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European Journal of Operational Research
Volume 174, Issue 1, 1 October 2006, Pages 112-123
 
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doi:10.1016/j.ejor.2005.03.010    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

Stochastics and Statistics

An application of DPCA to oil data for CBM modeling

Viliam MakisCorresponding Author Contact Information, a, E-mail The Corresponding Author, Jianmou Wua and Yan Gaoa

aDepartment of Mechanical and Industrial Engineering, 5 King’s College Road, University of Toronto, Toronto, Ont., Canada M5S 3G8

Received 16 November 2004; 
accepted 1 March 2005. 
Available online 13 June 2005.

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Abstract

In multivariate time series analysis, dynamic principal component analysis (DPCA) is an effective method for dimensionality reduction. DPCA is an extension of the original PCA method which can be applied to an autocorrelated dynamic process. In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). It is shown that the average maintenance cost corresponding to the optimal policy for Model 1 is considerably lower than that for Model 2, and when the optimal policies are applied to the oil data histories, the policy for Model 1 correctly indicates almost twice as many impending system failures as the policy for Model 2.

Keywords: Maintenance; Multivariate statistics; Replacement; Dynamic principal component analysis; Proportional hazards model

Article Outline

1. Introduction
2. The proportional hazards model and oil data description
3. Time series modeling of the oil data
4. Application of DPCA to oil data
5. CBM modeling and model comparison
5.1. Introduction of the CBM software EXAKT
5.2. CBM model building using DPCA covariates
5.3. CBM model comparison
6. Conclusions
Acknowledgements
References


 
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