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A model to predict the residual life of aircraft engines based upon oil analysis data

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  • Wenbin Wang
  • Wenjuan Zhang

Abstract

This paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Suggested Citation

  • Wenbin Wang & Wenjuan Zhang, 2005. "A model to predict the residual life of aircraft engines based upon oil analysis data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 276-284, April.
    • Handle: RePEc:wly:navres:v:52:y:2005:i:3:p:276-284
    DOI: 10.1002/nav.20072
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    References listed on IDEAS

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    Cited by:

    1. David Vališ & Libor Žák & Ondřej Pokora, 2015. "Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data," Journal of Risk and Reliability, , vol. 229(1), pages 36-45, February.
    2. Akram Khaleghei & Viliam Makis, 2015. "Model parameter estimation and residual life prediction for a partially observable failing system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(3), pages 190-205, April.
    3. W Wang, 2011. "Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 185-197, June.
    4. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    5. Ying Liao & Yisha Xiang & Min Wang, 2021. "Health assessment and prognostics based on higher‐order hidden semi‐Markov models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 259-276, March.

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