基于最优Morlet小波的全信息能量熵提取及其在滚动轴承状态监测中的应用

为利用振动信号中隐含的冲击特征成分来反映轴承性能退化趋势，综合利用小波变换技术和全信息技术，提出一种基于最优Morlet小波变换的全信息能量熵提取方法．以最小Shannon熵优化Morlet小波形状参数，通过多源振动数据的小波变换系数，利用信息熵综合反映冲击特征能量在不同频带分布差异．滚动轴承全寿命数据的应用结果表明，全信息能量熵的变化趋势能够监测轴承状态的劣化过程，而伴随的早期故障检测可以提高轴承使用的安全性．

Best Morlet Wavelet-Based Full Information Energy Entropy Extraction with Its Application to Rolling Bearing Condition Monitoring

In order to track degradational trend of bearing performance using shock feature hidden in vibration signal,a best Morlet wavelet transform--based extraction method of full information energy entropy is proposed through integrating Morlet wavelet transform technology and full in- formation technology. The optimization of Morlet wave shape factor is controlled by the minimum Shannon entropy. The information entropy derived from wavelet transform coefficients of multiple sources vibration data is used to reflect the different frequency range based energy distribution va- riance of shock feature. Viewed from the application for rolling bearing full lifetime vibration datasets,the results show that the feature trends can reflect the degradational process of bearing health and the bearing operational safety can be ensured by following the caution from incipient fault detection.