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Reliability analysis for preventive maintenance based on classical and Bayesian semi-parametric degradation approaches using locomotive wheel-sets as a case study

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  • Lin, Jing
  • Pulido, Julio
  • Asplund, Matthias

Abstract

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).

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  • Lin, Jing & Pulido, Julio & Asplund, Matthias, 2015. "Reliability analysis for preventive maintenance based on classical and Bayesian semi-parametric degradation approaches using locomotive wheel-sets as a case study," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 143-156.
    • Handle: RePEc:eee:reensy:v:134:y:2015:i:c:p:143-156
    DOI: 10.1016/j.ress.2014.10.011
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    References listed on IDEAS

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    1. Doostparast, Mohammad & Kolahan, Farhad & Doostparast, Mahdi, 2014. "A reliability-based approach to optimize preventive maintenance scheduling for coherent systems," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 98-106.
    2. Liu, Yongming & Liu, Liming & Stratman, Brant & Mahadevan, Sankaran, 2008. "Multiaxial fatigue reliability analysis of railroad wheels," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 456-467.
    3. Musharraf, Mashrura & Bradbury-Squires, David & Khan, Faisal & Veitch, Brian & MacKinnon, Scott & Imtiaz, Syed, 2014. "A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 1-8.
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    Cited by:

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    2. Bodunrin Brown & Bin Liu & Stuart McIntyre & Matthew Revie, 2023. "Reliability evaluation of repairable systems considering component heterogeneity using frailty model," Journal of Risk and Reliability, , vol. 237(4), pages 654-670, August.
    3. Zhou, Chongwen & Chinnam, Ratna Babu & Dalkiran, Evrim & Korostelev, Alexander, 2017. "Bayesian approach to hazard rate models for early detection of warranty and reliability problems using upstream supply chain information," International Journal of Production Economics, Elsevier, vol. 193(C), pages 316-331.
    4. Feng, Qiang & Zhao, Xiujie & Fan, Dongming & Cai, Baoping & Liu, Yiqi & Ren, Yi, 2019. "Resilience design method based on meta-structure: A case study of offshore wind farm," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 232-244.
    5. Hongming Zhou & Sufen Wang & Faqun Qi & Shun Gao, 2022. "Maintenance modeling and operation parameters optimization for complex production line under reliability constraints," Annals of Operations Research, Springer, vol. 311(1), pages 507-523, April.
    6. Fu, Yuqiang & Yuan, Tao & Zhu, Xiaoyan, 2019. "Importance-measure based methods for component reassignment problem of degrading components," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    7. Yu Zhang & Jiawen Zhang & Lin Luo & Xiaorong Gao, 2019. "Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    8. Wang, Jinting & Zhou, Zhuang & Peng, Hao, 2017. "Flexible decision models for a two-dimensional warranty policy with periodic preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 14-27.
    9. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    10. Slimacek, Vaclav & Lindqvist, Bo Henry, 2016. "Nonhomogeneous Poisson process with nonparametric frailty," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 14-23.
    11. Edson Ruschel & Eduardo Alves Portela Santos & Eduardo de Freitas Rocha Loures, 2020. "Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 53-72, January.

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