One-level Stationary Wavelet Packet Transform & Hilbert Transform based Rolling Bearing Fault Diagnosis

Rolling bearing fault diagnosis has received considerable attention for many years. This paper introduces a vibration-based fault diagnosis method. First, the one-level stationary wavelet packet transform (one-level SWPT) is selected to decompose a vibration signal into sub-band signals, whose instantaneous-frequency is calculated by Hilbert transform. According to the theoretically calculated fault characteristic frequency, the method adaptively selects the signal decomposition path to capture the bearing fault characteristic components. Finally, the performance of the proposed method is tested by the measured rolling bearing fault vibration signal. The results show that this method improves the simplicity and effectiveness of fault feature extraction and fault diagnosis.