Reliability Analysis for Preventive Maintenance based on Classical and Bayesian Semi-parametric Degradation Approaches using Locomotive Wheel-sets as a Case Study
2015 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 134, 143-156 p.Article in journal (Refereed) Published
This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both Accelerated Life Tests (ALT) and Design of Experiments (DOE) technology are used to determine how each critical factor affects the prediction of performance. With the Bayesian semi-parametric approach, a piecewise constant hazard regression model is used to establish the lifetime using degradation data. Gamma frailties are included to explore the influence of unobserved covariates within the same group. Ideally, results from the classical and Bayesian approaches will complement each other. To demonstrate these approaches, this paper considers a case study of locomotive wheel-set reliability. The degradation data are prepared by considering an Exponential and a Power degradation path separately. The results show that both classical and Bayesian semi-parametric approaches are useful tools to analyse degradation data and can, therefore, support a company in decision making for preventive maintenance. The approach can be applied to other technical problems (e.g. other industries, other components).
Place, publisher, year, edition, pages
2015. Vol. 134, 143-156 p.
Research subject Operation and Maintenance
IdentifiersURN: urn:nbn:se:ltu:diva-11964DOI: 10.1016/j.ress.2014.10.011Local ID: b0416d41-d8b3-4248-ac9f-c0e7424055adOAI: oai:DiVA.org:ltu-11964DiVA: diva2:984914
Validerad; 2015; Nivå 2; 20141019 (linjan)2016-09-292016-09-29Bibliographically approved