Abstract
This paper presents a model and methodology for estimating the residual time of a plant item. This plant item can be an engine or any complex technical system monitored by a regularly spaced oil analysis programme. Typically in the oil samples taken, two groups of observed variables are available, namely, metal concentrations and variables related to the condition of the lubricant and contaminants. We term the former as internal variables and the latter as external variables. External variables are those that may cause the change of the underlying condition of the plant item and therefore the residual time, while internal variables are those variables that only reflect the residual time but cannot change it. We modelled both variables in an oil-based monitoring case, but the principle can be generalized to other monitoring situations. The main techniques used are stochastic filtering for residual time prediction and the maximum likelihood method for parameters estimation. The model established was fitted to the real data of marine diesel engines monitored by an oil analysis programme and the results are presented.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bagdonavicius V and Nikuliu M (2001). Accelerated Life Models: Modeling and Statistical Analysis. Chapman & Hall: New York.
Cox DR and Oakes D (1984). Analysis of Survival Data. Chapman & Hall: New York.
Edwards DJ, Holt GD and Harris FC (1998). Predictive maintenance techniques and their relevance to construction plant. J Qual Maint Eng 4(1): 25–37.
Fodor IK (2002). A survey of dimension reduction techniques. LLNL Technical Report, UCRL-ID-148494.
Hyvärinen A and Oja E (1997). A fast fixed-point algorithm for independent component analysis. Neural Comput 9: 1483–1492.
Jardine AKS, Ralston P, Reid N and Stafford J (1989). Proportional hazards analysis of diesel engine failure data. Qual Reliab Eng Int 5: 207–216.
Jolliffe IT (1986). Principal component analysis. Springer-Verlag: New York.
Krishnan V (1984). Nonlinear filtering and smoothing. John Wiley and Sons: New York.
Lennon M, Mercier G, Mouchot MC and Hubert-Moy L (2001). Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images. Proceedings of geoscience and remote sensing symposium, (IGARSS '01), Sydney, Australia. IEEE, Piscataway, vol. 6, pp. 2893–2895.
Makis V, Wu J and Gao Y (2006). An application of DPCA to oil data for CBM modelling. Euro J Opl Res 174: 112–123.
Vlok PJ, Coetzee JL, Banjevic D, Jardine AKS and Makis V (2002). Optimal component replacement decisions using vibration monitoring and the proportional-hazards model. J Opl Res Soc 53: 193–202.
Wang W (2002). A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA J Man Maths 13: 3–16.
Wang W (2003). Modelling condition monitoring intervals: a hybrid of simulation and analytical approaches. J Opl Res Soc 54: 273–282.
Wang W and Christer AH (2000). Towards a general condition based maintenance model for a stochastic dynamic system. J Opl Res Soc 51: 145–155.
Wang W and Zhang W (2005). A model to predict the residual life of aircraft engines based on oil analysis data. Nav Log Res 52: 276–284.
Acknowledgements
This research is partially supported by the Engineering and Physical Sciences Research Council (EPSRC, UK) under grant number EP/C54658X/1.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
We start with i=1 based on both p(x 0) and p(y i |x i +) are Weibull,
Generalizing to the ith term, 
can be written as
where
Rights and permissions
About this article
Cite this article
Wang, W., Hussin, B. Plant residual time modelling based on observed variables in oil samples. J Oper Res Soc 60, 789–796 (2009). https://doi.org/10.1057/palgrave.jors.2602621
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/palgrave.jors.2602621