Bearing fault diagnosis based on Empirical Mode Decomposition and Adaptive Network-based Fuzzy Inference System

Abstract

The early fault characteristics of rolling bearings are weak and time-varying, and there are problems of modal aliasing and end effect in Empirical Mode Decomposition (EMD), and the decomposition effect is unstable. A bearing fault diagnosis method based on EMD and Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed. Firstly, the signal is decomposed into several Intrinsic Mode Functions (IMF) by EMD, then multiple fault feature quantities are obtained from the IMF1 component to construct the fault feature set. Finally, the extracted fault feature set is classified and identified by ANFIS. The experimental results show that the proposed method has high diagnostic accuracy and can effectively identify the fault type.