Condition-based maintenance for multi-component systems: Modeling, structural properties, and algorithms

Abstract

Condition-Based Maintenance (CBM) is an effective maintenance strategy to improve system performance while lowering operating and maintenance costs. Real-world systems typically consist of a large number of components with various interactions among components. However, existing studies on CBM mainly focus on single-component systems. Multi-component CBM, which joins the components’ stochastic degradation processes and the combinatorial maintenance grouping problem, remains an open issue in the literature. In this article, we study the CBM optimization problem for multi-component systems. We first develop a multi-stage stochastic integer model with the objective of minimizing the total maintenance cost over a finite planning horizon. We then investigate the structural properties of a two-stage model. Based on the structural properties, two efficient algorithms are designed to solve the two-stage model. Algorithm 1 solves the problem to its optimality and Algorithm 2 heuristically searches for high-quality solutions based on Algorithm 1. Our computational studies show that Algorithm 1 obtains optimal solutions in a reasonable amount of time and Algorithm 2 can find high-quality solutions quickly. The multi-stage problem is solved using a rolling horizon approach based on the algorithms for the two-stage problem. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc.

Keywords:

• Condition-based maintenance
• multi-component systems
• multi-stage stochastic integer programming
• endogenous uncertainty

Additional information

Funding

This work was supported in part by the U.S. National Science Foundation under Award 1855408.

Notes on contributors

Zhicheng Zhu

Zhicheng Zhu is a Ph.D student in Department of Industrial, Manufacturing & Systems Engineering at Texas Tech University. He received his B.S. and M.S. in Electrical Engineering from Sun Yat-sen University, China. His main research interests are maintenance optimization, decision-making under uncertainty, and reliability modeling.

Yisha Xiang

Dr. Yisha Xiang is an Assistant Professor in the Department of Industrial, Manufacturing & Systems Engineering at Texas Tech University. Her current research and teaching interests involves reliability modeling and optimization, maintenance optimization, and decision-making under uncertainty. Her research has been funded by National Science Foundation, including a CAREER grant, and industry. She has published articles in refereed journals, such as IISE Transactions, European Journal of Operational Research, and IEEE Transactions on Reliability. She was the recipient of the Ralph A. Evans/P.K. McElroyy Award for the best paper at the 2013 Reliability and Maintainability Symposium, and Stan Oftshun Best Paper Award from Society of Reliability Engineers in 2013 and 2017. She received her B.S. in Industrial Engineering from Nanjing University of Aero. & Astro., China, and M.S and Ph.D. in Industrial Engineering from University of Arkansas. She is an Associate Editor for IEEE Transactions Automation Science and Engineering, and she is a member of IISE and INFORMS.