摘要
故障预测与健康管理(PHM)是设备运行维护管理的有效方法,早期主要应用于航天、军用装备等领域。随着故障诊断和数据采集与分析等相关技术的不断发展,PHM方法逐渐在民用航空、装备制造等领域得到应用,并进一步在多行业得到推广和普及。传统PHM方法有基于经验模型的方法、基于数据驱动的方法和基于物理模型的方法。但这些方法存在着不少的局限性。随着数字孪生技术的兴起,其模型和数据相融合的问题处理方法,可有效地解决传统PHM方法的不足,是PHM技术发展的一个方向。在对PHM相关技术和方法进行了研究之后,综述了数字孪生技术及其在PHM中的应用方法,并对应用中的关键问题进行了总结和分析,指出数字孪生支持下的PHM方法未来发展方向。
Prognostics and health management(PHM)is an effective method for equipment operation and maintenance management,which is mainly applied in aerospace and military equipment in the early days.With the continuous development of fault diagnosis and related technologies such as data acquisition and analysis,PHM method is gradually applied in civil aviation,equipment manufacturing and other fields,and further promoted and popularized in multiple industries.Traditional PHM methods include empirical model-based methods,data-driven methods and physical model-based methods.But these methods have a number of limitations.With the rise of digital twin technology,its problem processing method of model and data integration can effectively solve the shortcomings of traditional PHM methods and is a direction of PHM technology development.After the study of PHM-related technologies and methods,the digital twin technology and its application methods in PHM are reviewed,and finally the key issues in the application are summarized and analyzed,pointing out the future development direction of PHM methods supported by digital twin.
作者
陆剑峰
徐煜昊
夏路遥
张浩
LU Jianfeng;XU Yuhao;XIA Luyao;ZHANG Hao(CIMS Research Center,School of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《自动化仪表》
CAS
2022年第6期1-7,12,共8页
Process Automation Instrumentation
基金
国家自然科学基金资助项目(72171173)
山东省重大科技创新工程基金资助项目(2019TSLH0211)。
关键词
故障预测与健康管理
数字孪生
设备运行管理
设备健康管理
视情维护
故障诊断
模型和数据融合
Prognostics and health management(PHM)
Digital twin
Equipment operation management
Equipment health management
Situational maintenance
Fault diagnosis
Model and data fusion
作者简介
陆剑峰(1973-),男,博士,副教授,主要从事智能制造方向的研究,E-mail:lujianfeng@tongji.edu.cn;通信作者:徐煜昊,男,在读硕士研究生,主要从事智能制造方向的研究,E-mail:2130808@tongji.edu.cn。
