中国机械工程

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[设备健康管理]基于CPS方法与脆弱性评估的制造系统健康状态智能诊断

高贵兵;岳文辉;王峰   

  1. 湖南科技大学机电工程学院,湘潭,411201
  • 出版日期:2019-01-25 发布日期:2019-01-29
  • 基金资助:
    国家自然科学基金资助项目(51705149);
    湖南省自然科学基金资助项目(2017JJ2124)

Intelligent Diagnosis on Health Status of Manufacturing Systems Based on Embedded CPS Method and Vulnerability Assessment

GAO Guibing;YUE Wenhui;WANG Feng   

  1. College of Mechanical and Electrical Engineering, Hunan Universty of Science & Technology, Xiangtan,Hunan,411201
  • Online:2019-01-25 Published:2019-01-29

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。

关键词: 信息物理系统, 健康状态, 脆弱性, 制造系统, 数据驱动

Abstract: 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.

Key words: cyber physical systems(CPS);health status, vulnerability ;manufacturing system;data driven

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