基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法

doi:10.3901/JME.2019.07.009

机械工程学报 ›› 2019, Vol. 55 ›› Issue (7): 9-18.doi: 10.3901/JME.2019.07.009

• 基于深度学习的机械装备故障预测与健康管理 • 上一篇下一篇

基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法

胡茑庆^1,2, 陈徽鹏^1,2, 程哲^1,2, 张伦^1,2, 张宇^1,2

1. 国防科技大学智能科学学院长沙 410072;
2. 国防科技大学装备综合保障技术重点实验室长沙 410072

收稿日期:2018-07-09 修回日期:2018-12-12 出版日期:2019-04-05 发布日期:2019-04-05
通讯作者: 陈徽鹏(通信作者),男,1994年出生,硕士研究生。主要研究方向为状态监控与故障诊断。E-mail:chenhuipeng12@nudt.edu.cn
作者简介:胡茑庆,男,1967年出生,博士,教授,博士研究生导师。主要研究方向为状态监控与故障诊断。E-mail:hnq@nudt.edu.cn
基金资助:
国家自然科学基金（51475463，51775550）和湖南省自然科学基金（2018JJ3604）资助项目。

Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks

HU Niaoqing^1,2, CHEN Huipeng^1,2, CHENG Zhe^1,2, ZHANG Lun^1,2, ZHANG Yu^1,2

1. College of Mechatronics and Automation, National University of Defense Technology, Changsha 410072;
2. Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410072

Received:2018-07-09 Revised:2018-12-12 Online:2019-04-05 Published:2019-04-05

摘要/Abstract

摘要： 行星齿轮箱振动信号具有非平稳特性，需要一定的先验知识和诊断专业知识设计和解释特征从而实现故障诊断。为了实现行星齿轮箱的智能诊断，提出一种基于经验模态分解（Empirical mode decomposition，EMD）和深度卷积神经网络（Deepconvolutional neural network，DCNN）的智能故障诊断方法。首先对振动信号进行经验模态分解得到内禀模式函数（Intrinsicmode function，IMF）；然后利用DCNN融合特征信息明显的IMF分量，并自动提取特征；最后，将特征用于分类器分类识别，从而实现行星齿轮箱故障诊断的自动化。试验结果表明：该方法能准确、有效地对行星齿轮箱的工作状态和故障类型进行分类。

关键词: 故障诊断, 经验模态分解, 深度卷积神经网络, 行星齿轮箱

Abstract: As the vibration signal of the planetary gearbox is usually nonstationary, a significant level of prior knowledge and diagnostic expertise is required to engineer and interpret features for fault diagnosis. In order to achieve intelligent diagnosis of the planetary gearbox, an intelligent fault diagnosis method based on empirical mode decomposition (EMD) and deep convolutional neural networks (DCNN) is proposed. Firstly, EMD is used to decompose the vibration signal to obtain intrinsic mode function (IMF) components. Then the IMFs with obvious fault character are fused through DCNN and features are extracted automatically. Finally, the learned features serve as the input parameters of classifier to classify working condition, and the atomization of the planetary gearbox fault diagnosis can be implemented. The experimental results show that the method can classify the working state and fault type of the planetary gearbox accurately and effectively.

Key words: deep convolutional neural networks, empirical mode decomposition, fault diagnosis, planetary gearbox

中图分类号:

TH17

胡茑庆, 陈徽鹏, 程哲, 张伦, 张宇. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18.

HU Niaoqing, CHEN Huipeng, CHENG Zhe, ZHANG Lun, ZHANG Yu. Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.

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基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法

Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks

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