基于自适应复平移Morlet小波的轴承包络解调分析方法

针对滚动轴承的传统包络解调分析技术需要人工选择参数的缺点，提出一种自适应包络解调分析方法。该方法针对轴承故障在振动信号中表现为冲击衰减波形的特点，采用复平移Morlet小波实现冲击特征波形的自动提取。同时，基于小波系数峭度值最大的优化策略，给出Morlet小波基函数的中心频率和包络因子的优化方法，从而实现与冲击特征成分的最优匹配，获得较好的包络信号。对模拟信号和实际轴承故障数据的应用分析表明，该方法通过对基函数波形的优化匹配，可以有效地解调出弱故障特征分量，效果优于普通的复平移Morlet小波变换，适合于轴承的早期故障特征提取。

OPTIMAL ENVELOPE DEMODULATION METHOD BASED ON ADAPTIVE COMPLEX SHIFTED MORLET WAVELET IN BEARING DIAGNOSIS

Due to the demodulation performance of traditional envelope demodulation methods depend strongly on a number of parameters selected by experience of the user, so an adaptive wavelet-based envelope method derived from complex shifted Morlet wavelet is proposed to overcome this shortcoming. The complex shifted Morlet wavelet family is adopted to extract fault features based on the fact that the bearing defect can excites vibration at specific impact component. Meanwhile, based on the optimal principle of maximal kurtosis of coefficient waveform, the shape parameters of Morlet wavelet, which include center frequency and envelope factor, can be adjusted adaptively to match with the impact component. Thereby, the optimization envelope can be constructed effectively. Experimental results and industrial measurement analysis show that the new approach, compared with the common complex shifted wavelet transform, could effectively extract the weak fault feature with the optimal matching of waveform, and is suitable to extract the incipient fault feature for rolling bearing.