Signal denoising based on EEMD for non-stationary signals and its application

Abstract

A novel adaptive denoising method based on ensemble empirical mode decomposition (EEMD) and zero-crossing detection is proposed and combined with energy moment and support vector machine(SVM) to apply in fault diagnosis. Firstly, with the method of EEMD, the non-stationary vibration signals are adaptively decomposed into a finite number of intrinsic mode functions(IMF), which can alleviate model mixing that may appear in EMD method. Then calculate the zero-crossing ratio of every IMF components and compare them to the predetermined threshold value, the IMF components which are satisfied for request of threshold value are obtained. So the denoised signal is obtained through reconstructing desirable IMF components. Otherwise, the energy moments of desirable IMF components are extracted as the input vector of binary tree support vector machine(BTSVM) to realize the fault diagnosis of diesel engine, which validate the effectiveness of the method.