| 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 | 734 | 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 | 263 | 2017 |
| Wind turbine fault detection using a denoising autoencoder with temporal information G Jiang, P Xie, H He, J Yan IEEE/Asme transactions on mechatronics 23 (1), 89-100, 2017 | 262 | 2017 |
| A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data Q He, Y Pang, G Jiang, P Xie IEEE transactions on industrial informatics 17 (10), 6875-6884, 2020 | 84 | 2020 |
| DeepLab-based spatial feature extraction for hyperspectral image classification Z Niu, W Liu, J Zhao, G Jiang IEEE Geoscience and Remote Sensing Letters 16 (2), 251-255, 2018 | 76 | 2018 |
| Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data Y Pang, Q He, G Jiang, P Xie Renewable Energy 161, 510-524, 2020 | 75 | 2020 |
| A multi-level-denoising autoencoder approach for wind turbine fault detection X Wu, G Jiang, X Wang, P Xie, X Li Ieee Access 7, 59376-59387, 2019 | 67 | 2019 |
| Early fault detection of wind turbines based on operational condition clustering and optimized deep belief network modeling H Wang, H Wang, G Jiang, J Li, Y Wang Energies 12 (6), 984, 2019 | 53 | 2019 |
| An unsupervised multiview sparse filtering approach for current-based wind turbine gearbox fault diagnosis Q He, J Zhao, G Jiang, P Xie IEEE Transactions on Instrumentation and Measurement 69 (8), 5569-5578, 2020 | 49 | 2020 |
| Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine J Li, M Li, J Zhang, G Jiang Measurement 133, 421-432, 2019 | 38 | 2019 |
| Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals G Jiang, C Jia, S Nie, X Wu, Q He, P Xie Measurement 196, 111159, 2022 | 35 | 2022 |
| Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds P Liang, B Wang, G Jiang, N Li, L Zhang Engineering Applications of Artificial Intelligence 118, 105656, 2023 | 33 | 2023 |
| A multimodal approach for identifying autism spectrum disorders in children J Han, G Jiang, G Ouyang, X Li IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 2003-2011, 2022 | 28 | 2022 |
| DeepFedWT: A federated deep learning framework for fault detection of wind turbines G Jiang, WP Fan, W Li, L Wang, Q He, P Xie, X Li Measurement 199, 111529, 2022 | 26 | 2022 |
| Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning GQ Jiang, P Xie, X Wang, M Chen, Q He Chinese Journal of Mechanical Engineering 30, 1314-1324, 2017 | 26 | 2017 |
| Two-level multi-domain feature extraction on sparse representation for motor imagery classification C Xu, C Sun, G Jiang, X Chen, Q He, P Xie Biomedical Signal Processing and Control 62, 102160, 2020 | 24 | 2020 |
| Dual residual attention network for remaining useful life prediction of bearings G Jiang, W Zhou, Q Chen, Q He, P Xie Measurement 199, 111424, 2022 | 23 | 2022 |
| M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis J Cui, P Xie, X Wang, J Wang, Q He, G Jiang Measurement 204, 112085, 2022 | 22 | 2022 |
| 基于 PCA 和多变量极限学习机的轴承剩余寿命预测 何群， 李磊， 江国乾， 谢平 中国机械工程 25 (7), 984, 2014 | 22 | 2014 |
| Intelligent fault diagnosis of gearbox based on vibration and current signals: a multimodal deep learning approach G Jiang, J Zhao, C Jia, Q He, P Xie, Z Meng 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-6, 2019 | 21 | 2019 |
Ping XieYanshan University在 ysu.edu.cn 的电子邮件经过验证
