Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine

Abstract Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical equipment operation. Recent works show that deep learning-based methods outperform most of traditional fault diagnosis techniques. However, a practical problem comes up in these studies, where deep learning models cannot be well trained and the classification accuracy is greatly affected because of the sample-imbalance problem, which means that there are a large amount of normal data but few fault samples. To solve the problem, an enhanced generative adversarial network (E-GAN) is proposed. Firstly, the deep convolutional generative adversarial network (DCGAN) is utilized to generate more samples to balance the training set. Then, by integrating K-means clustering algorithm, we developed a modified CNN diagnosis model for fault classification. The experiment results demonstrate that the proposed E-GAN can greatly improve the classification accuracy and is superior to the compared methods.

[1]  Yuichi Yoshida,et al.  Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.

[2]  Kilian Q. Weinberger,et al.  An empirical study on evaluation metrics of generative adversarial networks , 2018, ArXiv.

[3]  Karthik Kashinath,et al.  Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems , 2019, J. Comput. Phys..

[4]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[6]  Chuan Li,et al.  Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals , 2021 .

[7]  Konstantinos Gryllias,et al.  Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine , 2019, Mechanical Systems and Signal Processing.

[8]  Yang Li,et al.  Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks , 2020, Journal of Ambient Intelligence and Humanized Computing.

[9]  Reza Hassannejad,et al.  A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults , 2020, Expert Syst. Appl..

[10]  Philip H. S. Torr,et al.  Multi-agent Diverse Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Wessel N. van Wieringen,et al.  Testing for pathway (in)activation by using Gaussian graphical models , 2018 .

[12]  Xi Li,et al.  A lightweight neural network with strong robustness for bearing fault diagnosis , 2020 .

[13]  Wei Zhang,et al.  Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation , 2020, Neurocomputing.

[14]  Angel Domingo Sappa,et al.  Infrared Image Colorization Based on a Triplet DCGAN Architecture , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Bin Zhang,et al.  Bearing performance degradation assessment using long short-term memory recurrent network , 2019, Comput. Ind..

[16]  Adam Glowacz,et al.  Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery , 2021, IEEE Transactions on Instrumentation and Measurement.

[17]  Huaguang Zhang,et al.  A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets , 2019, IEEE Transactions on Industrial Informatics.

[18]  Long Wang,et al.  A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine , 2019, IEEE Internet of Things Journal.

[19]  Jun Zhou,et al.  Adaptive hash retrieval with kernel based similarity , 2018, Pattern Recognit..

[20]  Chuanyan Xu,et al.  Misfire Detection Based on Generalized Force Identification at the Engine Centre of Gravity , 2019, IEEE Access.

[21]  Konstantinos Gryllias,et al.  A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks , 2020 .

[22]  Ru-Ze Liang,et al.  Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison , 2016, Neural Computing and Applications.

[23]  David Pfau,et al.  Unrolled Generative Adversarial Networks , 2016, ICLR.

[24]  Jianyu Long,et al.  Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots , 2020 .

[25]  Kamla Prasan Ray,et al.  EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases , 2021, IEEE Transactions on Instrumentation and Measurement.

[26]  Zhibin Zhao,et al.  Few-shot transfer learning for intelligent fault diagnosis of machine , 2020 .

[27]  Lei Zhang,et al.  Regrouping particle swarm optimization based variable neural network for gearbox fault diagnosis , 2018, Journal of Intelligent & Fuzzy Systems.

[28]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Xiaoli Xu,et al.  Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings , 2020 .

[30]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[31]  Guolin He,et al.  A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery , 2021, IEEE Transactions on Industrial Informatics.

[32]  Xiaoyang Liu,et al.  FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults , 2020, IEEE Transactions on Industrial Informatics.

[33]  Zhiqiang Chen,et al.  Deep neural networks-based rolling bearing fault diagnosis , 2017, Microelectron. Reliab..

[34]  C. C. Pain,et al.  Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network , 2020, Computer Methods in Applied Mechanics and Engineering.

[35]  Konstantinos Gryllias,et al.  Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[36]  En Zhu,et al.  Large-scale k-means clustering via variance reduction , 2018, Neurocomputing.

[37]  Adam Glowacz,et al.  Fault diagnosis of single-phase induction motor based on acoustic signals , 2019, Mechanical Systems and Signal Processing.

[38]  Ming-Yu Liu,et al.  Coupled Generative Adversarial Networks , 2016, NIPS.

[39]  Mengxiao Hu,et al.  Exploring Bias in GAN-based Data Augmentation for Small Samples , 2019, ArXiv.

[40]  Ruyi Huang,et al.  Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis , 2019, IEEE Access.

[41]  Jun Jo,et al.  Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[42]  Ruqiang Yan,et al.  Generative adversarial networks for data augmentation in machine fault diagnosis , 2019, Comput. Ind..

[43]  Wentao Mao,et al.  Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study , 2019, IEEE Access.

[44]  Prabhat,et al.  Comparative Analysis of Deep Convolutional Generative Adversarial Network and Conditional Generative Adversarial Network using Hand Written Digits , 2020, 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).

[45]  Jiawei Xiang,et al.  Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN) , 2020 .

[46]  Jing Lin,et al.  Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder , 2020 .

[47]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[48]  Li Jiang,et al.  A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery , 2020 .

[49]  Jun Wang,et al.  An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition , 2018, Neurocomputing.

[50]  Chenglin Wen,et al.  Deep learning fault diagnosis method based on global optimization GAN for unbalanced data , 2020, Knowl. Based Syst..

[51]  Minping Jia,et al.  Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery , 2020 .

[52]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[53]  Fang Deng,et al.  Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty , 2020, Neurocomputing.

[54]  Junmei Wang,et al.  Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors. , 2019, Molecular pharmaceutics.

[55]  Pradipta Kishore Dash,et al.  Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression , 2017, Appl. Soft Comput..

[56]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[57]  Jianyu Long,et al.  Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis , 2020, IEEE Transactions on Fuzzy Systems.

[58]  José Cristóbal Riquelme Santos,et al.  Creation of Synthetic Data with Conditional Generative Adversarial Networks , 2019, SOCO.

[59]  Xiaojie Su,et al.  Fault Detection Filter Design for Nonlinear Singular Systems With Markovian Jump Parameters , 2020 .

[60]  Yang Wang,et al.  A Novel Method for Imbalanced Fault Diagnosis of Rotating Machinery Based on Generative Adversarial Networks , 2021, IEEE Transactions on Instrumentation and Measurement.