gap between the overall level and the international level. The reason is that domestic robots lack effective fault diagnosis and predictive maintenance support in terms of their safe and reliable operation. Different from traditional diagnostic objects, industrial robots not only have the characteristics of real-time drive control, continuous operation, electromechanical control and induction coupling, but also have significant individual differences and complex and changeable working conditions, which makes it difficult for the existing regular maintenance methods to ensure the reliable operation of industrial robots. . In addition, industrial robots not only have failure modes of structural body, execution components and control circuits, but also have problems such as loss of accuracy and performance degradation, which make it more difficult to carry out health assessment and performance prediction.
发明内容SUMMARY OF THE INVENTION
为了克服上述现有技术的缺点，本发明的目的在于提供一种在线预警和离线诊断的“预测-验证-反馈-优化”闭环系统，协同大数据、神经网络等方法，实现工业机器人在复杂工况下的准确故障预警，实现更准确的预测性维保。In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a closed-loop system of "prediction-verification-feedback-optimization" for online early warning and offline diagnosis, which cooperates with methods such as big data and neural network to realize industrial robots in complex work. Accurate early warning of faults under conditions to achieve more accurate predictive maintenance.
为达到上述目的，本发明采用如下技术方案：To achieve the above object, the present invention adopts the following technical solutions:
一种在线预警和离线诊断的“预测-验证-反馈-优化”闭环系统，包括在线预警闭环系统和离线诊断闭环系统；在线预警闭环系统包括在线预测、在线验证、在线反馈、在线优化四个环节，在线预测环节实现故障类型预测，在线验证环节判别在线预测是否正确，在线反馈环节将故障信息反馈至故障预警系统，在线优化环节对在线误差自检算法进行优化，完善故障案例库；离线诊断闭环系统包括离线预测、离线验证、离线反馈、离线优化四个环节，离线预测环节通过数模联动算法建立精度退化模型，实现某一时间节点机器人健康状况离线预测，离线验证环节验证离线预测准确性，离线反馈环节将离线预测参数反馈至精度退化模型，离线优化环节分析参数影响以及精度退化模型演变规律，对数模联动算法进行改进以及参数优化。A "prediction-verification-feedback-optimization" closed-loop system for online early warning and offline diagnosis, including online early warning closed-loop system and offline diagnosis closed-loop system; online early warning closed-loop system includes online prediction, online verification, online feedback, and online optimization. Four links , the online prediction link realizes fault type prediction, the online verification link determines whether the online prediction is correct, the online feedback link feeds back the fault information to the fault early warning system, and the online optimization link optimizes the online error self-checking algorithm to improve the fault case database; offline diagnosis closed-loop The system includes four links: offline prediction, offline verification, offline feedback, and offline optimization. The offline prediction link establishes an accuracy degradation model through a digital-analog linkage algorithm to achieve offline prediction of the robot's health status at a certain time node. The offline verification link verifies the accuracy of offline prediction. The offline feedback link feeds the offline prediction parameters to the accuracy degradation model, and the offline optimization link analyzes the influence of parameters and the evolution law of the accuracy degradation model, and improves the digital-analog linkage algorithm and optimizes parameters.
所述的在线预警闭环系统的故障预警系统搭载在边缘端服务器上，故障预警系统包括在线误差自检算法和故障案例库和报警模块；在线误差自检算法提取故障信息，故障案例库提供不同故障的特征信息，报警模块对故障进行报警。The fault early warning system of the online early warning closed-loop system is mounted on the edge server, and the fault early warning system includes an online error self-checking algorithm, a fault case library and an alarm module; the online error self-checking algorithm extracts fault information, and the fault case library provides different faults. The characteristic information, the alarm module will alarm the fault.
所述的在线预警闭环系统中在线预测环节包括故障预警系统对误差自检算法提取的故障信息与故障案例库进行对比，对故障类型进行识别，同时利用报警模块进行报警。The online prediction link in the online early warning closed-loop system includes that the fault early warning system compares the fault information extracted by the error self-checking algorithm with the fault case database, identifies the fault type, and uses the alarm module to give an alarm.
