Vibro-acoustic fault analysis of bearing using FFT, EMD, EEMD and CEEMDAN and their implications

Abstract

This paper analyses the vibro-acoustic characteristics of the bearing using FFT (Fast Fourier Transform), EMD (Empirical Mode Decomposition), EEMD (Ensemble EMD) and CEEMDAN (Complete EEMD with Adaptive Noise) algorithms. The main objective is to find out the best algorithm that avoids mode mixing problems while decomposing the signal and also enhance the feature extraction. It is observed that even though acoustic and vibration can be used for the fault detection in the bearing, duo follow differently interns of their statistical distributions. The feature of the bearing is acquired using acoustic and vibration sensors and analyzed using non-linear and non-stationary signal processing techniques. The statistical distribution of the data plays a major role in truly extracting the components using signal processing techniques. All the algorithms are data driven, as per the conditional events of the system, these algorithms efficiency increases or decreases. Here, the vibro-acoustic feature of the normally distributed acoustic and vibration signature are extracted effectively using CEEMDAN with least computational time and efficient signal extraction.