Feature extraction of bearing vibration signals using second generation wavelet and spline-based local mean decomposition

In order to extract the fault feature frequency of weak bearing signals, we put forward a local mean decomposition (LMD) method combining with the second generation wavelet transform. After performing the second generation wavelet denoising, the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions (PFs). Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns. Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault. This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.