A Dynamic Maintenance Strategy for Prognostics and Health Management of Degrading Systems: Application in Locomotive Wheel-sets
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]
This paper develops a dynamic maintenance strategy for prognostics and health management (PHM) of a degrading system. The system under investigation suffers a continuous degradation process, modeled as a Gamma process. In addition to the degradation process, the system is subject to aging, which contributes to the increase of failure rate. An additive model is employed to describe the impact of degradation level and aging on system failure rate. Inspection is implemented upon the system so as to effectively avoid failure. At inspection, the system will be repaired or replaced in terms of the degradation level. Different from previous studies which assume that repair will always lead to an improvement on system degradation, in our study, however, the effect of repair is twofold. It will reduce the system age to 0 but will increase the degradation level. System reliability is analyzed as a first step to serve for the maintenance decision making. Based on the reliability evolution, a maintenance model is formulated with respect to the inspection time. The optimal decision is achieved by minimizing the expected cost rate in one repair cycle. Finally, a case study of locomotive wheel-sets is adopted to illustrate the effectiveness of the proposed model. Our approach incorporates the joint influence of aging and degradation process, and determines the optimal inspection time dynamically, which exhibits the advantage of flexibility and can achieve better performance in field use.
Place, publisher, year, edition, pages
IEEE, 2018.
Keywords [en]
prognostics & health management (PHM), maintenance strategy optimization, reliability analysis, degradation process, railway transportation
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-71047DOI: 10.1109/ICPHM.2018.8448740ISI: 000539546400050Scopus ID: 2-s2.0-85062146671ISBN: 978-1-5386-1165-4 (electronic)OAI: oai:DiVA.org:ltu-71047DiVA, id: diva2:1252193
Conference
IEEE 9th International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA, 11-13 June 2018.
2018-10-012018-10-012021-08-24Bibliographically approved