PHM 2021 Keynote Speech 2

Big Data-driven Remaining Useful Life Prediction Methods for Machines


Remaining useful life (RUL) prediction for machines is one of the cutting-edge research topics in the field of PHM. It is able to extend the operational time and increases the availability, safety and reliability of machines, therefore decreasing the maintenance cost and brings economic benefits. To predict the RUL accurately, numerous sensors are mounted on each machine and massive data are acquired by the high sampling frequency after the long-time operation, which promotes RUL prediction for machines to enter the era of big data. This talk will discuss the several key issues in the academic research of RUL prediction, then present recent progress of the speaker's group, and finally show some practical diagnosis and prognosis systems for wind turbines, industrial robots, metro equipment, trucks, etc.


Yaguo Lei avatar
Dr. Yaguo Lei China


Xi'an Jiaotong University, China

Dr. Yaguo Lei is the Professor of Xi'an Jiaotong University. He ever worked at the University of Duisburg-Essen, Germany as an Alexander von Humboldt fellow and at the University of Alberta, Canada as a postdoctoral research fellow. He is also a fellow of IET and ISEAM, a senior member of IEEE, a member of ASME, and senior members of CMES, ORSC and CAA, respectively, and the associate editors/ the editorial board members of IEEE TIE, MSSP, NC&A, MST, etc.

His research interests includes health condition monitoring and intelligent maintenance, big-data era intelligent fault diagnostics and prognostics, reliability evaluation and remaining useful life prediction, mechanical signal analysis and processing, and mechanical system dynamic modeling. He has pioneered many signal processing techniques, intelligent diagnosis models and remaining useful life prediction methods. He has written one monography in English, which was awarded ‘Excellent Export Books’. His H-index has raised to 50, and he was selected as the Highly Cited Researcher by Clarivate in 2019 and 2020, respectively.