所述的在线预警闭环系统中在线验证环节包括将在线预测的故障类型与实际故障类型进行对比，判别在线预测结果，若在线预测结果准确，则继续对机器人运行状态进行监测，收集故障信息；若在线预测结果错误，则继续进行在线反馈及在线优化环节。The online verification link in the online early warning closed-loop system includes comparing the fault type predicted online with the actual fault type, and judging the online prediction result. If the online prediction result is accurate, continue to monitor the running state of the robot and collect fault information; If the online prediction result is wrong, the online feedback and online optimization will continue.
所述的在线预警闭环系统中在线反馈环节是指将在线误差自检算法提取的故障信息反馈至故障预警系统。The online feedback link in the online early warning closed-loop system refers to feeding back the fault information extracted by the online error self-checking algorithm to the fault early warning system.
所述的在线预警闭环系统中在线优化环节是指根据故障信息对在线误差自检算法进行优化，改进算法；若出现新型故障，则将故障信息录入故障案例库，提高系统的报警正确率。The online optimization link in the online early warning closed-loop system refers to optimizing the online error self-checking algorithm according to the fault information and improving the algorithm; if a new type of fault occurs, the fault information is entered into the fault case database to improve the alarm accuracy of the system.
所述的离线诊断闭环系统中离线预测环节包括根据工业机器人历史工作状况，利用数模联动算法建立工业机器人精度退化模型，历史工作状况包括工作时间、工作载荷参数，通过精度退化模型计算工业机器人在某一时间节点的运行特征参数，从而对工业机器人健康状态进行离线预测。The offline prediction link in the offline diagnosis closed-loop system includes establishing an accuracy degradation model of the industrial robot by using a digital-analog linkage algorithm according to the historical working conditions of the industrial robot. The historical working conditions include working time and working load parameters. The operating characteristic parameters of a certain time node can be used to predict the health status of industrial robots offline.
所述的离线诊断闭环系统中离线验证环节包括将精度退化模型计算出工业机器人在某一时间节点的运行特征参数与工业机器人运行至该时间节点实际特征参数进行对比，判别结果包括离线预测结果准确和离线预测结果有偏差；若离线预测结果准确，则继续利用精度退化模型对工业机器人健康状态进行预测；若离线预测结果有偏差，则实施离线反馈及离线优化环节。The offline verification link in the offline diagnosis closed-loop system includes comparing the operation characteristic parameters of the industrial robot at a certain time node calculated by the precision degradation model with the actual characteristic parameters of the industrial robot running to this time node, and the discriminating results include that the offline prediction results are accurate. There is a deviation from the offline prediction result; if the offline prediction result is accurate, the accuracy degradation model will continue to be used to predict the health state of the industrial robot; if the offline prediction result is deviated, offline feedback and offline optimization will be implemented.
所述的离线诊断闭环系统中离线反馈环节是指将数模联动算法预测的运行特征参数反馈至精度退化模型。The offline feedback link in the offline diagnosis closed-loop system refers to feeding back the operating characteristic parameters predicted by the digital-analog linkage algorithm to the precision degradation model.
所述的离线诊断闭环系统中离线优化环节包括分析参数影响以及模型演变规律，对数模联动算法进行改进以及参数优化，提高精度退化模型对工业机器人健康状态预测的准确率。The offline optimization link in the offline diagnosis closed-loop system includes analyzing the influence of parameters and the evolution law of the model, improving the digital-analog linkage algorithm and optimizing the parameters, so as to improve the accuracy of the precision degradation model for predicting the health state of the industrial robot.
