A novel joint denoising method for gear fault diagnosis with improved quaternion singular value decomposition

Abstract

Singular value decomposition (SVD) has drawn increasing attention in recent years as an effective signal noise reduction method. However, it is inapplicable to multivariate signals with abundant fault information. Existing quaternion singular value decomposition (QSVD) can decompose multivariate signals simultaneously, but the methods based on QSVD dependent on the embedding dimension and reconstructed components order seriously, and they are not suitable to noisy signals. To solve the problem, a novel joint denoising method improved quaternion singular value decomposition (IQSVD) is proposed, which can determine two parameters adaptively. Firstly, to select the embedding dimension, the improved power spectral density (IPSD) is proposed with considering frequency features and time-domine traits together. And then the periodic intensity (PI) is used to determine the reconstructed components by searching the component composing the most abundant periodic part. Finally, combined with envelope spectrum analysis, the gear fault is diagnosed. The results of the gear simulated and experimental multivariate signals verify the effectiveness of proposed method.