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A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information

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  • M J Carr
  • W Wang

Abstract

The ability to predict the expected time remaining before a component fails is crucial when scheduling maintenance activities and component replacements. The current paper presents a comparison of the proportional hazards model and a probabilistic filtering approach when applied to the estimation of a components residual life using stochastically related oil-based wear information. The condition information is collected at irregular monitoring points from aircraft engines and consists of the concentrations of various contaminating metallic particles in an oil sample. Issues regarding the use of multiple information parameters are also addressed.

Suggested Citation

  • M J Carr & W Wang, 2008. "A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information," Journal of Risk and Reliability, , vol. 222(1), pages 47-55, March.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:1:p:47-55
    DOI: 10.1243/1748006XJRR76
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    References listed on IDEAS

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    1. P J Vlok & J L Coetzee & D Banjevic & A K S Jardine & V Makis, 2002. "Optimal component replacement decisions using vibration monitoring and the proportional-hazards model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 193-202, February.
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    Cited by:

    1. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    2. 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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