Rolling bearing faults severity classification using a combined approach based on multi-scales principal component analysis and fuzzy technique

Safety and fault diagnosis of rotating machinery play an important role in industrial systems. The reliability of the diagnosis performances is mainly linked to the signal processing tools used in the analysis phase. In this paper, a new method is proposed for the classification of rolling defects combining the optimized wavelet multi-resolution analysis (OWMRA), principal component analysis (PCA), and neuro-fuzzy. The OWMRA is performed to decompose the measured vibration signals in different frequency bands and extract additional information about the defect. The decomposition levels obtained from optimized WMRA are then used as the input of the PCA method. The extraction of individual feature sets including time domain features is generated to disclose health conditions of the bearing. Hence, the proposed classification method of neuro-fuzzy based on multi-scale principal component analysis (MSPCA) applies to real signals to analyze several types of defects on the bearings’ ball, outer race and inner race with variable fault diameter; load; and motor speed. The obtained results prove the reliability of the proposed diagnosis method to classify three types of bearing faults, and to give a better classification with greater efficiency compared to the application of individual classifiers or the artificial neural networks (ANN) alone. Finally, The effectiveness of our approach has been proven in terms of the successful classification rate compared on the one hand with two classification algorithms and two sets of features and on the other hand with the time domain method based on different choices of features.