基于改进型深度网络数据融合的滚动轴承故障识别
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摘要
针对传统智能诊断方法依赖于信号处理和故障诊断经验提取故障特征以及模型泛化能力差的问题,基于深度学习理论,提出将卷积神经网络算法结合softmax分类器,针对数据集不平衡问题引入加权损失函数、正则化以及批量归一化等模型优化技术搭建适于滚动轴承故障诊断的改进型深度卷积神经网络模型。模型从原始实测轴承振动信号出发逐层学习实现特征提取与目标分类。实验结果表明,优化后的深度学习模型可实现对早期微弱故障、不同程度故障的精确识别,在不平衡数据集上也可达到95%的识别准确率,并且模型拥有较快的收敛速度和较强的泛化能力。
Traditional intelligent diagnosis methods rely too much on the experience of signal processing and fault diagnosis to extract fault features,and generalization ability of models is poor.Based on the theory of deep learning,a convolutional neural network algorithm combined with the softmax classifier is proposed to introduce weighting to the solution of data set imbalance problem.Model optimization techniques such as weighted loss function,regularization,and batch normalization are applied to the construction of an improved deep convolutional neural network model for rolling bearing fault diagnosis.The model learns from the original measured bearing vibration signal by layer-by-layer learning to achieve feature extraction and target classification.Experimental results show that the optimized deep learning model can achieveaccurate recognition of early weak faults and different levels of faults,and its recognition accuracy on unbalanced data sets can reach 95%.Furthermore the model has faster convergence speed and strong generalization ability.
Traditional intelligent diagnosis methods rely too much on the experience of signal processing and fault diagnosis to extract fault features,and generalization ability of models is poor.Based on the theory of deep learning,a convolutional neural network algorithm combined with the softmax classifier is proposed to introduce weighting to the solution of data set imbalance problem.Model optimization techniques such as weighted loss function,regularization,and batch normalization are applied to the construction of an improved deep convolutional neural network model for rolling bearing fault diagnosis.The model learns from the original measured bearing vibration signal by layer-by-layer learning to achieve feature extraction and target classification.Experimental results show that the optimized deep learning model can achieveaccurate recognition of early weak faults and different levels of faults,and its recognition accuracy on unbalanced data sets can reach 95%.Furthermore the model has faster convergence speed and strong generalization ability.
引文
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[3]Shen Z J,Chen X F,Zhang X L,et al.A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM[J].Measurement,2012,45(1):30-40.
[4]雷亚国,何正嘉.混合智能故障诊断与预示技术的应用进展[J].振动与冲击,2011,30(9):129-135.LEI Yaguo,HE Zhengjia.Advances in applications of hybrid intelligent fault diagnosis and prognosis technique[J].Journal of Vibration and Shock,2011,30(9):129-135.(in Chinese)
[5]雷亚国,贾峰,周昕,等.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报,2015,51(21):49-56.LEI Yaguo,JIA Feng,ZHOU Xin,et al.A deep learning-based method for machinery health monitoring with big data[J].Journal of Mechanical Engineering,2015,51(21):49-56.(in Chinese)
[6]Jia F,Lei Y G,Lu N,et al.Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J].Mechanical Systems and Signal Processing,2018,110:349-367.
[7]Shao H D,Jiang H K,Lin Y,et al.A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J].Mechanical Systems and Signal Processing,2018,102:278-297.
[8]Kingma D,Ba J.Adam:a method for stochastic optimization[EB/OL].(2017-01-30)[2018-09-25]https:∥arxiv.org/pdf/1412.6980v9.pdf.
[9]Szegedy C,Ioffe S,Vanhoucke V,et al.Inception-v4,inception-resnet and the impact of residual connections on learning[C]∥Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence(AAAI-17).Association for the Advancement of Artificial Intelligence,2017:4278-4284.
[10]Hinton G E,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing co-adaptation of feature detectors[EB/OL].(2012-07-03)[2018-09-25].https:∥arxiv.org/pdf/1207.0580.pdf.
[11]Bengio Y,Lamblin P,Popovici D,et al.Greedy layer-wise training of deep networks[J].Advances in Neural Information Processing Systems,2007,19:153-160.
[12]姚德臣,杨建伟,程晓卿,等.基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J].机械工程学报,2018,54(9):168-176.YAO Dechen,YANG Jianwei,CHENG Xiaoqing,et al.Railway rolling bearing fault diagnosis based on muti-scale IMF permutation entropy and SA-SVM classifier[J].Journal of Mechanical Engineering,2018,54(9):168-176.(in Chinese)
[13]赵志宏,杨绍普.基于相对小波能量的滚动轴承故障诊断[J].电子测量与仪器报,2011,25(1):44-49.ZHAO Zhihong,YANG Shaopu.Fault diagnosis of roller bearing based on relative wavelet energy[J].Journal of Electronic Measurement and Instrument,2011,25(1):44-49.(in Chinese)