Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model

Liu, Bin and Liang, Zhenglin and Parlikad, Ajith Kumar and Xie, Min and Kuo, Way (2017) Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model. Reliability Engineering and System Safety, 168. pp. 200-209. ISSN 0951-8320 (https://doi.org/10.1016/j.ress.2017.04.010)

[thumbnail of Liu-etal-RESS-2017-Condition-based-maintenance-for-systems-with-aging-and-cumulative-damage]
Preview
Text. Filename: Liu_etal_RESS_2017_Condition_based_maintenance_for_systems_with_aging_and_cumulative_damage.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (941kB)| Preview

Abstract

This paper develops a condition-based maintenance (CBM) policy for systems subject to aging and cumulative damage. The cumulative damage is modeled by a continuous degradation process. Different from previous studies which assume that the system fails when the degradation level exceeds a specific threshold, this paper argues that the degradation itself does not directly lead to system failure, but increases the failure risk of the system. Proportional hazards model (PHM) is employed to characterize the joint effect of aging and cumulative damage. CBM models are developed for two cases: one assumes that the distribution parameters of the degradation process are known in advance, while the other assumes that the parameters are unknown and need to be estimated during system operation. In the first case, an optimal maintenance policy is obtained by minimizing the long-run cost rate. For the case with unknown parameters, periodic inspection is adopted to monitor the degradation level of the system and update the distribution parameters. A case study of Asphalt Plug Joint in UK bridge system is employed to illustrate the maintenance policy.

ORCID iDs

Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124, Liang, Zhenglin, Parlikad, Ajith Kumar, Xie, Min and Kuo, Way;