Hidden Markov Models and Gaussian Mixture Models for Bearing Fault Detection Using Fractals

Abstract

Bearing vibration signals features are extracted using time domain fractal based feature extraction technique. This technique uses multi-scale fractal dimension (MFD) estimated using box-counting dimension. The extracted features are then used to classify faults using Gaussian mixture models (GMM) and hidden Markov models (HMM). The results obtained show that the proposed feature extraction technique does extract fault specific information. Furthermore, the experimentation shows that HMM outperforms GMM. However, the disadvantage of HMM is that it is computationally expensive to train compared to GMM. It is therefore concluded that the proposed framework gives enormous improvement to the performance of the bearing fault detection and diagnosis, but it is recommended to use the GMM classifier when time is the major issue.