A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

Abstract

The fault diagnosis of rolling element bearings is very important for ensuring the safe operation of rotary machineries. Targeting the nonstationary characteristics of the vibration signals of rolling element bearings, a novel approach based on dual-tree complex wavelet packet transform, improved intrinsic time-scale decomposition, and the online sequential extreme learning machine is proposed in this article for the fault recognition of rolling element bearing. First, the feature extraction method of the measured signal is presented by combining improved intrinsic time-scale decomposition with dual-tree complex wavelet packet transform as preprocessor and two-step screening processes based on the energy ratio, the vibration signal is adaptively decomposed into a set of proper rotation components; second, the matrix formed by different proper rotation components and singular value decomposition is used to obtain singular value as eigenvector; finally, singular values are input to online sequential extreme learning machine to realize the fault diagnosis of rolling element bearings. The effectiveness of the proposed method of fault diagnosis is demonstrated. The experimental results show that the proposed method can effectively extract the fault characteristics and accurately identify the fault patterns.