Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package

Author:

Zhu Huibin1,He Zhangming12,Xiao Yaqi1,Wang Jiongqi1,Zhou Haiyin1

Affiliation:

1. College of Sciences, National University of Defense Technology, Changsha 410073, China

2. Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

Abstract

The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately.

Funder

Civil Space Pre-research Project

National Natural Science Foundation of China

Provincial Natural Science Foundation

Hunan Provincial Postgraduate Research Innovation Project

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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