关键词: 精细广义复合多元多尺度反向散布熵, 滚动轴承, 故障诊断, 特征提取

Abstract: Multi-scale reverse dispersion entropy(MRDE） might effectively measure the complexity of time series, but MRDE had defects in coarse-grained structure and lacked the effective use of other channel information in characterizing the nonlinear fault characteristics of rolling bearings. To accurately extract fault features from bearing signals, combined with the ideas of refinement and generalized composite multi-scale, the multi-variate sample entropy theory that characterized the multi-variate complexity of synchronized multi-channel data was applied to the rolling bearing fault diagnosis and RGCMvMRDE was proposed. Then, a rolling bearing fault diagnosis method was proposed based on RGCMvMRDE and gravitational search algorithm support vector machine(GSA-SVM). Firstly, the RGCMvMRDE was applied to comprehensively characterize the fault feature information of rolling bearings and the fault feature sets were contracted. Secondly, GSA-SVM was used to identify the fault type intelligently. Finally, the proposed fault diagnosis method was applied to analyze experimental data of rolling bearing with comparing with the existing fault feature extraction methods based on MRDE, generalized MRDE(GMRDE） and refined composite multi-yariate multi-scale permutation entropy RCMvMPE. The results indicate that the proposed method may effectively and accurately identify different fault types and fault degrees of rolling bearings and the diagnosis effectiveness is better than those of the compared methods.

Key words: refined generalized composite multi-variate multi-scale reverse dispersion entropy(RGCMvMRDE), rolling bearing, fault diagnosis, feature extraction