Santos, Augusto, Cavalcante, Cristiano, Ren, Junru, Wu, Shaomin (2023) A novel delay time modelling method for incorporating reuse actions in three-state single-component systems. Reliability Engineering & System Safety, . ISSN 0951-8320. (doi:10.1016/j.ress.2023.109129) (KAR id:99871)
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Language: English
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Author's Accepted Manuscript
Language: English DOI for this version: 10.22024/UniKent/01.02.99871.3359733
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Official URL: https://doi.org/10.1016/j.ress.2023.109129 |
Abstract
This paper presents a new delay time modelling method for reusing single-component systems with two defective states and one failure state. It assumes that a component may be reused for the purposes of resource, economic and environmental sustainability. The possibility of reusing industrial components is not generally considered in maintenance models, which represents a knowledge gap in the literature, especially in the delay time related models. To address this gap, this paper proposes a method based on the delay time modelling method to investigate different scenarios of component reusability and uses real-world systems in the mining industry to illustrate its applicability. The paper then derives the expected cost rate, obtains lower and upper bounds of the expected total cost, considers the improving learning rate of correctly classifying defective components and incorporates the environmental impact of disposed components in optimization of the inspection interval. Results discuss when the reuse action may provide economic benefits even when the reused item may have different reliability than new one.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ress.2023.109129 |
Additional information: | For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. |
Uncontrolled keywords: | reuse of deteriorating components; delay time; component heterogeneity; misclassification problem; cone crusher equipment |
Subjects: | H Social Sciences > HA Statistics > HA33 Management Science |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Funders: |
Economic and Social Research Council (https://ror.org/03n0ht308)
University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Shaomin Wu |
Date Deposited: | 03 Feb 2023 09:32 UTC |
Last Modified: | 04 Mar 2024 16:47 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/99871 (The current URI for this page, for reference purposes) |
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