基于自动编码器和SVM的轴承故障诊断方法
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摘要
支持向量机(support vector machine,SVM)应用于轴承故障诊断前,首先要提取轴承的特征信号.在以往的特征信号提取中,往往是依据已有的知识模型进行特征筛选.随着近年来深度神经网络(deep neural network,DNN)的应用与推广,自动编码器(auto-encoder,AE)在特征提取方面的优势尤为突出.作为一种无监督的学习方式,AE能够基于数据驱动提取信号的特征值,使得特征提取不再依赖于先验知识,从而让整个故障诊断过程更具智能化.本文运用改进的AE、去噪自动编码器(denoising autoencoder,DAE),进行轴承信号特征提取,并用SVM进行故障诊断.最终与基于经验模态分解(empirical mode decomposition,EMD)能量熵的SVM对比,反映具有无监督学习方式的DAE-SVM在轴承故障诊断方面的优越性,诊断准确率接近100%.
The fault feature should be extracted before the SVM was applied to the bearing fault diagnosis. In the previous feature signal extraction,it was based on the existing knowledge model. With the application and promotion of DNN in recent years,AE had a special advantage in feature extraction. As an unsupervised learning method,AE could extract the features of the signal based on data driven,making the feature extraction no longer depends on prior knowledge,and the whole fault diagnosis processed more intelligent. In this paper,the improved AE、DAE,were used to extract the features of the bearing signals,and the fault diagnosis was carried out by SVM. Finally,by compared with the SVM based on EMD energy entropy feature extraction,the superiority of DAE-SVM with unsupervised learning method was reflected in bearing fault diagnosis,and its diagnostic accuracy was nearly 100%.
The fault feature should be extracted before the SVM was applied to the bearing fault diagnosis. In the previous feature signal extraction,it was based on the existing knowledge model. With the application and promotion of DNN in recent years,AE had a special advantage in feature extraction. As an unsupervised learning method,AE could extract the features of the signal based on data driven,making the feature extraction no longer depends on prior knowledge,and the whole fault diagnosis processed more intelligent. In this paper,the improved AE、DAE,were used to extract the features of the bearing signals,and the fault diagnosis was carried out by SVM. Finally,by compared with the SVM based on EMD energy entropy feature extraction,the superiority of DAE-SVM with unsupervised learning method was reflected in bearing fault diagnosis,and its diagnostic accuracy was nearly 100%.
引文
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[13]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.
[2]姚亚夫,张星.基于瞬时能量熵和SVM的滚动轴承故障诊断[J].电子测量与仪器学报,2013,27(10):957-962.
[3]GUO T,DENG Z M.An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing[J].Applied acoustics,2017(127):46-62.
[4]任子晖,渠虎,王翠,等.基于补充总体局部均值分解的轴承故障诊断方法[J].郑州大学学报(工学版),2018,39(3):62-66.
[5]HUANG Y,WANG K,ZHOU Q,et al.Feature extraction for gas metal arc welding based on EMD and time-frequency entropy[J].International journal of advanced manufacturing technology,2017(2):1-10.
[6]LEI Y G,JIA F,LIN J,et al.An intelligent fault diagnosis method using unsupervised feature learning to-wards mechanical big data[J].IEEE Transactions on industrial electronics,2016,63(5):3137-3147.
[7]BENGIO Y.Learning deep architectures for AI[M].Foundations and trends in machine learning,2009,2(1):1-127.
[8]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[9]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems,Curran Associates Inc.2012:1097-1105.
[10]JIA F,LEIY G,LIN J,et al.Deep neural networks:A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J].Mechanical systems&signal processing,2016(72):303-315.
[11]ERHAN D,BENGIO Y,COURVILLE A,et al.Why does unsupervised pre-training help deep learning?[J].Journal of machine learning research,2010,11(3):625-660.
[12]VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising auto-encoders[C]∥Proceedings of the 25th International Conference on Machine Learning,ACM,2008:1096-1103.
[13]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.