Gear Fault Diagnosis Based on DBNS

被引：5

作者：

Chen B. ^{[1
]}

Liu H. ^{[1
]}

Xu C. ^{[2
]}

Chen F. ^{[1
]}

Xiao W. ^{[1
]}

Zhao C. ^{[1
]}

机构：

[1] Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, 443002, Hubei

[2] Hubei Special Equipment Inspection and Testing Institute Yichang Branch, Yichang, 443002, Hubei

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 02期

关键词：

Deep belief network(DBN); Fault diagnosis; Feature extraction; Gear transmission;

D O I：

10.3969/j.issn.1004-132X.2019.02.011

中图分类号：

学科分类号：

摘要：

Aiming at the problems of gears and other parts in a gear transmission system that were prone to ault or failure, this paper presented a fault diagnosis method for gear transmissions based on deep learning theory. Firstly, the powerful feature self-extraction ability of DBNs was used to extract the features of the vibration signals of the gear transmission systems. Then the fault signals were identified by the complex map representation capability of DBNs. The diagnosis examples show that if the original time-domain signals of gear vibration are not extracted, the correct recognition rate may only reach about 60% when directly using DBNs to diagnose. If a simple Fourier transform is applied to the time domain signals, then DBNs may be used to diagnose the frequency spectrum of the processed vibration signals. The accuracy rate may reach 99.7%, which confirms the simplicity and effectiveness of the fault diagnosis method described herein. © 2019, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：205 / 211

页数：6

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