Title / AuthorYear
Deep learning for smart manufacturing: Methods and applications
J Wang, Y Ma, L Zhang, RX Gao, D Wu
Journal of Manufacturing Systems 48, 144-156
2018
Artificial intelligence for fault diagnosis of rotating machinery: A review
R Liu, B Yang, E Zio, X Chen
Mechanical Systems and Signal Processing 108, 33-47
2018
A new convolutional neural network-based data-driven fault diagnosis method
L Wen, X Li, L Gao, Y Zhang
IEEE Transactions on Industrial Electronics 65 (7), 5990-5998
2017
A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals
W Zhang, G Peng, C Li, Y Chen, Z Zhang
Sensors 17 (2), 425
2017
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
H Shao, H Jiang, H Zhao, F Wang
Mechanical Systems and Signal Processing 95, 187-204
2017
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
L Jing, M Zhao, P Li, X Xu
Measurement 111, 1-10
2017
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
F Jia, Y Lei, L Guo, J Lin, S Xing
Neurocomputing 272, 619-628
2018
Machine health monitoring using local feature-based gated recurrent unit networks
R Zhao, D Wang, R Yan, K Mao, F Shen, J Wang
IEEE Transactions on Industrial Electronics 65 (2), 1539-1548
2017
A new deep transfer learning based on sparse auto-encoder for fault diagnosis
L Wen, L Gao, X Li
IEEE Transactions on Systems, Man, and Cybernetics: Systems 49 (1), 136-144
2017
Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network
H Shao, H Jiang, H Zhang, T Liang
IEEE Transactions on Industrial Electronics 65 (3), 2727-2736
2017
An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox
L Jing, T Wang, M Zhao, P Wang
Sensors 17 (2), 414
2017
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
F Jia, Y Lei, N Lu, S Xing
Mechanical Systems and Signal Processing 110, 349-367
2018
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
H Shao, H Jiang, H Zhang, W Duan, T Liang, S Wu
Mechanical Systems and Signal Processing 100, 743-765
2018
Convolutional discriminative feature learning for induction motor fault diagnosis
W Sun, R Zhao, R Yan, S Shao, X Chen
IEEE Transactions on Industrial Informatics 13 (3), 1350-1359
2017
A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
H Shao, H Jiang, Y Lin, X Li
Mechanical Systems and Signal Processing 102, 278-297
2018
Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox
G Jiang, H He, J Yan, P Xie
IEEE Transactions on Industrial Electronics 66 (4), 3196-3207
2018
Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data
L Guo, Y Lei, S Xing, T Yan, N Li
IEEE Transactions on Industrial Electronics 66 (9), 7316-7325
2018
Stacked multilevel-denoising autoencoders: A new representation learning approach for wind turbine gearbox fault diagnosis
G Jiang, H He, P Xie, Y Tang
IEEE Transactions on Instrumentation and Measurement 66 (9), 2391-2402
2017
Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes
M Zhao, M Kang, B Tang, M Pecht
IEEE Transactions on Industrial Electronics 65 (5), 4290-4300
2017
Highly accurate machine fault diagnosis using deep transfer learning
S Shao, S McAleer, R Yan, P Baldi
IEEE Transactions on Industrial Informatics 15 (4), 2446-2455
2018
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