A Two-Stage Method for Weak Feature Extraction of Rolling Bearing Combining Cyclic Wiener Filter with Improved Enhanced Envelope Spectrum

Abstract

Due to the interference of various strong background signals, it is often difficult to extract effective features by using conventional methods such as envelope spectrum analysis when early weak fault arises in rolling bearing. Inspired by the current two main research directions of weak fault diagnosis of rolling bearing, that is, the enhancement of impulse features of faulty vibration signal through vibration analysis and the selection of fault information sensitive frequency band for further envelope spectrum analysis, and based on the second-order cyclostationary characteristic of the vibration signal of faulty bearing, a two-stage method for weak feature extraction of rolling bearing combining cyclic Wiener filter with improved enhanced envelope spectrum (IEES) is proposed in the paper. Firstly, the original vibration signal of the rolling bearing’s early weak fault is handled by cyclic Wiener filter exploiting the spectral coherence (SCoh) theory and the noise components are filtered out. Then, SCoh is applied on the filtered signal. Subsequently, an IEES method obtained by integrating over the selected fault information sensitive spectral frequency band of the SCoh spectral is used to extract the fault features. The innovation of the proposed method is to fully excavate the advantages of cyclic Wiener filter and IEES simultaneously. The feasibility of the proposed method is verified by simulation firstly, and vibration signals collected from accelerated bearing degradation tests and engineering machines are used to further verify its effectiveness. Additionally, its superiority over the other state-of-the-art methods is also compared.