Remaining useful life prediction of individual units subject to hard failure

Abstract

To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key. It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes. This article proposes a two-stage prognostic framework for individual units subject to hard failure, based on joint modeling of degradation signals and time-to-event data. The proposed algorithm features a low computational load, online prediction, and dynamic updating. Its application to automotive battery RUL prediction is discussed in this article as an example. The effectiveness of the proposed method is demonstrated through a simulation study and real data.

Keywords::

• Remaining useful life prediction
• hard failure
• joint model

Additional information

Notes on contributors

Qiang Zhou

Qiang Zhou is an Assistant Professor at the Department of Systems Engineering and Engineering Management, City University of Hong Kong. He received a B.S. in Automotive Engineering (2005) and an M.S. in Mechanical Engineering (2007) from Tsinghua University, China, an M.S. in Statistics (2010) and a Ph.D. in Industrial Engineering (2011) at the University of Wisconsin–Madison. His research interests include statistical modeling and analysis of complex engineering systems, failure prognosis and health management, and design and analysis of computer experiments.

Junbo Son

Junbo Son is a Ph.D. candidate in Industrial and Systems Engineering at the University of Wisconsin–Madison. He received a B.S. in Industrial Systems and Information Engineering (2010) from the Korea University, South Korea.

Shiyu Zhou

Shiyu Zhou is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison. He received his B.S. and M.S. in Mechanical Engineering from the University of Science and Technology of China in 1993 and 1996, respectively, and his master's in Industrial Engineering and Ph.D. in Mechanical Engineering from the University of Michigan in 2000. His research interests include in-process quality and productivity improvement methodologies by integrating statistics, system and control theory, and engineering knowledge. His research is sponsored by the National Science Foundation, Department of Energy, Department of Commerce, and industry. He is a recipient of a CAREER Award from the National Science Foundation and the Best Application Paper Award from IIE Transactions. He is a member of IIE, INFORMS, ASME, and SME.

Xiaofeng Mao

Xiaofeng Mao received a B.E. degree from Peking University, Beijing, China, in 2001 and M.S. and Ph.D. degrees in Mechanical Engineering from The Pennsylvania State University, University Park, in 2007 and 2010, respectively. From 2011 to 2012, he performed research on diagnosis and prognosis of Li-ion and lead-acid batteries, and propulsion motors at the General Motors Research and Development center. In 2012, he joined the Chassis Control Group at General Motors. His research interests include nonlinear and robust control, hybrid electric vehicles, and chassis control.

Mutasim Salman

Mutasim Salman is a Laboratory Group Manager and a Technical Fellow in the Electrical, Controls and Integration Laboratory of GM Research and Development Center. He received his bachelor's degree in Electrical Engineering from the University of Texas at Austin and M.S. and Ph.D. in Electrical Engineering with a specialization in Systems and Control from the University of Illinois at Urbana–Champaign. He is responsible for the development and validation of algorithms for state-of-health monitoring, diagnosis, prognosis, and fault-tolerant control of a vehicle in critical systems. He has extensive experience in the modeling, control, and energy management strategies for hybrid vehicle. He has several GM awards, including four Boss Kettering, three McCuen, and two President and Chairman Awards.