Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 174, Issue 1, Pages 112-123Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2005.03.010
Keywords
maintenance; multivariate statistics; replacement; dynamic principal component analysis; proportional hazards model
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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. (c) 2005 Elsevier B.V. All rights reserved.
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