Application of DPCA based stochastic filtering model and comparison of optimal CBM policies

This paper builds improved Stochastic Filtering Model (SFM) for Condition Based Maintenance (CBM) using covariates obtained from Dynamic Principal Component Analysis (DPCA) of oil data. DPCA covariates, derived from DPCA of the oil data, represent most of the variability in the original data with reduced dimension and little cross-correlation. This makes DPCA covariates ideal for maintenance modeling and decision making. Then, we test the correlation of selected DPCA covariates, define their states as Markov process, and build the SFM. A case study using oil data is also developed. The maintenance policy using DPCA covariates appears superior to the one using original variables.