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A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering

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  • Wang, Wenbin
  • Hussin, B.
  • Jefferis, Tim

Abstract

This paper presents a case study of condition based maintenance modelling based on measured metal concentrations observed in oil samples of a fleet of marine diesel engines. The decision model for optimising the replacement time of the diesel engines conditional on observed measurements is derived and applied to the case discussed. We described the datasets, which were cleaned and re-organised according to the need of the research. The residual time distribution required in the decision model was formulated using a technique called stochastic filtering. Procedures for model parameter estimation are constructed and discussed in detail. The residual life model presented has been fitted to the case data, and the modelling outputs are discussed.

Suggested Citation

  • Wang, Wenbin & Hussin, B. & Jefferis, Tim, 2012. "A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering," International Journal of Production Economics, Elsevier, vol. 136(1), pages 84-92.
  • Handle: RePEc:eee:proeco:v:136:y:2012:i:1:p:84-92
    DOI: 10.1016/j.ijpe.2011.09.016
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    References listed on IDEAS

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    1. Al-Najjar, Basim, 2007. "The lack of maintenance and not maintenance which costs: A model to describe and quantify the impact of vibration-based maintenance on company's business," International Journal of Production Economics, Elsevier, vol. 107(1), pages 260-273, May.
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    Cited by:

    1. Mengyao Gu & Youling Chen, 2019. "Two improvements of similarity-based residual life prediction methods," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 303-315, January.
    2. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    3. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    4. Zhu, Qiushi & Peng, Hao & Timmermans, Bas & van Houtum, Geert-Jan, 2017. "A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs," International Journal of Production Economics, Elsevier, vol. 193(C), pages 365-380.
    5. Yiwei Wang & Christian Gogu & Nicolas Binaud & Christian Bes & Raphael T Haftka & Nam-Ho Kim, 2018. "Predictive airframe maintenance strategies using model-based prognostics," Journal of Risk and Reliability, , vol. 232(6), pages 690-709, December.
    6. Junyu Guo & Hong-Zhong Huang & Weiwen Peng & Jie Zhou, 2019. "Bayesian information fusion for degradation analysis of deteriorating products with individual heterogeneity," Journal of Risk and Reliability, , vol. 233(4), pages 615-622, August.
    7. Wang, Zhaoqiang & Hu, Changhua & Wang, Wenbin & Zhou, Zhijie & Si, Xiaosheng, 2014. "A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 186-195.

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