4.7 Article

Maintenance Policies for Two-Unit Balanced Systems Subject to Degradation

Journal

IEEE TRANSACTIONS ON RELIABILITY
Volume 71, Issue 2, Pages 1116-1126

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2022.3167046

Keywords

Maintenance engineering; Degradation; Costs; Reliability; Inspection; Optimization; Correlation; Balanced systems; maintenance policy optimization; Markov decision process (MDP); multivariate degradation; stochastic degradation

Funding

  1. National Natural Science Foundation of China [72002149, 72032005, 71971181]

Ask authors/readers for more resources

In this article, a novel maintenance policy optimization method is proposed for systems with two balanced components. The method utilizes Markov decision process and dynamic programming algorithms to optimize the maintenance decisions, and examples are provided for illustration and insight.
In this article, we present a novel maintenance policy optimization method for systems with two balanced components. The components in the system are assumed to degrade over time according to a bivariate Wiener process. The maintenance actions aim at eliminating the differences of degradation levels of system components at the cost of aggravating the degradation. Utilizing the Markov decision process, the maintenance model is put forward under both the finite and the infinite planning horizons, from which we find the structural properties of the optimal policies. Backwards dynamic programming and value iteration algorithms are employed to optimize the maintenance decisions. Examples along with sensitivity analysis are presented to facilitate the illustration and insight attainment. We find that the maintenance policies are to a great extent regulated by the absolute degradation difference between the two components.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available