Condition-based maintenance for multi-component systems using importance measure and predictive information

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101343

Title:	Condition-based maintenance for multi-component systems using importance measure and predictive information
Authors:	阮金英;Nguyen, Kim-Anh;Do, Phuc;Grall, Antoine
Contributors:	太空及遙測研究中心
Keywords:	Automatic;condition-based maintenance;Engineering Sciences;gamma process;importance measure;multi-component system;reliability prediction
Date:	2014-10-02
Issue Date:	2026-04-21 14:31:10 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Taylor & Francis
Abstract:	摘要： This paper presents a predictive condition-based maintenance strategy for multi-component systems whose structure may impact components deterioration process. To select components for preventive maintenance actions, a decision rule relying on both structural importance measure of components and their predictive reliability that can be estimated at inspection times is proposed. For corrective maintenance actions, an adaptive opportunistic maintenance decision rule taking into account both the criticality level of components and logistic support constraints is introduced. Moreover, both economic and structure dependencies between components are studied and integrated in maintenance model. A 12-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance strategy. Indeed, the proposed strategy provides more flexibility in maintenance decision-making, hence leading to significant profits in terms of maintenance cost when compared to existing strategies. 出版者： Taylor & Francis 出版日期： 2014-10-02 出處： International journal of systems science. Operations & logistics, 2014-10, Vol.1 (4), p.228-245 版權： 2014 Taylor & Francis 2014 版權： licence_http://creativecommons.org/publicdomain/zero 識別號： ISSN: 2330-2674 識別號： EISSN: 2330-2682 識別號： DOI: 10.1080/23302674.2014.983582
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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