基于EMD和SSAE的滚动轴承故障诊断方法
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摘要
深度学习以其强大的特征提取能力展现了它在故障诊断领域的绝对优势。为此,提出了一种基于EMD和SSAE的滚动轴承故障诊断方法。首先采用EMD方法分析滚动轴承振动信号,并用得到的IMF构造Hankel矩阵,获得能反映信号特征的奇异值;其次将奇异值划分为训练集与测试集样本,建立基于SSAE方法的故障诊断模型;最后训练与测试搭建的深度神经网络,得到诊断准确率。所提方法不需要大量的故障诊断先验知识,无需对信号去噪处理,简化了滚动轴承故障诊断的特征提取过程,具有较高的故障诊断准确率。
Deep learning has demonstrated its absolute superiority in the field of fault diagnosis with its strong feature extraction ability in recent years.In this paper,a fault diagnosis method based on EMD and SSAE is proposed.Firstly,EMD method is used to analyze the vibration signal of the rolling bearing,and the Hankel matrices is constructed by the obtained IMFs,then the singular values representing the characteristics of the signal is obtained.Secondly,the samples are divided into a training set and a test set,and a fault diagnosis model based on SSAE method is established.Finally,after training and testing the deep neural network,the diagnostic accuracy can be obtained.The proposed method doesn't need a lot of prior knowledge of fault diagnosis and even signal denoising processing,simplifying the feature extraction of rolling bearing fault diagnosis,which also has a higher accuracy of fault diagnosis.
Deep learning has demonstrated its absolute superiority in the field of fault diagnosis with its strong feature extraction ability in recent years.In this paper,a fault diagnosis method based on EMD and SSAE is proposed.Firstly,EMD method is used to analyze the vibration signal of the rolling bearing,and the Hankel matrices is constructed by the obtained IMFs,then the singular values representing the characteristics of the signal is obtained.Secondly,the samples are divided into a training set and a test set,and a fault diagnosis model based on SSAE method is established.Finally,after training and testing the deep neural network,the diagnostic accuracy can be obtained.The proposed method doesn't need a lot of prior knowledge of fault diagnosis and even signal denoising processing,simplifying the feature extraction of rolling bearing fault diagnosis,which also has a higher accuracy of fault diagnosis.
引文
[1]Cui L,Huang J,Zhang F.Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis[J].IEEETransactions on Industrial Electronics,2017,64(11):8695-8706.
[2]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
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[6]vokelj M,Zupan S,Prebil I.Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method[J].Mechanical Systems&Signal Processing,2011,25(7):2631-2653.
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[8]雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断[J].机械工程学报,2008,44(7):112-117.Lei Yaguo,He Zhengjia,Zi Yanyang.Fault diagnosis based on novel hybrid intelligent model[J].Journal of Mechanical Engineering,2008,44(7):112-117.
[9]Pan M C,Tsao W C.Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings[J].International Journal of Mechanical Sciences,2013,69(4):114-124.
[10]Yang Y,Yu Dejie,Cheng J.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound&Vibration,2006,294(1):269-277.
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[12]Chen Z,Deng S,Chen X,et al.Deep neural networks-based rolling bearing fault diagnosis[J].Microelectronics Reliability,2017,75:327-333.
[13]郭亮,高宏力,张一文,等.基于深度学习理论的轴承状态识别研究[J].振动与冲击,2016,35(12):167-171.Guo Liang,Gao Hongli,Zhang Yiwen,et al.Research on bearing condition monitoring based on deep learning[J].Journal of Vibration and Shock,2016,35(12):167-171.
[14]Chen Z,Li W.Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J].IEEE Transactions on Instrumentation&Measurement,2017,66(7):1693-1702.
[15]段向阳,王永生,苏永生.基于奇异值分解的信号特征提取方法研究[J].振动与冲击,2009,28(11):30-33.Duan Xiangyang,Wang Yongsheng,Su Yongsheng.Feature extraction methods based on singular value decomposition[J].Journal of Vibration and Shock,2009,28(11):30-33.
[16]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.Zhang Chao,Chen Jianjun,Guo Xun.A gear fault diagnosis method based on EMD energy entropy and SVM[J].Journal of Vibration and Shock,2010,29(10):216-220.