与现有技术相比，本发明具有以下有益的技术效果：Compared with the prior art, the present invention has the following beneficial technical effects:
本发明在线预警闭环系统将故障预警系统中在线误差自检算法提取的故障信息与故障案例库对比，识别故障类型并报警，将在线预测的故障类型与实际故障类型对比，判别报警结果是否正确，在线反馈算法提取的故障信息，对算法进行优化，若出现新型故障，则将故障信息录入故障案例库，提高系统的报警准确率；离线诊断闭环系统利用数模联动算法根据历史工作状况建立精度退化模型，并利用精度退化模型离线预测工业机器人在某一时间节点的运行特征参数，将离线预测参数与实际运行参数对比，若二者有偏差则将离线预测参数反馈至精度退化模型，分析参数影响以及模型演变规律，对精度退化模型进行参数优化，提高健康状态预测正确率。本发明实现了在线预警和离线诊断的闭环反馈，对实现工业机器人在复杂工况下的准确故障预警、实现更准确的预测性维保具有重要意义。The online early warning closed-loop system of the invention compares the fault information extracted by the online error self-checking algorithm in the fault early warning system with the fault case database, identifies the fault type and gives an alarm, compares the fault type predicted online with the actual fault type, and judges whether the alarm result is correct or not. The fault information extracted by the online feedback algorithm is used to optimize the algorithm. If a new type of fault occurs, the fault information will be entered into the fault case database to improve the alarm accuracy of the system; the offline diagnosis closed-loop system uses the digital-analog linkage algorithm to establish the accuracy degradation according to the historical working conditions. model, and use the accuracy degradation model to offline predict the operating characteristic parameters of the industrial robot at a certain time node, and compare the offline predicted parameters with the actual operating parameters. As well as the model evolution law, the parameters of the precision degradation model are optimized to improve the accuracy of health state prediction. The invention realizes the closed-loop feedback of online early warning and offline diagnosis, and is of great significance for realizing accurate fault early warning of industrial robots under complex working conditions and realizing more accurate predictive maintenance.
附图说明Description of drawings
图1为本发明的系统组成。Fig. 1 is the system composition of the present invention.
图2为本发明在线预警闭环系统的运行框架图。FIG. 2 is an operation frame diagram of the online early warning closed-loop system of the present invention.
图3为本发明离线诊断闭环系统的运行框架图。FIG. 3 is an operation frame diagram of the off-line diagnosis closed-loop system of the present invention.
具体实施方式Detailed ways
下面结合实施例和附图对本发明做详细描述。The present invention will be described in detail below with reference to the embodiments and accompanying drawings.
参照图1，一种在线预警和离线诊断的“预测-验证-反馈-优化”闭环系统，包括在线预警闭环系统和离线诊断闭环系统；在线预警闭环系统包括在线预测、在线验证、在线反馈、在线优化四个环节，在线预测环节实现故障类型预测，在线验证环节判别在线预测是否正确，在线反馈环节将故障信息反馈至故障预警系统，在线优化环节对在线误差自检算法进行优化，完善故障案例库；离线诊断闭环系统包括离线预测、离线验证、离线反馈、离线优化四个环节，离线预测环节通过数模联动算法建立精度退化模型，实现某一时间节点机器人健康状况离线预测，离线验证环节验证离线预测准确性，离线反馈环节将离线预测参数反馈至精度退化模型，离线优化环节分析参数影响以及精度退化模型演变规律，对数模联动算法进行改进以及参数优化。Referring to Figure 1, a "prediction-verification-feedback-optimization" closed-loop system for online early warning and offline diagnosis includes an online early warning closed-loop system and an offline diagnosis closed-loop system; the online early warning closed-loop system includes online prediction, online verification, online feedback, online Four links are optimized. The online prediction link realizes fault type prediction, the online verification link judges whether the online prediction is correct, the online feedback link feeds back the fault information to the fault early warning system, and the online optimization link optimizes the online error self-checking algorithm to improve the fault case database. The offline diagnosis closed-loop system includes four links: offline prediction, offline verification, offline feedback, and offline optimization. The offline prediction link establishes an accuracy degradation model through a digital-analog linkage algorithm to achieve offline prediction of the robot's health status at a certain time node. The offline verification link verifies offline Prediction accuracy, the offline feedback link feeds the offline prediction parameters to the accuracy degradation model, and the offline optimization link analyzes the influence of parameters and the evolution law of the accuracy degradation model, and improves the digital-analog linkage algorithm and optimizes parameters.
参照图2，所述的在线预警闭环系统的故障预警系统搭载在边缘端服务器上，故障预警系统包括在线误差自检算法和故障案例库和报警模块；在线误差自检算法提取故障信息，故障案例库提供不同故障的特征信息，报警模块对故障进行报警。Referring to Figure 2, the fault early warning system of the online early warning closed-loop system is mounted on the edge server, and the fault early warning system includes an online error self-checking algorithm, a fault case database and an alarm module; the online error self-checking algorithm extracts fault information, and the fault case The library provides characteristic information of different faults, and the alarm module alarms the faults.
