Best Morlet wavelet-based full information energy entropy extraction with its application to rolling bearing condition monitoring

摘要

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