Self-multivariate Spectral Decomposition and Mode-fused Envelope Spectrum for Enhancing Bearing Fault Feature

Abstract

In practical diagnostic scenarios, multivariate vibration data (MVD) actually contain more comprehensive fault information. Variational mode extraction (VME) has recently become a promising tool for extracting a specific mode from vibration data. However, its performance of feature extraction is restricted by presetting initial parameters and the joint information within MVD is ignored to be analyzed by VME. To address these issues, a self-multivariate spectral decomposition (SMSD), which includes multivariate spectral feature detector and multivariate mode extraction, is presented to efficiently decouple the fault-related features from MVD. The proposed method can avoid the omitting of effective modes and inclusion of interferential modes. Furthermore, a mode-fused envelope spectrum (MFES) is built to suppress in-band noise of extracted modes. Three cases demonstrate that SMSD can identify multivariate fault-related modes effectively and MFES can exhibit the fault-related frequencies clearly. Moreover, its superior performance is verified by comparing with some well-known multivariate decomposition methods.