Aiming at rolling bearings’ early fault features being weaker and difficult to extract, and the decomposition layer number k in VMD being too difficult to determine, the improved VMD method was proposed. Energy difference was taken as an evaluation parameter to adaptively determine the decomposing layer number k. Then, the improved VMD was combined with the envelope derivative operator, a rolling bearing early fault feature diagnosis method was proposed. Firstly, a rolling bearing’s original fault vibration signal was decomposed with the VMD. According to the energy difference curve, the optimal value of k was determined. Secondly, according to the kurtosis criterion, sensitive components were selected from k IMFs obtained with decomposition to reconstruct a signal. The reconstructed signal was demodulated and analyzed with the envelope derivative operator. The rolling bearing’s fault feature information was extracted correctly from the energy spectrum of the reconstructed signal. Through analyzing simulated signals and test data, the validity and feasibility of the proposed method was verified.
任学平，李攀，王朝阁，张超. 基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击, 2018, 37(15): 6-13.
REN Xueping,LI Pan,WANG Chaoge,ZHANG Chao. Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(15): 6-13.
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