|本期目录/Table of Contents|

[1]徐先峰,王 研,刘阿慧,等.基于CNN并引入EMD预处理机制的滚动轴承故障诊断方法[J].工业仪表与自动化装置,2020,(04):7-11.[doi:1000-0682(2020)04-0000-00]
　XU Xianfeng,WANG Yan,LIU Ahui,et al.A rolling bearing fault diagnosis method based on CNN and introducing EMD preprocessing mechanism[J].Industrial Instrumentation & Automation,2020,(04):7-11.[doi:1000-0682(2020)04-0000-00]
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基于CNN并引入EMD预处理机制的滚动轴承故障诊断方法

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年04期
页码:
7-11
栏目:
出版日期:
2020-08-15

文章信息/Info

Title:
A rolling bearing fault diagnosis method based on CNN and introducing EMD preprocessing mechanism
作者:
徐先峰; 王 研; 刘阿慧; 郎 彬
长安大学 电子与控制工程学院，西安 710064
Author(s):
XU Xianfeng; WANG Yan; LIU Ahui; LANG Bin
School of Electronic & Control Engineering, Chang’an University, Xi’an 710064, China
关键词:
关键词：滚动轴承; EMD; 相关系数; CNN; 故障诊断
Keywords:
rolling bearing;  EMD ;  correlation;  CNN;  fault diagnosis
分类号:
TH113
DOI:
1000-0682(2020)04-0000-00
文献标志码:
A
摘要:
滚动轴承故障信号具有非线性、非平稳、强噪声特性，传统算法依赖于人工特征提取且缺乏自适应性。为此，该文利用经验模态分解(EMD)将原始特征集分解为一系列平稳的本征模态函数(IMF)，结合相关系数遴选能突出更多局部特征的IMF构建特征向量，剔除部分噪声干扰信号。构造卷积神经网路(CNN)的多层特征提取网络，以遴选的特征向量为输入将其逐级变换为抽象的深层特征，最后完成特征域到故障类别域地映射。实验结果表明，该算法相比较其他方法具有更高的准确率、更好的鲁棒性。
Abstract:
The signal of rolling bearing fault has non-linear, non-stationary and strong noise characteristics. The traditional algorithm depends on artificial feature extraction and lacks adaptability. In order to solve this problem, the original feature set was decomposed into a series of stationary intrinsic modal functions(IMF) by empirical mode decomposition(EMD),combined with correlation coefficient to select IMF which can highlight more local features constructing the feature vector, and eliminates some noise interference signals. A multi-layer feature extraction network of convolutional neural networks(CNN) is constructed, which is transformed into abstract deep features step by step with selected feature vectors as input, and finally the mapping between feature domain and fault category domain is completed. The experimental results show that the algorithm has higher accuracy and better robustness compared with other methods.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2020-01-10
基金项目：
国家自然科学基金(61201407);
陕西省自然科学基础研究计划(2016JQ5103)
作者简介：
徐先峰(1982),男,山东泰安人,博士,副教授,硕士生导师,主要研究方向为信号处理、深度学习理论及应用、智能电网等方面。
更新日期/Last Update: 1900-01-01