所述的在线预警闭环系统中在线预测环节包括故障预警系统对误差自检算法提取的故障信息与故障案例库进行对比，对故障类型进行识别，同时利用报警模块进行报警。The online prediction link in the online early warning closed-loop system includes that the fault early warning system compares the fault information extracted by the error self-checking algorithm with the fault case database, identifies the fault type, and uses the alarm module to give an alarm.
所述的在线预警闭环系统中在线验证环节包括将在线预测的故障类型与实际故障类型进行对比，判别在线预测结果，若在线预测结果准确，则继续对机器人运行状态进行监测，收集故障信息；若在线预测结果错误，则继续进行在线反馈及在线优化环节。The online verification link in the online early warning closed-loop system includes comparing the fault type predicted online with the actual fault type, and judging the online prediction result. If the online prediction result is accurate, continue to monitor the running state of the robot and collect fault information; If the online prediction result is wrong, the online feedback and online optimization will continue.
所述的在线预警闭环系统中在线反馈环节是指将在线误差自检算法提取的故障信息反馈至故障预警系统。The online feedback link in the online early warning closed-loop system refers to feeding back the fault information extracted by the online error self-checking algorithm to the fault early warning system.
所述的在线预警闭环系统中在线优化环节是指根据故障信息对在线误差自检算法进行优化，改进算法；若出现新型故障，则将故障信息录入故障案例库，提高系统的报警正确率。The online optimization link in the online early warning closed-loop system refers to optimizing the online error self-checking algorithm according to the fault information and improving the algorithm; if a new type of fault occurs, the fault information is entered into the fault case database to improve the alarm accuracy of the system.
参照图3，所述的离线诊断闭环系统中离线预测环节包括根据工业机器人历史工作状况，利用数模联动算法建立工业机器人精度退化模型，历史工作状况包括工作时间、工作载荷等参数，通过精度退化模型计算工业机器人在某一时间节点的运行特征参数，从而对工业机器人健康状态进行离线预测。Referring to Figure 3, the offline prediction link in the offline diagnosis closed-loop system includes establishing an accuracy degradation model of the industrial robot by using a digital-analog linkage algorithm according to the historical working conditions of the industrial robot. The historical working conditions include parameters such as working time and workload. The model calculates the operating characteristic parameters of the industrial robot at a certain time node, so as to predict the health state of the industrial robot offline.
所述的离线诊断闭环系统中离线验证环节包括将精度退化模型计算出工业机器人在某一时间节点的运行特征参数与工业机器人运行至该时间节点实际特征参数进行对比，判别结果包括离线预测结果准确和离线预测结果有偏差；若离线预测结果准确，则继续利用精度退化模型对工业机器人健康状态进行预测；若离线预测结果有偏差，则实施离线反馈及离线优化环节。The offline verification link in the offline diagnosis closed-loop system includes comparing the operation characteristic parameters of the industrial robot at a certain time node calculated by the precision degradation model with the actual characteristic parameters of the industrial robot running to this time node, and the discriminating results include that the offline prediction results are accurate. There is a deviation from the offline prediction result; if the offline prediction result is accurate, the accuracy degradation model will continue to be used to predict the health state of the industrial robot; if the offline prediction result is deviated, offline feedback and offline optimization will be implemented.
所述的离线诊断闭环系统中离线反馈环节是指将数模联动算法预测的运行特征参数反馈至精度退化模型。The offline feedback link in the offline diagnosis closed-loop system refers to feeding back the operating characteristic parameters predicted by the digital-analog linkage algorithm to the precision degradation model.
所述的离线诊断闭环系统中离线优化环节包括分析参数影响以及模型演变规律，对数模联动算法进行改进以及参数优化，提高精度退化模型对工业机器人健康状态预测的准确率。The offline optimization link in the offline diagnosis closed-loop system includes analyzing the influence of parameters and the evolution law of the model, improving the digital-analog linkage algorithm and optimizing the parameters, so as to improve the accuracy of the precision degradation model for predicting the health state of the industrial robot.
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