Reliability bounds for multi-state systems by fusing multiple sources of imprecise information

Abstract

It is crucial to evaluate reliability measures of a system over time, so that reliability-related decisions, such as maintenance planning and warranty policy, can be appropriately made for the system. However, accurately assessing system reliability becomes challenging if only limited amounts of reliability data are available. On the other hand, imprecise information related to reliability measures of a system can be collected based on experts’ judgments/experiences, and these pieces of information may be, however, heterogeneous and come from multiple sources. By properly fusing the imprecise information, reliability bounds of a system can be assessed to facilitate the ensuing reliability-related decision-making. In this article, a constrained optimization framework is proposed to assess reliability bounds of multi-state systems by fusing multiple sources of imprecise information. The proposed framework is composed of three basic steps: (i) constructing a set of constraints for a resulting optimization formulation by representing all the imprecise information as functions of unknown parameters of the degradation models for components; (ii) identifying the upper and lower bounds of the system reliability function by resolving the resulting constrained optimization problem via a tailored feasibility-based particle swarm algorithm; and (iii) developing a model selection approach to choose the best component degradation model that matches with all the imprecise information to the maximum extent. A numerical example along with an engineering example is given to demonstrate the effectiveness of the proposed method.

Keywords:

• Multi-state system
• multiple sources of imprecise information
• reliability bounds
• imprecise information fusion
• model selection

Additional information

Funding

The authors acknowledge the grant support received from the National Natural Science Foundation of China under contract numbers 71771039 and 71922006.

Notes on contributors

Tangfan Xiahou

Tangfan Xiahou received his B.E. degree in industrial engineering in 2015 and M.Sc. degree in mechanical engineering in 2018, from the University of Electronic Science and Technology of China, Chengdu, China, where he is currently working towards a Ph.D. degree in mechanical engineering with the School of Mechanical and Electrical Engineering. His research interests include system reliability under uncertainty and Dempster–Shafer evidence theory.

Yu Liu

Yu Liu is a professor of industrial engineering with the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China. He received a Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2010. He was a visiting predoctoral fellow with the Department of Mechanical Engineering, Northwestern University, USA, from 2008 to 2010 and a postdoctoral research fellow with the Department of Mechanical Engineering, University of Alberta, Canada, from 2012 to 2013. He has authored or coauthored more than 50 peer reviewed articles in international journals and conferences. His research interests include system reliability modeling and analysis, maintenance decisions, prognostics and health management, and design under uncertainty. He is a Senior Member of IEEE and IISE.