Generative adversarial networks for data augmentation in machine fault diagnosis

Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process., 115, 213, 10.1016/j.ymssp.2018.05.050

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

Ma, 2018, Deep coupling autoencoder for fault diagnosis with multimodal sensory data, IEEE Trans. Industr. Inform., 14, 1137, 10.1109/TII.2018.2793246

Shao, 2018, Highly-accurate machine fault diagnosis using deep transfer learning, IEEE Trans. Industr. Inform.

Goodfellow, 2014, Generative adversarial nets, Adv. Neural Inf. Process. Syst., 2672

Arjovsky, 2017, Wasserstein gan, arXiv preprint

Gulrajani, 2017, Improved training of wasserstein gans, Adv. Neural Inf. Process. Syst., 5767

Radford, 2015, Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv preprint

Odena, 2016, Semi-supervised learning with generative adversarial networks, arXiv preprint

Odena, 2016, Conditional image synthesis with auxiliary classifier GANs, arXiv preprint

Donahue, 2018, Synthesizing audio with generative adversarial networks, arXiv preprint

Hartmann, 2018, EEG-GAN: generative adversarial networks for electroencephalographic (EEG) brain signals, arXiv preprint

Kindermans, 2017, Learning how to explain neural networks: PatternNet and PatternAttribution, arXiv preprint

Springenberg, 2015, Unsupervised and semi-supervised learning with categorical generative adversarial networks, arXiv preprint

Salimans, 2016, Improved techniques for training gans, Adv. Neural Inf. Process. Syst., 2234

Heusel, 2017, Gans trained by a two time-scale update rule converge to a local nash equilibrium, Adv. Neural Inf. Process. Syst., 6626

Peyré, 2017

Diederik, 2014, ADAM: a method for stochastic optimization, arXiv preprint arXiv

Yang, 2015, Induction motor fault diagnosis using multiple class feature selection, in Proc, IEEE Int. Instrum. Meas. Technol. Conf., 256