一种基于一维卷积神经网络的旋转机械智能故障诊断方法

doi:10.13232/j.cnki.jnju.2019.01.014

南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (1): 133–142.doi: 10.13232/j.cnki.jnju.2019.01.014

一种基于一维卷积神经网络的旋转机械智能故障诊断方法

安　晶^1,2，艾　萍²*，徐　森³，刘　聪¹，夏建生¹，刘大琨¹

1. 盐城工学院机械工程学院，盐城，224051； 2. 河海大学计算机与信息学院，南京，211100； 3. 盐城工学院信息工程学院，盐城，224051

接受日期:2018-12-08 出版日期:2019-02-01 发布日期:2019-01-26
通讯作者: 艾　萍, E－mail：aip@163.com，aip@hhu.edu.cn E-mail:aip@163.com，aip@hhu.edu.cn
基金资助:
国家自然科学基金(51420105014，51505408)，江苏省“333工程”，江苏省高等学校自然科学研究项目(18KJB520050)；江苏高校品牌专业建设工程“机械设计制造及其自动化”(PPZY2015B123)

An intelligent fault diagnosis method for rotating machinery based on one dimensional convolution neural network

An Jing^1,2，Ai Ping²*，Xu Sen³，Liu Cong¹，Xia Jiansheng¹，Liu Dakun¹

1. School of Mechanical Engineer，Yancheng Institute of Technology，Yancheng，224051，China； 2. College of Computer and Information Engineering，Hohai University，Nanjing，211100，China； 3. School of Information Engineering，Yancheng Institute of Technology，Yancheng，224051，China

Accepted:2018-12-08 Online:2019-02-01 Published:2019-01-26
Contact: Ai Ping, E－mail：aip@163.com，aip@hhu.edu.cn E-mail:aip@163.com，aip@hhu.edu.cn

摘要/Abstract

摘要： 状态监测和故障诊断对于维护系统性能和保证运行安全具有重要意义. 针对传统智能识别方法需要复杂的特征提取过程和大量的诊断经验等问题，结合振动信号自身的一维性的特点，提出一种基于一维卷积神经网络(1－Dimensional Convolutional Neural Network，1DCNN)的旋转机械智能故障诊断方法. 首先将数据信号通过傅里叶变换转换成频域信号并进行预处理，然后训练卷积神经网络自动提取特征，最后通过Softmax回归进行分类. 在基准数据集上的实验结果表明，1DCNN模型不仅能有效地从原始信号中进行多种工况、多种故障位置、多种故障程度的特征提取和诊断，而且具有很高的故障识别精度，获得了优于主流故障诊断方法的结果．

关键词: 卷积神经网络, 一　维, 故障诊断, 特征提取

Abstract: Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional intelligent identification method requires a complex feature extraction process and much diagnosis experience. Considering the characteristics of one dimension of vibration signals，a new method of intelligent fault diagnosis based on 1－Dimensional Convolutional Neural Network(1DCNN)is presented. Firstly，the data signal is converted into the frequency domain signal by Fourier Transformation and preprocessed. Then，the convolution neural network is trained to automatically extract the features，and finally the classification is made through Softmax regression. Experimental results on the benchmark datasets indicate that 1DCNN model can not only effectively extract features and diagnosis of multiple working conditions，multiple fault locations and different health conditions，but also be able to achieve higher identification accuracy than existing methods.

Key words: convolutional neural network, one－dimensional, fault diagnosis, feature extraction

中图分类号:

TP183

安　晶, 艾　萍, 徐　森, 刘　聪, 夏建生, 刘大琨. 一种基于一维卷积神经网络的旋转机械智能故障诊断方法[J]. 南京大学学报(自然科学版), 2019, 55(1): 133–142.

An Jing, Ai Ping, Xu Sen, Liu Cong, Xia Jiansheng, Liu Dakun. An intelligent fault diagnosis method for rotating machinery based on one dimensional convolution neural network[J]. Journal of Nanjing University(Natural Sciences), 2019, 55(1): 133–142.

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一种基于一维卷积神经网络的旋转机械智能故障诊断方法

An intelligent fault diagnosis method for rotating machinery based on one dimensional convolution neural network

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