An optimal maintenance strategy for multi-state systems based on a system linear integral equation and dynamic programming

Abstract

An optimal preventive maintenance strategy for multi-state systems based on an integral equation and dynamic programming is described herein. Unlike traditional preventive maintenance strategies, this maintenance strategy is formulated using an integral equation, which can capture the system dynamics and avoid the curse of dimensionality arising from complex semi-Markov processes. The linear integral equation of the system is constructed based on the system kernel. A numerical technique is applied to solve this integral equation and obtain all of the mean elapsed times from each reliable state to each unreliable state. An analytical approach to the optimal preventive maintenance strategy is proposed that maximizes the expected operational time of the system subject to the total maintenance budget based on dynamic programming in which both backward and forward search techniques are used to search for the local optimal solution. Finally, numerical examples concerning two different scales of systems are presented to demonstrate the performance of the strategy in terms of accuracy and efficiency. Moreover a sensitivity analysis is provided to evaluate the robustness of the proposed strategy.

Stochastic comparisons of parallel systems with scale proportional hazards components equipped with starting devices

“…For example, a power plant gets its reliability and availability by gas turbine start-up procedure, the maximum time length needed for an on-line conference depends on number of participants successfully registered the conference, and so on. Such random extremes play an important role in financial economics, reliability theory, actuarial science, auction theory, hydrology and many other areas; see, for example, [2], [3], [4], [5], [6], and [8].…”

Bayesian decision making in determining optimal leased term and preventive maintenance scheme for leased facilities

“…Since the defective products originated from improper PM under uncertainty environment, the decision makers need to improve PM scheduling for optimizing production lines. Jin et al (2020) [24] proposed an optimal PM strategy for multi-state systems by using dynamic programming. Unlike traditional PM strategies, the PM strategy is formulated using an integral equation and avoid the difficulty of complex semi-Markov processes.…”