Using principal components in a proportional hazards model with applications in condition-based maintenance

Abstract

This paper proposes the application of a principal components proportional hazards regression model in conditionbased maintenance (CBM) optimization. The Cox proportional hazards model with time-dependent covariates is considered. Principal component analysis (PCA) can be applied to covariates (measurements) to reduce the number of variables included in the model, as well as to eliminate possible collinearity between the covariates. The main issues and problems in using the proposed methodology are discussed. PCA is applied to a simulated CBM data set and two real data sets obtained from industry: oil analysis data and vibration data. Reasonable results are obtained. Journal of the Operational Research Society (2006) 57, 910–919. doi:10.1057/palgrave.jors.2602058 Published online 31 August 2005

DOI: 10.1057/palgrave.jors.2602058

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Cite this paper

@article{Lin2006UsingPC, title={Using principal components in a proportional hazards model with applications in condition-based maintenance}, author={D. Lin and Dragan Banjevic and Andrew K. S. Jardine}, journal={JORS}, year={2006}, volume={57}, pages={910-919} }