旋转机械一维深度卷积神经网络故障诊断研究

PDF(1506 KB)

振动与冲击 ›› 2018, Vol. 37 ›› Issue (23) : 31-37.

论文

周奇才，刘星辰，赵炯，沈鹤鸿，熊肖磊

作者信息 +

Fault diagnosis for rotating machinery based on 1D depth convolutional neural network

ZHOU Qicai， LIU Xingchen， ZHAO Jiong， SHEN Hehong， XIONG Xiaolei

Author information +

文章历史 +

摘要

针对旋转机械故障特征需要人工提取、复杂故障识别困难和诊断模型鲁棒性差的问题，本文在经典卷积神经网络AlexNet基础上，提出基于一维深度卷积神经网络的故障诊断模型，模型采用改进的一维卷积核和池化层以适应一维时域信号。相比传统智能诊断模型的人工特征提取和故障分类两阶段模式，本模型将两者合二为一：首先利用多个交替的卷积层和池化层完成原始信号自适应特征学习，然后结合全连接层实现故障诊断。通过轴承和齿轮箱健康状态监测实验表明，本文提出模型可以实现高精度、稳定和快速的故障诊断，并与BP神经网络、SVM、一维LeNet5模型和经典AlexNet模型对比，证明本文提出模型的优势，最后通过PCA可视化分析说明模型在特征提取上的有效性。

Abstract

Aiming at problems of rotating machinery’s fault features needing to be extracted manually,complex fault recognition being difficult and diagnosis model’s poor robustness,a novel 1D depth convolutional neural network-based rotating machinery fault diagnosis model was proposed based on the classical convolution neural network model AlexNet.This new model adopted the modified 1D convolutional kernel and pool layers to adapt 1D time domain signals.The traditional intelligent diagnosis model included two distinct modules of manual feature extraction and classification,and the proposed model combined these two modules into one.With the proposed model,multiple alternate convolution and pool layers were used to complete learning the original signal’s self-adaptive features and then all connected layers were combined to realize fault diagnosis.Bearings and gearboxes health monitoring tests showed that the proposed model can realize accurate,stable and fast fault diagnosis; compared to BP neural network,SVM,1D-LeNet5 model and the classical AlexNet model,this new model is the best; the feature extraction effectiveness of the proposed model is verified with the PCA visualized analysis.

导出引用

周奇才，刘星辰，赵炯，沈鹤鸿，熊肖磊. 旋转机械一维深度卷积神经网络故障诊断研究[J]. 振动与冲击, 2018, 37(23): 31-37

ZHOU Qicai， LIU Xingchen， ZHAO Jiong， SHEN Hehong， XIONG Xiaolei. Fault diagnosis for rotating machinery based on 1D depth convolutional neural network[J]. Journal of Vibration and Shock, 2018, 37(23): 31-37

参考文献

[1] Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance[J]. Mechanical Systems & Signal Processing, 2006, 20(7):1483-1510.
[2] Heng A, Zhang S, Tan A C C, et al. Rotating machinery prognostics: State of the art, challenges and opportunities[J]. Mechanical Systems & Signal Processing, 2009, 23(3):724-739.
[3] Widodo A, Kim E Y, Son J D, et al. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine[J]. Expert Systems with Applications, 2009, 36(3):7252-7261.
[4] Liang B, Iwnicki S D, Zhao Y. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis[J]. Mechanical Systems & Signal Processing, 2013, 39(1-2):342-360.
[5] Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89(3):16-27.
[6] Yu J. Health Condition Monitoring of Machines Based on Hidden Markov Model and Contribution Analysis[J]. IEEE Transactions on Instrumentation & Measurement, 2012, 61(8):2200-2211.
[7] Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89(3):16-27.
[8] Ke L, Peng C, Wang S. An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network[J]. Sensors, 2012, 12(5):5919-39.
[9] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[10] Zhao R, Yan R, Chen Z, et al. Deep Learning and Its Applications to Machine Health Monitoring: A Survey[J]. 2016.
[11] 任浩, 屈剑锋, 柴毅,等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8):1345-1358.
Ren Hao; Qu Jian-feng; Chai Yi; Tang Qiu; Ye Xin. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8):1345-1358.
[12] 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 Hong-Li; Zhang Yi-Wen; Huang Hai-feng. Research on bearing condition monitoring based on deep learning[J]. Journal of Vibration and Shock, 2016, 35(12):167-171.
[13] Jia F, Lei Y, 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, 72-73:303-315.
[14] Shao H, Jiang H, Zhao H, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems & Signal Processing, 2017, 95:187-204.
[15] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems. Lake Tahoe, Curran Associates Inc. 2012:1097-1105.
[16] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition. Boston, IEEE, 2015:1-9.
[17] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]. Computer Vision and Pattern Recognition. Las Vegas, IEEE, 2016:770-778.
[18] Wen L, Li X, Gao L, et al. A New Convolutional Neural Network Based Data-Driven Fault Diagnosis Method[J]. IEEE Transactions on Industrial Electronics, 2017, PP(99):1-1.
[19] Ince T, Kiranyaz S, Eren L, et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11):7067-7075.
[20] Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems & Signal Processing, 2018, 100:439-453.
[21] Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery[J]. Journal of Sound & Vibration, 2016, 377:331-345.
[22] Chen Z Q, Li C, Sanchez R V. Gearbox Fault Identification and Classification with Convolutional Neural Networks[J]. Shock and Vibration,2015,(2015-10-13), 2015, 2015(2):1-10.
[23] LÉcun, Yann, Bottou, Leon, Bengio, Yoshua, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[24] Fawzi A, Samulowitz H, Turaga D, et al. Adaptive data augmentation for image classification[C]. Phoenix, IEEE International Conference on Image Processing. IEEE, 2016:3688-3692.
[25] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 2010, 9:249-256