Aiming at nonlinear and non-stationary characteristics of rolling bearing vibration signals, a feature extraction method for rolling bearing vibration signals based on variational mode decomposition(VMD) and Volterra prediction model was proposed. VMD with a good ability of non-stationary signal decomposition was utilized to decompose a rolling bearing vibration signal into finite stationary intrinsic mode functions (IMFs). Then phase space reconstruction was conducted for these IMFs. Volterra prediction model was established in the reconstructed phase space. With the class distance within class criterion, the model’s parameters were optimized to describe the bearing vibration signal. Four different states of rolling bearing vibration signals were analyzed, the optimized feature parameters had a better classification performance. Test results indicated that the proposed method can effectively be used to extract nonlinear and non-stationary features of vibration signals, and improve the fault diagnosis accuracy of rolling bearings.
张云强1，张培林1，王怀光1，杨玉栋2，吴定海1. 结合VMD和Volterra预测模型的轴承振动信号特征提取[J]. 振动与冲击, 2018, 37(3): 129-135.
ZHANG Yun-qiang1, ZHANG Pei-lin1, WANG Huai-guang1, YANG Yu-dong2, WU Ding-hai1. Feature extraction method for rolling bearing vibration signals based on VMD and Volterra prediction model. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(3): 129-135.
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