A novel replacement policy for a linear deteriorating system using stochastic process with dependent components

Abstract

In this study, a mechanical system with linear deterioration and preventive maintenance is considered. The state of the system over time is represented by a semicontinuous stochastic process with dependent components. The system cycles through on and off periods during its lifetime. The state of the system deteriorates linearly as a function of the usage time during on periods. When the system is offline, preventive maintenance is conducted, which improves the system state by a random amount. The system's on and off times and random improvement amounts are assumed to have general distributions. For such a system, our objective is to determine the expected value and variance for the number of preventive maintenance activities needed during the system lifetime and to propose a novel replacement policy for the system based on delay‐time modeling. Finally, the effectiveness of the obtained asymptotic results and the proposed replacement policy are tested through simulation.

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

“…The results in this article are still limited, and diverse extensions of the modeling and analysis would emerge:1.Our model can be combined into problems involving both monitoring and replacement encountered in industrial problems like replacement of deteriorating systems 80,81 …”