[17]杨宇,于德介,程军圣.基于EMD的奇异值分解技术在滚动轴承故障诊断中的应用[J].振动与冲击,2005,24(2):12-15.Yang Yu,Yu Dejie,Cheng Junsheng.Application of EMD based singular value decomposition technique to fault diagnosis for rolling bearing[J].Journal of Vibration and Shock,2005,24(2):12-15.
[18]Zhao X,Ye B.Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis[J].Mechanical Systems&Signal Processing,2009,23(4):1062-1075.
[19]王超,孔凡让,黄伟国,等.改进的奇异值分解在轴承故障诊断中的应用[J].振动工程学报,2014,27(2):296-303.Wang Chao,Kong Fanrang,Huang Weiguo,et al.Application of improved singular value decomposition in bearing fault diagnosis[J].Journal of Vibration Engineering,2014,27(2):296-303.
[2]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[3]Huang N E,Shen Z,Long S R.A new view of nonlinear water waves:The Hilbert spectrum[J].Annual Review of Fluid Mechanics,1999,31(1):417-457.
[4]雷亚国.基于改进Hilbert-Huang变换的机械故障诊断[J].机械工程学报,2011,47(5):71-77.Lei Yaguo.Machinery fault diagnosis based on improved Hilbert-Huang transform[J].Journal of Mechanical Engineering,2011,47(5):71-77.
[5]Liu Y P,Li Y,Ma H T.Seismic random noise reduction by empirical mode decomposition combined with translation invariant scale-adaptive threshold[C].International Conference on Wavelet Analysis and Pattern Recognition,IEEE,2012:53-57.
[6]vokelj M,Zupan S,Prebil I.Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method[J].Mechanical Systems&Signal Processing,2011,25(7):2631-2653.
[7]Mert A,Akan A.EEG denoising based on empirical mode decomposition and mutual information[C].Mediterranean Conference on Medical and Biological Engineering and Computing,Seville,Spain,2013:631-634.
[8]雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断[J].机械工程学报,2008,44(7):112-117.Lei Yaguo,He Zhengjia,Zi Yanyang.Fault diagnosis based on novel hybrid intelligent model[J].Journal of Mechanical Engineering,2008,44(7):112-117.
[9]Pan M C,Tsao W C.Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings[J].International Journal of Mechanical Sciences,2013,69(4):114-124.
[10]Yang Y,Yu Dejie,Cheng J.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound&Vibration,2006,294(1):269-277.
[11]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[12]Chen Z,Deng S,Chen X,et al.Deep neural networks-based rolling bearing fault diagnosis[J].Microelectronics Reliability,2017,75:327-333.
[13]郭亮,高宏力,张一文,等.基于深度学习理论的轴承状态识别研究[J].振动与冲击,2016,35(12):167-171.Guo Liang,Gao Hongli,Zhang Yiwen,et al.Research on bearing condition monitoring based on deep learning[J].Journal of Vibration and Shock,2016,35(12):167-171.
[14]Chen Z,Li W.Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J].IEEE Transactions on Instrumentation&Measurement,2017,66(7):1693-1702.
[15]段向阳,王永生,苏永生.基于奇异值分解的信号特征提取方法研究[J].振动与冲击,2009,28(11):30-33.Duan Xiangyang,Wang Yongsheng,Su Yongsheng.Feature extraction methods based on singular value decomposition[J].Journal of Vibration and Shock,2009,28(11):30-33.
[16]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.Zhang Chao,Chen Jianjun,Guo Xun.A gear fault diagnosis method based on EMD energy entropy and SVM[J].Journal of Vibration and Shock,2010,29(10):216-220.
[17]杨宇,于德介,程军圣.基于EMD的奇异值分解技术在滚动轴承故障诊断中的应用[J].振动与冲击,2005,24(2):12-15.Yang Yu,Yu Dejie,Cheng Junsheng.Application of EMD based singular value decomposition technique to fault diagnosis for rolling bearing[J].Journal of Vibration and Shock,2005,24(2):12-15.
[18]Zhao X,Ye B.Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis[J].Mechanical Systems&Signal Processing,2009,23(4):1062-1075.
[19]王超,孔凡让,黄伟国,等.改进的奇异值分解在轴承故障诊断中的应用[J].振动工程学报,2014,27(2):296-303.Wang Chao,Kong Fanrang,Huang Weiguo,et al.Application of improved singular value decomposition in bearing fault diagnosis[J].Journal of Vibration Engineering,2014,27(2):296-303.