Early fault diagnosis of rolling bearings based on signal reconstruction through SDD-SVD and FWEO

Abstract

An approach based on the singular spectrum decomposition (SSD)-singular value decomposition (SVD) and frequency weight energy operator (FWEO) was proposed for early fault diagnosis of rolling bearings. Since the interference of heavy noise in the early stage of bearing fault, SSD could eliminate abundant noise, meanwhile adaptively decompose the nonlinear, non-stationary signals into multiple mono-components which had distinct physical meanings. Subsequently, SVD was adopted for de-noising in every mono-components because the noise was distributed throughout the frequency domain. In the following, signal reconstruction was performed based on the selected mono-components with de-noising. Finally, FWEO was employed for envelop analysis and the early fault diagnosis of the rolling bearing is realized. Two groups of experiment data concerning signals of early bearing fault were investigated to valid the effectiveness of the proposed method, and good fault identification effects were obtained.