Feature Extraction for Weak Fault of Rolling Bearing Based on Hybrid Signal Processing Technique

As the core component and the most vulnerable part of mechanical equipment, The rolling bearing is prone to failure during the operation of the process. In order to ensure the safe operation of the entire system, it is important to extract fault features of early and weak fault signal which has a very lower signal-noise ratio. Aiming at this problem, a hybrid method that based on Higher-Density Dual-Tree Discrete Wavelet Packet(HDDWP), Improved Hilbert Huang Transform(IHHT) is proposed. First, using HDDWP to eliminate the high frequency components of the original signal to improve signal-noise radio. Then the original signal is decomposed into a set of the intrinsic mode functions(IMFs) through the Complete Ensemble Empirical Mode Decomposition(CEEMD), some useful IMFs were collected by using Cross-Correlation function, and then calculated the Marginal Spectrum by Hilbert Transform. The experimental results indicate that the proposed method can effectively extract the early and weak fault features, and thereby to show that the proposed methods are feasible and effective.