Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Guo, 2016, Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis, Measurement, 93, 490, 10.1016/j.measurement.2016.07.054

Ren, 2019, A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning, Measurement, 148, 10.1016/j.measurement.2019.106906

Ding, 2014, Data-driven design of observer-based fault diagnosis systems, 175

Zhang, 2015, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM, Measurement, 69, 164, 10.1016/j.measurement.2015.03.017

Razavi-Far, 2019, A semi-supervised diagnostic framework based on the surface estimation of faulty distributions, IEEE Trans. Industr. Inf., 15, 1277, 10.1109/TII.2018.2851961

Li, 2015, Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis, Neurocomputing, 168, 119, 10.1016/j.neucom.2015.06.008

S. Hu, Y. Liang, L. Ma, Y. He, MSMOTE: Improving classification performance when training data is imbalanced, in: Proceedings of Second International Workshop on Computer Science and Engineering, vol. 2, 2009, pp. 13–17.

G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, C. Malossi, BAGAN: data augmentation with balancing GAN, arXiv preprint arXiv:1803.09655.

M. Mirza, S. Osindero, Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784.

Chawla, 2002, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953

T. Han, C. Liu, W. Yang, D. Jiang, Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions, ISA Transactions.

Guo, 2018, Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data, IEEE Trans. Industr. Electron.

Yang, 2019, An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings, Mech. Syst. Signal Process., 122, 692, 10.1016/j.ymssp.2018.12.051

Li, 2018, A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning, Neurocomputing, 310, 77, 10.1016/j.neucom.2018.05.021

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Nets, Curran Associates Inc, 2014.

H. Han, W.-Y. Wang, B.-H. Mao, Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning, in: Proceedings of Advances in Intelligent Computing, Berlin, Heidelberg, 2005, pp. 878–887.

Haibo, 2008, ADASYN Adaptive synthetic sampling approach for imbalanced learning, 1322

C. Bunkhumpornpat, K. Sinapiromsaran, C. Lursinsap, Safe-Level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, in: Proceedings of Advances in Knowledge Discovery and Data Mining, Berlin, Heidelberg, 2009, pp. 475–482.

Ma, 2015, Review on dynamics of cracked gear systems, Eng. Fail. Anal., 55, 224, 10.1016/j.engfailanal.2015.06.004

Luo, 2019, Research on vibration performance of the nonlinear combined support-flexible rotor system, Nonlinear Dyn., 98, 113, 10.1007/s11071-019-05176-2

Yu, 2019, A combined polynomial chirplet transform and synchroextracting technique for analyzing nonstationary signals of rotating machinery, IEEE Trans. Instrum. Meas.

Jiang, 2019, A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines, Mech. Syst. Signal Process., 116, 668, 10.1016/j.ymssp.2018.07.014

Jiang, 2018, Initial center frequency-guided VMD for fault diagnosis of rotating machines, J. Sound Vib., 435, 36, 10.1016/j.jsv.2018.07.039

Shen, 2018, An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder, Eng. Appl. Artif. Intell., 76, 170, 10.1016/j.engappai.2018.09.010

Li, 2019, Multi-layer domain adaptation method for rolling bearing fault diagnosis, Signal Processing, 157, 180, 10.1016/j.sigpro.2018.12.005

L. Wen, L. Gao, X. Li, A new deep transfer learning based on sparse auto-encoder for fault diagnosis, IEEE Trans. Syst., Man, Cybern.: Syst. (99) (2017) 1–9.

Li, 2019, Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places, IEEE Trans. Industr. Electron.

Li, 2019, Diagnosing rotating machines with weakly supervised data using deep transfer learning, IEEE Trans. Industr. Inf.

Lu, 2017, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Signal Processing, 130, 377, 10.1016/j.sigpro.2016.07.028

Sun, 2017, Convolutional discriminative feature learning for induction motor fault diagnosis, IEEE Trans. Industr. Inf., 13, 1350, 10.1109/TII.2017.2672988

Li, 2019, Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks, IEEE Trans. Industr. Electron., 66, 5525, 10.1109/TIE.2018.2868023

Guo, 2017, A recurrent neural network based health indicator for remaining useful life prediction of bearings, Neurocomputing, 240, 98, 10.1016/j.neucom.2017.02.045

Lei, 2016, An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data, IEEE Trans. Industr. Electron., 63, 3137, 10.1109/TIE.2016.2519325

Sun, 2016, A sparse auto-encoder-based deep neural network approach for induction motor faults classification, Measurement, 89, 171, 10.1016/j.measurement.2016.04.007

Jia, 2018, Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization, Mech. Syst. Signal Process., 110, 349, 10.1016/j.ymssp.2018.03.025

Mao, 2017, Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine, Mech. Syst. Signal Process., 83, 450, 10.1016/j.ymssp.2016.06.024

Martin-Diaz, 2017, Early fault detection in induction motors using AdaBoost with imbalanced small data and optimized sampling, IEEE Trans. Ind. Appl., 53, 3066, 10.1109/TIA.2016.2618756

Zhang, 2018, Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning, J. Manuf. Syst., 48, 34, 10.1016/j.jmsy.2018.04.005

X. Li, W. Zhang, Q. Ding, J.-Q. Sun, Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation, J. Intell. Manuf.https://doi.org/10.1007/s10845-018-1456-1.

Khan, 2018, Towards bearing health prognosis using generative adversarial networks: Modeling bearing degradation, 1

Wang, 2018, An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition, Neurocomputing, 310, 213, 10.1016/j.neucom.2018.05.024

Mao, 2019, Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study, IEEE Access, 7, 9515, 10.1109/ACCESS.2018.2890693

Cabrera, 2019, Generative adversarial networks selection approach for extremely imbalanced fault diagnosis of reciprocating machinery, IEEE Access, 7, 70643, 10.1109/ACCESS.2019.2917604

F. Zhou, S. Yang, H. Fujita, D. Chen, C. Wen, Deep learning fault diagnosis method based on global optimization GAN for unbalanced data, Knowledge-Based Syst.

A.L. Maas, A.Y. Hannun, A.Y. Ng, Rectifier nonlinearities improve neural network acoustic models, in: Proceedings of 30th International Conference on Machine Learning, vol. 28, 2013.

S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: Proceedings of 32nd International Conference on Machine Learning, vol. 1, Lile, France, 2015, pp. 448–456.

Smith, 2015, Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study, Mech. Syst. Signal Process., 64–65, 100, 10.1016/j.ymssp.2015.04.021

Lema, 2017, Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning, J. Mach. Learn. Res., 18, 559

D. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980.