A Fault Feature Detection Approach for Fault Diagnosis of Rolling Element Bearings Based on Redundant Second Generation Wavelet Packet Transform and Local Characteristic-Scale Decomposition

Fault vibration signals of rolling element bearings are usually multi-component modulation signals, how to detect and extract the fault feature of vibration signals has become a key issue for ensuring the safe operation of modern rotating machineries. A novel hybrid method based on Redundant Second Generation Wavelet Packet Transform (RSGWPT) and Local Characteristic-Scale Decomposition (LCD) for detecting and extracting the fault feature of vibration signals is proposed. The RSGWPT-LCD with two-stage screening processes based on the energy ratio is used to obtain a set of Desired Intrinsic Scale Components (ISCs). Applying the spectrum analysis on those desired ISCs generated by the proposed method, the fault characteristics are easily detected and extracted. The proposed approach is verified by simulation and practical bearing vibration signals under different conditions. The results show that the proposed approach is feasible and effective for the fault diagnosis of rolling element bearing.