A reliability approach for prediction and management of part obsolescence for improved system health

Abstract

Accurate prediction of part obsolescence is critical to maintaining system health, especially for the long-lived systems typical in aerospace and naval domains. While there are methods that predict an expected date of obsolescence, a numerical likelihood of obsolescence can be useful. This work describes a Weibull-based conditional probability method for the prediction of part-level obsolescence risk. Several considerations inherent to the problem environment and using a probabilistic method to estimate risk are discussed and addressed, including accounting for changing product life, using dynamic binning and Weibull regression; sample bias, through data cleaning; and small datasets with potentially highly censored data, using a modified synthetic minority oversampling technique (SMOTE) that can sample both the minority and majority classes. Development of an approximate measure of uncertainty of obsolescence is also presented. Use of the method is demonstrated with a multiplexer dataset and shows the feasibility of the approach.

Keywords:

• conditional Weibull probability distribution
• health monitoring
• obsolescence risk prediction
• synthetic minority oversampling technique (SMOTE)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partially supported by the United States Office of Naval Research (ONR) under grant number N00024-10-D-6318; and the Naval Sea Systems Command (NAVSEA) Naval Engineering Education Consortium (NEEC) under grant number N00174-20-1-0005.

Notes on contributors

Christina M. Mastrangelo

Christina M. Mastrangelo is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Washington. Christina received the B.S. degree, M.S. degree, and Ph.D. degree in Industrial Engineering at Arizona State University, AZ, USA. Her research focuses on empirical stochastic modeling and predictive analytics. She is a member of ASA, INFORMS, and a senior member of IISE. Her email address is [email protected].

Kara A. Olson

Kara A. Olson is a Research Scientist/Engineer in the Department of Industrial and Systems Engineering at the University of Washington. She holds a Doctorate in Computer Science from Old Dominion University, Norfolk, Virginia, as well as a Master of Science, Computer Science, Bachelor of Computer Science, and Bachelor of Science, Mathematics from Old Dominion. Her research interests include analysis of simulation models in order to enhance understanding as well as predictive analytics. She is a member of ACM and IEEE/CS. Her email address is [email protected].