Prediction of the failure interval with maximum power based on the remaining useful life distribution

Abstract

Prognosis of the Remaining Useful Life (RUL) of a unit or system plays an important role in system reliability analysis and maintenance decision making. One key aspect of the RUL prognosis is the construction of the best prediction interval for failure occurrence. The interval should have a reasonable length and yield the best prediction power. In current practice, the center-based interval and traditional confidence interval are widely used. Although both are easy to construct, they do not provide the best prediction performance. In this article, we propose a new scheme, the Maximum Power Interval (MPI), for estimating the interval with maximum prediction power. The MPI guarantees the best prediction power under a given interval length. Some technical challenges involved in the MPI method were resolved using the maximum entropy principle and truncation method. A numerical simulation study confirmed that the MPI has better prediction power than other prediction intervals. A case study using a real industry data set was conducted to illustrate the capability of the MPI method.

Keywords:

• Remaining useful life
• interval prediction
• maximum entropy
• prediction power

Additional information

Notes on contributors

Junbo Son

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

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 M.S. in Mechanical Engineering (2007) from Tsinghua University, China; and an M.S. in Statistics (2010) and 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.

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. degrees 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 industries. 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.

Mutasim Salman

Mutasim Salman is a Lab Group manager and a Technical Fellow in the Electrical, Controls and Integration Laboratory of the 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 University of Illinois at Urbana–Champaign. He has the responsibility of development and validation of algorithms for state of health monitoring, diagnosis, prognosis, and fault-tolerant control of a vehicle's critical systems. He has an extensive experience in hybrid vehicles, modeling, and control and energy management strategies. He has several GM awards, including four GM prestigious Boss Kettering, three McCuen, and two President and Chairman Awards.