Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter

Abstract

The early weak fault characteristics of rolling bearings are extremely weak and are easily overwhelmed by other noises. In order to effectively extract the characteristics of the early weak faults of the rolling bearings and draw on the multilayer wavelet decomposition idea, a method for diagnosing the early weak faults of the rolling bearing based on the multilayer reconstruction filter is proposed. As we all know, empirical wavelet transform (EWT) makes full use of wavelet filter bank, and variational mode decomposition (VMD) uses Wiener filter bank. This paper fully combines the advantages of the above two methods, adaptively determines the number of modes through empirical wavelet decomposition and divides the original signal, extracts the frequency band that contains the fault characteristic information, and effectively eliminates noise interference. These steps are repeated until the optimal component of the condition is obtained. In the output layer, the weak fault impact components are further separated by the strong filtering and signal decomposition capability of VMD. The advantages of the proposed method are proved by the experiment of weak fault of rolling bearing and the accelerated failure experiment of full life. The proposed method has the advantages of reducing noise influence and adaptive estimation of decomposed modes, which can be applied more efficiently in practice.