Challenges and Opportunities of AI-Enabled Monitoring,Diagnosis&Prognosis:A Review

• 作者: Zhibin Zhao Jingyao Wu Tianfu Li Chuang Sun Ruqiang Yan Xuefeng Chen
• 来源期刊: 中国机械工程学报
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis
• 页码范围: 3-31
• 页数: 29页
• 语种: 英文

摘要

Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry.With the development of artificial intelligence(AI),especially deep learning(DL)approaches,the application of AI-enabled methods to monitor,diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry.However,there is still a gap to cover monitoring,diagnosis,and prognosis based on AI-enabled methods,simultaneously,and the importance of an open source community,including open source datasets and codes,has not been fully emphasized.To fill this gap,this paper provides a systematic overview of the current development,common technologies,open source datasets,codes,and challenges of AI-enabled PHM methods from three aspects of monitoring,diagnosis,and prognosis.