This paper presents an innovative hybrid approach that combines Hidden Markov Models (HMM) with Radial Basis Function Neural Networks (RBFNN) for the automatic classification of mechanical faults in rolling element bearings using vibration signal analysis. The signals are sourced from the well-established database ( https://engineering.case.edu/bearingdatacenter/welcome ), widely used in fault diagnosis research. Raw signals are preprocessed to extract relevant features across time, frequency, and time–frequency domains, including wavelet packet decomposition. To enhance classification robustness and reduce computational complexity, dimensionality reduction is performed using Principal Component Analysis (PCA), complemented by Fisher score-based feature selection. HMMs are trained to capture the temporal dynamics of the signals, while RBFNNs leverage the reduced feature space for fine-grained classification. A comprehensive performance comparison is conducted between standalone HMM and RBFNN models, as well as their integration within the hybrid HMM-RBFNN system. Experimental results demonstrate that the proposed hybrid method significantly improves classification accuracy, highlighting its potential for industrial predictive maintenance applications.
Sedira et al. (Thu,) studied this question.