Fault Prognostic Based on Hybrid Method of State Judgment and Regression

Abstract

Fault prognostic is one of the most important problems in equipment health management system. This paper presents a hybrid method of mixture of Gaussian hidden Markov model (MG-HMM) and fixed size least squares support vector regression (FS-LSSVR) for fault prognostic. The system is established based on three parts. The first part trains the MG-HMM and FS-LSSVR model. According to the known samples, several MG-HMM models can be learned based on expectation maximization (EM) algorithm. Then, the forward variables can be calculated based on these MG-HMM models. Based on these forward variables, the corresponding FS-LSSVR models are built. All the MG-HMM models and corresponding FS-LSSVR models are combined into a model library. The second part recognizes the unknown sample based on the model library. This part obtains the MG-HMM model and FS-LSSVR model by maximization likelihood calculation between the unknown sample and MG-HMM models. The third part of the system calculates the forward variables based on the MG-HMM obtained from the second part. These forward variables are inputted into the corresponding FS-LSSVR model to compute the remaining useful life (RUL) of the unknown sample. Finally, we carry out experiments on benchmark data set to verify the proposed method. The results illustrate the effectiveness of the hybrid method.