A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China;School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China;School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China;School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China;School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China;School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
Recommended Citation:
Huaqing Wang,Ruitong Li,Gang Tang,et al. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition[J]. PLOS ONE,2014-01-01,9(10)