Abstract:Due to bearing incipient fault size being smaller and susceptible to environmental noise and signal degradation, fault impact signals are often very weak.Variational mode decomposition (VMD) has a certain application in bearing fault feature extraction, but bearing weak fault extraction is not ideal under stronger background noise.Here, aiming at this problem, the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and VMD were proposed to do bearing early fault diagnosis.Influences of filter length on MOMEDA effect were studied.An adaptive MOMEDA method based on the forward and backward one was proposed to determine the optimal filter length.The adaptive MOMEDA was used to de-noise signals and avoid false peal values after traditional MED iteration and filtering.The de-noised signals were decomposed with VMD to do signal reconstruction based on spectral kurtosis.Fault features were extracted from the reconstructed signals to gain the better effect.Finally, the feasibility and effectiveness of the proposed method were verified with tests.
刘岩,伍星,刘韬,陈庆. 基于自适应MOMEDA与VMD的滚动轴承早期故障特征提取[J]. 振动与冲击, 2019, 38(23): 219-229.
LIU Yan,WU Xing,LIU Tao,CHEN Qing. Feature extraction for rolling bearing incipient faults based on adaptive MOMEDA and VMD. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(23): 219-229.
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