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

D Lin (), D Banjevic () and A K S Jardine ()
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D Lin: University of Toronto
D Banjevic: University of Toronto
A K S Jardine: University of Toronto

Journal of the Operational Research Society, 2006, vol. 57, issue 8, 910-919

Abstract: Abstract This paper proposes the application of a principal components proportional hazards regression model in condition-based 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.

Keywords: condition-based maintenance; optimal maintenance policy; principal component analysis; proportional hazards model; hazard regression (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (11)

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DOI: 10.1057/palgrave.jors.2602058

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