Research on Fault Diagnosis Method of Rolling Bearing Based on Model Stacking

被引：1

作者：

Lv Peng ^{[1
]}

Wang Xu ^{[1
]}

Xiao Jianglin ^{[1
]}

机构：

[1] CRRC ZhuZhou Inst CO LT, Elect Reliabil Technol Dept, Zhuzhou, Peoples R China

来源：

2021 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2021年

关键词：

Rolling Bearing; Fault Diagnosis; Model Fusion;

D O I：

10.1109/VPPC53923.2021.9699203

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

When rolling bearing fails, the vibration signal will show non-stationary characteristics. The characteristics of non-stationary vibration signal are closely related to the fault condition of the bearing. It is an effective way to diagnose the fault of the rolling bearing by analyzing the non-stationary vibration signal. In this paper, time-frequency analysis is used to decompose the vibration signal of the bearing, the matrix characteristics of the signal are obtained through singular value decomposition, and the fault diagnosis algorithm is constructed through the stacking method in machine learning. Finally, experiments have verified the effectiveness of the above theoretical methods in rolling bearing fault diagnosis.

引用

页数：4

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