【摘要】 结合数学形态学与信息熵理论,提出一种基于多尺度形态分解谱熵的电机轴承退化特征提取方法。对不同损伤程度轴承的振动信号进行多尺度形态分解,分别计算其在不同尺度域内的复杂性度量:能谱熵与奇异谱熵,以其作为预测特征矢量可以对性能退化趋势有很好的线性反映能力。仿真与实例数据验证了此方法的有效性。更多还原

【Abstract】 Combining the mathematical morphology with information entropy theory,a method is proposed for degeneration feature extraction of motor bearings based on multi scale morphological decomposition spectrum entropy.The multi scale morphological decomposition is carried out for bearing vibration signals with different damage degree,the complexity indicator of power spectral entropy and singular spectral entropy in different scale domain is computed separately.Taking the two indicators as forecasting character vector is able to linearly reflect performance degeneration trend.The effectiveness of the method is verified by simulation and instance data.更多还原