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Fault Diagnosis of Ball Bearings Using SVM Based on Artificial Fish Swarm Algorithm

A ball bearing is a very important component for a mechanical equipment, because the performance of mechanical equipment is influenced by the healthy status of the ball bearings directly. In this study, wavelet packet transform (WPT) and support vector machine (SVM) are studied on fault diagnosis of ball bearings. We analyze four different status of ball bearing based on the vibration signals of the motor, such as the normal condition, with defeat at the inner raceway, with failure ball elements, and with defeat at the outer raceway. First, the vibration signal is transferred by the WPT and the wavelet packet energy spectrums are used for feature extraction. Second, the SVM is used to classify the healthy status of the motor according the pattern recognition. The features obtained using wavelet packet energy spectrum are used to train the SVM for classification of the ball bearing's healthy status. To obtain the optimal parameters of the SVM, the PSO and AFSA are used to find the best parameters. Finally, external samples are input to the SVM for validation of the proposed method.