A GKPCA-NHSMM based methodology for accurate RUL prognostics of nonlinear mechanical system with multistate deterioration

Abstract

Remaining useful life (RUL) prognostics is a core problem in prognostics and health management (PHM). Accurate RUL prediction is crucial not only to the verification of mission goals but also to failure prevention and maintenance decision in a more effective and efficient manner. However, the substantial nonlinearity is one of most important challenges in deterioration modeling and RUL estimation of nonlinear mechanical system. An interesting contribution is the improvement of RUL prediction accuracy by the use of both greedy kernel principal components analysis (GKPCA) for dimensional reduction to extract feature from multi dimension data set of monitored nonlinear mechanical system and nonhomogeneous hidden semi-Markov model (NHSMM) to model the multistate deterioration process. A case study with the data set from turbofan engines is analyzed using the methodology, and by comparing the prediction accuracy with the previously linear PCA-NHSMM's, the result verifies the effectiveness (closer to actual RUL, earlier tracked health state, smaller boundary width) and efficiency(higher prognostics robustness) of the methodology.