Weak fault feature extraction of rolling bearing based on minimum entropy de-convolution and sparse decomposition

Abstract

The rolling bearing fault signal under strong background noise is very weak because of environmental noise impaction and the attenuation of signal. The feature extraction of rolling bearings’ weak fault is very important in avoiding serious disaster, but it is also very difficult. Sparse decomposition has been used in the fault feature extraction of rolling bearings. But its performance is very poor when the background noise is very strong. This text combines the minimum entropy de-convolution (MED) and sparse decomposition to extract the feature of a rolling bearing’s weak fault. Firstly, the rolling bearing weak fault signal with strong background noise is de-noised using the MED method, and subsequently the de-noised signal is handled by sparse decomposition. Finally, the fault feature extraction method-envelope demodulation is carried on the last given signal and better results are obtained. In conclusion, through simulation and experiment the effectiveness and the feasibility of the proposed method are verified.