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[1]鲁艳军,陈汉新,贺文杰,等.基于混合特征提取和WNN的齿轮箱故障诊断[J].武汉工程大学学报,2011,(05):82-88.[doi:10.3969/j.issn.16742869.2011.05.022]
　LU Yanjun,CHEN Hanxin,HE Wenjie,et al.Gearbox fault diagnosis based on hybrid feature extraction and wavelet neural network[J].Journal of Wuhan Institute of Technology,2011,(05):82-88.[doi:10.3969/j.issn.16742869.2011.05.022]
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基于混合特征提取和WNN的齿轮箱故障诊断(/HTML)
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《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
期数:
2011年05期
页码:
82-88
栏目:
机电与信息工程
出版日期:
2011-05-31

文章信息/Info

Title:
Gearbox fault diagnosis based on hybrid feature
extraction and wavelet neural network
文章编号:
16742869(2011)05008207
作者:
鲁艳军1; 陈汉新1; 贺文杰1; 2; 尚云飞1; 陈绪兵1
1.武汉工程大学机电工程学院，湖北 武汉 430205；2.法国国立梅斯工程师学院，梅斯 57078
Author(s):
LU Yanjun1;  CHEN Hanxin1;  HE Wenjie1; 2;  SHANG Yunfei1;  CHEN Xubing1
1. School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China；
2. Ecole Nationale d’Ingénieurs de Metz, Metz 57078, France
关键词:
齿轮箱; 特征提取; 小波神经网络; 故障诊断
Keywords:
gearbox;  feature extraction;  wavelet neural network;  fault diagnosis
分类号:
TH165+.3
DOI:
10.3969/j.issn.16742869.2011.05.022
文献标志码:
A
摘要:
提出了一种基于混合特征提取和小波神经网络(WNN)的齿轮箱故障诊断方法，运用时域分析法、小波分解和小波包分解相结合的方法对齿轮箱振动信号进行故障特征提取，将所提取的特征值作为WNN分类器的特征输入参数，采用反向传播(BP)算法对WNN结构中的平移参数、尺度参数、连接权值和阈值进行调整和优化.在实验中采用不同裂纹尺寸的齿轮来模拟三种故障模式，通过对三种故障齿轮进行诊断和分类，能证明本文所提议的故障诊断方法是有效且可靠的.
Abstract:
A new method of fault diagnosis for gearbox based on hybrid feature extraction and wavelet neural network (WNN) was proposed in this paper. The time domain analysis, wavelet packet decomposition and wavelet decomposition were applied to extract the fault feature information of vibration signals collected from gearbox. The extracted feature values were regarded as the feature input vector of WNN. The scale parameters, translation parameters, weight values and threshold values in WNN structure were optimized by traditional backpropagation (BP) algorithm. Three gear fault modes were simulated with different crack sizes in the experiment. The effectiveness and reliability of the presented fault diagnosis method were demonstrated through identification and classification for several fault modes.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：20110114基金项目：湖北省教育厅科学技术研究重大项目(Z20101501)；武汉市科技局科技攻关项目(201010621237)作者简介：：鲁艳军（1987），男，湖北天门人，硕士研究生.研究方向：机械设备故障诊断与信号处理.指导老师：陈汉新，男，教授，博士研究生指导老师.研究方向：机械故障诊断及监控、化工石油管道的无损检测.
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