Skip to main content
Kent Academic Repository

A novel data-driven approach to optimizing replacement policy

Ahmadi, Reza, Wu, Shaomin (2017) A novel data-driven approach to optimizing replacement policy. Reliability Engineering and System Safety, 167 . pp. 506-516. ISSN 0951-8320. (doi:10.1016/j.ress.2017.06.027) (KAR id:62142)


Parallel systems are a commonly used structure in reliability engineering. A common characteristic of such systems is that the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The paper assumes that times between inspections conform to a modulated Poisson process. This assumption allows the frequency of inspection responds to the variation of the disruption state. The paper then estimates the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Pareto or exponential distribution. Numerical results are provided to show the application of the proposed model.

Item Type: Article
DOI/Identification number: 10.1016/j.ress.2017.06.027
Uncontrolled keywords: Replacement; Inspection; Renewal-Reward; Optimization; Partial information; Filtering theorem.
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Shaomin Wu
Date Deposited: 26 Jun 2017 08:25 UTC
Last Modified: 09 Dec 2022 03:26 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.