A switching diffusion model for lifetime estimation in randomly varying environments

Abstract

This article presents a switching diffusion model for estimating the useful lifetime of a component that operates in a randomly varying environment. The component’s degradation process is unobservable; therefore, a signal of degradation is observed to estimate the environment parameters using a Markov chain Monte Carlo statistical procedure. These parameter estimates serve as key inputs to an analytical stochastic model that approximates the first passage time of the degradation process to a critical threshold. Several numerical examples involving simulated and real degradation data are presented to illustrate the quality of these approximations.

Keywords:

• Degradation-based reliability
• random environment

Additional information

Notes on contributors

John A. Flory

John A. Flory is an analyst with the System Readiness and Sustainment Technologies group at Sandia National Laboratory in Albuquerque, New Mexico. After serving 9 years as an officer in the U.S. Air Force, he earned a Ph.D. in Industrial Engineering (Operations Research) from the University of Pittsburgh. He is a professional member of IIE and INFORMS.

Jeffrey P. Kharoufeh

Jeffrey P. Kharoufeh is an Associate Professor in the Department of Industrial Engineering at the University of Pittsburgh. He holds a Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University. His primary research interest is the modeling, analysis, and control of stochastic systems with applications in reliability, queueing, and energy systems. He is a Senior Member of IIE, former Department Editor of IIE Transactions, and a professional member of the INFORMS Applied Probability Society.

Nagi Z. Gebraeel

Nagi Gebraeel is a Chandler Family Associate Professor in the Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He holds a Ph.D. in Industrial Engineering from Purdue University. His research interests include sensor-based prognostics, and degradation modeling, reliability engineerings, and maintenance operations and logistics. He is a Professional Member of IIE and the INFORMS Quality, Statistics and Reliability Society.