基于CPS方法与脆弱性评估的制造系统健康状态智能诊断
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摘要
现有的制造系统健康管理技术很少应用信息物理系统(CPS),也很少有学者从脆弱性的角度探究制造系统的"病因"。根据制造系统的结构特征和健康管理技术原理,提出在其关键设备上嵌入基于CPS与脆弱性分析的健康管理模块,以实现系统健康状态与影响因素的在线动态分析。重点研究基于脆弱性的系统健康状态判断方法、基于数据驱动的制造系统性能异常判断和亚健康状态下设备异常因素的识别。基于嵌入式CPS的制造系统设备健康状态诊断与分析可以根据设备服役过程中的脆弱性状况判断设备的健康状态,基于数据驱动的设备异常因素判断方法可以监测设备服役过程中的性能参数变化情况,及时判断造成设备异常的关键因素。通过柔性制造系统仿真实验,证明了所提方法可实时判断系统的健康状态,有效识别导致设备亚健康状态的性能参数。
Recently,the current health management technology was seldom applied in CPS,and few scholars explored the causes of the system failure from the perspective of vulnerability.According to the characteristics of forecast on health status in manufacturing systems,a forecasting health model embed on the key equipment was raised,which was aiming to achieve on-line forecast health and identification of critical factors in the whole service life cycles of the equipment.The research concentrated on the system framework of equipment health status online judgement,the abnormal judgment of manufacturing system performance based on data driven and the identification of equipment abnormal factors in sub-health states.Based on the performance changes during the operations of the equipment,the vulnerability status of equipment might be determined by the proposed method,and the changes of performance parameters in working processes were monitored by the equipment abnormality determination method,which might be adopted for finding the key factors that caused equipment abnormality.The results in a flexible manufacturing system show that those methods are effective ways to improve the efficiency of judgement of health status and to realize intelligent identification of critical factors.
Recently,the current health management technology was seldom applied in CPS,and few scholars explored the causes of the system failure from the perspective of vulnerability.According to the characteristics of forecast on health status in manufacturing systems,a forecasting health model embed on the key equipment was raised,which was aiming to achieve on-line forecast health and identification of critical factors in the whole service life cycles of the equipment.The research concentrated on the system framework of equipment health status online judgement,the abnormal judgment of manufacturing system performance based on data driven and the identification of equipment abnormal factors in sub-health states.Based on the performance changes during the operations of the equipment,the vulnerability status of equipment might be determined by the proposed method,and the changes of performance parameters in working processes were monitored by the equipment abnormality determination method,which might be adopted for finding the key factors that caused equipment abnormality.The results in a flexible manufacturing system show that those methods are effective ways to improve the efficiency of judgement of health status and to realize intelligent identification of critical factors.
引文
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[3] WANG Biyang,WANG Xifan,BIE Zhaohong,et al.Reliability Model of MMC Considering Periodic Preventive Maintenance[J].IEEE Transactions on Power Delivery,2017,32(3):1535-1544.
[4] BIAGETTI,T,SCIUBBA E.Automatic Diagnostics and Prognostics of Energy Conversion Processes via Knowledge Based Systems[J].Energy,2004,29(12/15):2553-2572.
[5]王玉刚,彭海军,王思臣.某型机载设备故障诊断专家系统知识库的设计[J].数据挖掘,2016,6(1):37-41.WANG Yugang,PENG Haijun, WANG Sichen.Design of Expert System Knowledge Base for Certain Airborne Equipment[J].Journal of Data Mining,2016,6(1):37-41.
[6] SIKORA M.Induction and Pruning of Classification Rules for Prediction of Microseismic Hazards in Coal Mines[J].Expert Systems with Applications,2011,38(6):6748-6758.
[7] CAESARENDRAA W,WIDODOB A,THOM P H,et al.Combined Probability Approach and Indirect Data-driven Method for Bearing Degradation Prognostics[J].IEEE Transcations on Reliabilbity,2011,60(1):14-20.
[8] THOM P H,YANG B S.Estimation and Forecasting of Machine Health Condition Using ARMA/GARCH Model[J].Mechanical Systems and Signal Processing,2010,24(2):546-558.
[9] WANG Qingxiao,CHEN J D.Application of GreyMarkov Forecasting Model to Machines Fault Forecast[J].Mechanical Science Technology,1997,16(3):491-495.
[10] DERLER P,LEE E A,VINCENTELLI A S.Modeling Cyber-physical Systems[J].Proceedings of the IEEE,2012,100(1):13-28.
[11] JIANG Bingnan,FEI Yunsi.A PHEV Power Management Cyber-physical System for On-road Applications[J].IEEE Transactions on Vehicular Technology,2017,66(7):5797-5807.
[12] CHEN Lü,LIU Yahui,HU Xiaosong,et al.Simultaneous Observation of Hybrid States for Cyber-Physical Systems:a Case Study of Electric Vehicle Powertrain[J].IEEE Transactions on Cybernetics,2017,48(8):2357-2367.
[13] MICHNIEWICZ J,REINHART G.Cyber-Physical-Robotics—Modelling of Modular Robot Cells for Automated Planning and Execution of Assembly Tasks[J].Mechatronics,2016,34:170-180.
[14] MONOSTORI L,KADAR B,BAUERNHANSL T,et al.Cyber-Physical Systems in Manufacturing[J].CIRP Annals—Manufacturing Technology,2016,65(2):621-641.
[15] PIRVU B C,ZAMFIRESCU C B,GORECKY D.Engineering Insights from an Anthropocentric CyberPhysical System:a Case Study for an Assembly Station[J].Mechatronics,2016,34(2):147-159.
[16] YAO Xifan,LIN Yingzi.Emerging Manufacturing Paradigm Shifts for the Incoming Industrial Revolution[J].International Journal of Advanced Manufacturing Technology,2016,85(5/8):1665-1676.
[17]高贵兵,岳文辉,张人龙.基于状态熵的制造系统结构脆弱性评估方法[J].计算机集成制造系统,2017,23(10):2211-2220.GAO Guibing,YUE Wenhui,ZHANG Renlong.Structural Vulnerability Assessment Method of Manufacturing Systems Based on State Entropy[J].Computer Integrated Manufacturing Systems,2017,23(10):2211-2220.
[18] CHEN Quan,MILI L.Composite Power System Vulnerability Evaluation to Cascading Failures Using Importance Sampling and Antithetic Variates[J].IEEE Transactions on Power Systems,2013,28(3):2321-2330.
[19] SHAWE-TAYLOR J,CRISTIANINI N.Kernel Methods for Pattern Analysis[J].Publications of the American Statistical Association,2004,101(476):1730-1730.