[设备健康管理]深度置信网络在齿轮故障诊断中的应用

摘要/Abstract

摘要： 针对齿轮传动系统中齿轮等零部件易出现故障或失效等问题，提出了一种基于深度学习理论的齿轮传动系统故障诊断方法。首先利用深度置信网络强大的特征自提取能力，对齿轮传动系统的振动信号进行特征提取，然后通过DBNs的复杂映射表征能力对故障信号进行故障判别。诊断实例表明，若不对齿轮振动的原始时域信号进行特征提取，直接利用DBNs对其进行诊断时，故障识别正确率只能达到 60%左右；如果对时域信号进行简单的傅里叶变换后，再利用 DBNs 对处理后的振动信号频谱进行诊断分析，正确率能达到 99.7%，从而证明了所提故障诊断方法的简易性和有效性。

关键词: 齿轮传动, 特征提取, 深度置信网络, 故障诊断

Abstract: 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.

Key words: gear transmission, feature extraction, deep belief network(DBN), fault diagnosis

中图分类号:

陈保家, 刘浩涛, 徐超, 陈法法, 肖文荣, 赵春华. [设备健康管理]深度置信网络在齿轮故障诊断中的应用[J]. 中国机械工程.

CHEN Baojia, LIU Haotao, XU Chao, CHEN Fafa, XIAO Wenrong, ZHAO Chunhua. Gear Fault Diagnosis Based on DBNS[J]. China Mechanical Engineering.

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