Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery
It is very important to ensure that the large rotating machinery operates safely and reliably. The behaviour characteristics of the speed-up and speed-down process in a rotating machinery possess the distinct diagnostic value. The abundant information, non-stationarity, poor repeatability and reproducibility in the speed-up and speed-down process lead to the necessity to find the corresponding approach of feature extraction and fault recognition. The Hidden Markov model (HMM) is very suitable for modelling the dynamic time series, and has a strong capability of pattern classification, especially for a signal with abundant information, non-stationarity, poor repeatability and reproducibility. At the same time, HMM can process the random long sequences in theory. Based on these features, HMM is very suitable for the signal from the speed-up and speed-down process in rotating machinery. As a result, HMM is introduced to the fault diagnosis of rotating machinery, and a new HMM-based approach of the fault diagnosis for the speed-up and speed-down process is proposed. The main idea of the proposed approach is that the feature vectors, which are obtained by the FFT, wavelet transform, bispectrum, etc., are used as fault features, respectively, and the HMMs as the classifiers to recognise the faults of the speed-up and speed-down process in rotating machinery. The experimental results show that the proposed approach is feasible and effective.
speed-up and speed-down process rotating machinery hidden markov model (hmm) pattern classification fault diagnosis