Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is “small, light, and fast,” and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery.

[1]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[2]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[3]  Milos Manic,et al.  Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware , 2018, IEEE Industrial Electronics Magazine.

[4]  Wei Gao,et al.  A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network , 2018, Sensors.

[5]  Yonghong Zhang,et al.  Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network , 2017, Chinese Journal of Mechanical Engineering.

[6]  Aidong Men,et al.  Rate-distortion optimal wavelet packet transform for low bit rate video coding , 2009, 2009 IEEE International Workshop on Imaging Systems and Techniques.

[7]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[8]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[9]  Tao Zhang,et al.  A Survey of Model Compression and Acceleration for Deep Neural Networks , 2017, ArXiv.

[10]  Myeongsu Kang,et al.  Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes , 2018, IEEE Transactions on Industrial Electronics.

[11]  Jun Wang,et al.  Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network , 2017 .

[12]  이창기.  Long Short-Term Memory 기반의 Recurrent Neural Network를 이용한 개체명 인식 , 2015 .

[13]  Bin Yan,et al.  Insulator detection and recognition of explosion based on convolutional neural networks , 2019, Int. J. Wavelets Multiresolution Inf. Process..

[14]  Jian Sun,et al.  Convolutional neural networks at constrained time cost , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[16]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[17]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Thomas R. Kurfess,et al.  Rolling element bearing diagnostics in run-to-failure lifetime testing , 2001 .

[19]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Haidong Shao,et al.  An enhancement deep feature fusion method for rotating machinery fault diagnosis , 2017, Knowl. Based Syst..

[22]  Ruqiang Yan,et al.  Machine health monitoring with LSTM networks , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[23]  Wei Yu,et al.  A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends , 2018, IEEE Access.

[24]  C. O. Alenoghena,et al.  Enhanced discrete wavelet packet sub-band frequency edge detection using Hilbert transform , 2018, Int. J. Wavelets Multiresolution Inf. Process..

[25]  Purushottam Gangsar,et al.  Multi-fault Diagnosis of Induction Motor at Intermediate Operating Conditions using Wavelet Packet Transform and Support Vector Machine , 2018 .

[26]  Stéphane Mallat,et al.  Rigid-Motion Scattering for Texture Classification , 2014, ArXiv.

[27]  Xi Zhao,et al.  Accurate landmarking from 3D facial scans by CNN and cascade regression , 2018, Int. J. Wavelets Multiresolution Inf. Process..

[28]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[29]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[30]  Qin Wei,et al.  Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[31]  Wen Yu,et al.  Randomized algorithms for nonlinear system identification with deep learning modification , 2016, Inf. Sci..

[32]  Jae-Yoon Jung,et al.  LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks , 2018, Sensors.

[33]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[34]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[35]  Diego Cabrera,et al.  Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .

[36]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Ruqiang Yan,et al.  Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks , 2017, Sensors.

[38]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[39]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[40]  Honglak Lee,et al.  Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units , 2016, ICML.

[41]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[42]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[43]  Tao Zhang,et al.  Deep Model Based Domain Adaptation for Fault Diagnosis , 2017, IEEE Transactions on Industrial Electronics.

[44]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[45]  Yimin Shao,et al.  Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults , 2018 .

[46]  Xuefeng Chen,et al.  Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine , 2017, IEEE Transactions on Industrial Informatics.

[47]  Binqiang Chen,et al.  An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network , 2017, Materials.

[48]  Zhibin Zhao,et al.  Sparse Deep Stacking Network for Fault Diagnosis of Motor , 2018, IEEE Transactions on Industrial Informatics.

[49]  Hod Lipson,et al.  Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.

[50]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.