《计算机应用研究》|Application Research of Computers

基于小波包-AR谱和深度学习的轴承故障诊断研究

Research on bearing fault diagnosis based on wavelet packet-auto regressive model spectrum and deep learning

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作者 贺思艳,刘亚,田新诚
机构 1.山东电子职业技术学院 智能制造工程系,济南 250200;2.山东大学 控制科学与工程学院,济南 250061
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文章编号 1001-3695(2019)06-033-1758-04
DOI 10.19734/j.issn.1001-3695.2018.04.0261
摘要 针对轴承故障信号的非平稳性和非线性的特点,采用小波包分解和自回归(auto-regressive,AR)谱估计相结合的方法提取振动信号特征值;为了提高诊断结果的精度,提出用深度信念网络(deep believe network,DBN)进行诊断模型训练。首先,对轴承振动信号进行小波包分解和自回归谱估计,计算不同频段的能量实现轴承故障特征提取;其次,将提取到的特征值作为深度信念网络的输入向量,进行模型训练;最后,用训练好的模型进行故障诊断。为验证所提方法的有效性,采用美国凯斯西储大学提供的旋转轴承数据集,将提出的算法与三种故障诊断方法进行对比实验。实验结果表明,所提方法具有更好的诊断性能。
关键词 小波包分解; 特征提取; 深度信念网络; 故障诊断
基金项目 山东省重点研发计划资助项目(2016ZDJS02B03)
山东省重大科技创新工程项目(2017CXGC0601)
本文URL http://www.arocmag.com/article/01-2019-06-033.html
英文标题 Research on bearing fault diagnosis based on wavelet packet-auto regressive model spectrum and deep learning
作者英文名 He Siyan, Liu Ya, Tian Xincheng
机构英文名 1.Dept. of Intelligent Manufacturing Engineering,Shandong College of Electronic Technology,Jinan 250200,China;2.School of Control Science & Engineering,Shandong University,Jinan 250061,China
英文摘要 As the bearing fault signal is non-stationary and non-linear in nature, this paper used the wavelet packet decomposition(WPD) and auto-regressive(AR) spectrum estimating method to extract features. In order to improve the accuracy of diagnosis, this paper proposed the deep believe network to train diagnostic models. Firstly, it used the WPD and AR spectrum estimating method to calculate the energy of different signal bands to realize the feature extraction of vibration signals. Then, it used the extracted eigenvalues as input vectors of the deep belief network to conduct model training. Finally, it used the trained model to carry out bearing fault diagnosis. In order to verify the effectiveness of the proposed method, used the rotating bearing data set provided by the Case Western Reserve University in the United States, the algorithm was compared with three fault diagnosis methods. Experimental results show that the proposed method has better diagnostic performance.
英文关键词 wavelet packet decomposition; feature extraction; deep believe network; fault diagnosis
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收稿日期 2018/4/26
修回日期 2018/6/11
页码 1758-1761,1766
中图分类号 TP206.3
文献标志码 A