Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections
Liu, Bin and Zhao, Xiujie and Liu, Yiqi and Do, Phuc (2021) Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections. International Journal of Production Research, 59 (24). pp. 7537-7559. ISSN 0020-7543 (https://doi.org/10.1080/00207543.2020.1844919)
Preview |
Text.
Filename: Liu_etal_IJPR_2020_Maintenance_optimisation_for_systems_with_multi_dimensional_degradation_and_imperfect.pdf
Accepted Author Manuscript Download (650kB)| Preview |
Abstract
In this paper, we develop a maintenance model for systems subjected to multiple correlated degradation processes, where a multivariate stochastic process is used to model the degradation processes, and the covariance matrix is employed to describe the interactions among the processes. The system is considered failed when any of its degradation features hits the pre-specified threshold. Due to the dormancy of degradation-based failures, inspection is implemented to detect the hidden failures. The failed systems are replaced upon inspection. We assume an imperfect inspection, in such a way that a failure can only be detected with a specific probability. Based on the degradation processes, system reliability is evaluated to serve as the foundation, followed by a maintenance model to reduce the economic losses. We provide theoretical boundaries of the cost-optimal inspection intervals, which are then integrated into the optimisation algorithm to relieve the computational burden. Finally, a fatigue crack propagation process is employed as an example to illustrate the effectiveness and robustness of the developed maintenance policy. Numerical results imply that the inspection inaccuracy contributes significantly to the operating cost and it is suggested that more effort should be paid to improve the inspection accuracy.
ORCID iDs
Liu, Bin ORCID: https://orcid.org/0000-0002-3946-8124, Zhao, Xiujie, Liu, Yiqi and Do, Phuc;-
-
Item type: Article ID code: 75442 Dates: DateEvent17 December 2021Published1 December 2020Published Online14 October 2020AcceptedSubjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management Department: Strathclyde Business School > Management Science Depositing user: Pure Administrator Date deposited: 16 Feb 2021 13:00 Last modified: 15 Dec 2024 01:32 URI: https://strathprints.strath.ac.uk/id/eprint/75442