Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model

The behavior characteristics of the speed-up and speed-down process of rotating machinery possess the distinct diagnostic value. The abundant information, non-stationarity, poor repeatability and reproducibility in this process lead to the necessity to find the corresponding method of feature extraction and fault recognition. In this paper, combining independent component analysis (ICA) and Factorial Hidden Markov Model (FHMM), a new method of the fault recognition named ICA–FHMM is proposed. In the proposed method, ICA is used for the redundancy reduction and feature extraction of the multi-channel detection, and FHMM as a classifier to recognize the faults of the speed-up and speed-down process in rotating machinery. This method is compared with another recognition method named ICA–HMM, in which ICA is similarly used for the feature extraction, however Hidden Markov Model (HMM) as a classifier. Experimental results show that the proposed method is very effective, and the ICA–FHMM recognition method is superior to the ICA–HMM recognition method.