利用FCKT以及深度自编码神经网络的滚动轴承故障智能诊断
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摘要
实时、快速、批量地对振动信号进行处理成为故障诊断领域的未来发展趋势,但是可能会带来数据维数灾难问题。针对在样本较大情况下深度学习运行时间较长,以及层与层之间节点数的减少使得故障识别准确率降低问题,提出首先计算原始时域信号的频谱在不同偏移点数下的相关峭度值(FCKT)作为新的样本数据,并结合深度自编码神经网络实现轴承的智能故障分类。新样本相对于原始样本,实现了数据的维数约减,缩短了样本集的分析时间。同时,在保持各样本数据原有信息的基础上,使得样本之间差异性更突出。另外,该方法在避免深度学习算法层与层之间的权值根据经验设定的同时,解决了通过逐层减少隐含层节点数来提高计算效率时带来的分类识别准确率降低的问题。最后,通过试验数据对比分析验证了算法的有效性。
Real-time, fast, and batch processing of vibration signals have become a future development trend in the field of fault diagnosis. However, it may lead to data dimensional disasters. In view of the fact that the long running time and the low fault identification accuracy of deep learning algorithm under the condition of large sample. The frequency spectrum correlation Kurtosis of original time domain signal is calculated under different iteration periods(FCKT) as new samples data and use the deep auto-coding neural network is used to realize the intelligent fault classification of planetary gearbox. Compared with the original samples, the new samples reduce the data dimension and shorten the analysis time. At the same time, the differences between the samples are more prominent based on the original information of each sample data. In addition, the method solves the problems that the weight parameters between layers of deep learning algorithm are set according to experience and the classification accuracy is reduced by reducing the number of hidden nodes layer by layer to improve the calculation efficiency. Finally, the validity of the algorithm is verified by comparison of experimental data.
Real-time, fast, and batch processing of vibration signals have become a future development trend in the field of fault diagnosis. However, it may lead to data dimensional disasters. In view of the fact that the long running time and the low fault identification accuracy of deep learning algorithm under the condition of large sample. The frequency spectrum correlation Kurtosis of original time domain signal is calculated under different iteration periods(FCKT) as new samples data and use the deep auto-coding neural network is used to realize the intelligent fault classification of planetary gearbox. Compared with the original samples, the new samples reduce the data dimension and shorten the analysis time. At the same time, the differences between the samples are more prominent based on the original information of each sample data. In addition, the method solves the problems that the weight parameters between layers of deep learning algorithm are set according to experience and the classification accuracy is reduced by reducing the number of hidden nodes layer by layer to improve the calculation efficiency. Finally, the validity of the algorithm is verified by comparison of experimental data.
引文
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[2]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[3]TAO S,ZHANG T,YANG J,et al.Bearing fault diagnosis method based on stacked autoencoder and softmax regression[C]//Proceedings of the Control Conference,2015:6331-6335.
[4]JIA F,LEI Y,LIN J,et al.Deep neural networks:Apromising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J].Mechanical Systems&Signal Processing,2016,72:303-315.
[5]LEI Y,JIA F,LIN J,et al.An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J].IEEE Transactions on Industrial Electronics,2016,63(5):3137-3147.
[6]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.
[7]SUN W,SHAO S,ZHAO R,et al.A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J].Measurement,2016,89:171-188.
[8]陈仁祥,杨星,杨黎霞,等.栈式稀疏加噪自编码深度神经网络的滚动轴承损伤程度诊断[J].振动与冲击,2017,36(21):125-131.CHEN Renxiang,YANG Xing,YANG Lixia,et al.Fault severity diagnosis method for rolling bearings based on a stacked sparse denoising auto-encoder[J].Shock and Vibration,2017,36(21):125-131.
[9]CHEN R,CHEN S,HE M,et al.Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion[J].Journal of Risk&Reliability,2017,231(6):666-679.
[10]AHMED H O A,WONG M L D,NANDI A K.Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features[J].Mechanical Systems&Signal Processing,2018,99:459-477.
[11]CHEN L,WANG Z Y,QIN W L,et al.Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J].Signal Processing,2017,130:377-388.
[12]SCHOLKOPF B,SMOLA A,MULLER K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
[13]MCDONALD G L,ZHAO Q,ZUO M J.Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems&Signal Processing,2012,33(1):237-255.
[14]CHEN X,ZHANG B,FENG F,et al.Optimal resonant band demodulation based on an improved correlated Kurtosis and its application in bearing fault diagnosis[J].Sensors,2017,17(2):360-379.
[15]FENG J,LEI Y,SHAN H,et al.Early fault diagnosis of bearings using an improved spectral Kurtosis by maximum correlated Kurtosis deconvolution[J].Sensors,2015,15(11):29363-29377.
[16]ZHOU H,CHEN J,DONG G.Application of maximum correlated Kurtosis deconvolution on rolling element bearing fault diagnosis[M].London:Springer International Publishing,2015.
[17]TANG G,WANG X,HE Y.Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution[J].Journal of Mechanical Science and Technology,2016,30(1):43-54.