An application of DPCA to oil data for CBM modeling |
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| Institution: | 1. Department of Neurology, University Medical Centre Ljubljana, Zaloska 2, 1000 Ljubljana, Slovenia;2. Sobell Department of Motor Neuroscience & Movement Disorders, UCL, Institute of Neurology, 33 Queen Square, London, WC1N 3BG, United Kingdom;3. Department of Clinical Psychology, Trinity College Dublin, University of Dublin, Dublin 2, Ireland;4. Department of Clinical Psychology, University College Dublin, Stillorgan Rd, Belfield, Dublin 4, Ireland;1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China;2. Department of Industrial Engineering, Tsinghua University, Beijing 10084, China;1. Signals and Systems Laboratory, Institute of Electrical and Electronics Engineering, University M’Hamed Bougara of Boumerdes, Avenue of Independence, 35000, Boumerdes, Algeria;2. Systems and Advanced Material Laboratory, Badji Mokhtar - Annaba University, BP 12, 23000, Annaba, Algeria;3. Ain El Kebira Cement Plant, SCAEK, BP 01, 19400, Ain El Kebira, Algeria |
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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. |
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