| 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 | 517 | 2017 |
| Rolling bearing fault diagnosis using an optimization deep belief network H Shao, H Jiang, X Zhang, M Niu Measurement Science and Technology 26 (11), 115002, 2015 | 431 | 2015 |
| 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 | 360 | 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 | 350 | 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 | 314 | 2018 |
| Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine S Haidong, J Hongkai, L Xingqiu, W Shuaipeng Knowledge-Based Systems 140, 1-14, 2018 | 264 | 2018 |
| An enhancement deep feature fusion method for rotating machinery fault diagnosis H Shao, H Jiang, F Wang, H Zhao Knowledge-Based Systems 119, 200-220, 2017 | 259 | 2017 |
| Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet H Shao, H Jiang, F Wang, Y Wang ISA transactions 69, 187-201, 2017 | 228 | 2017 |
| Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images H Shao, M Xia, G Han, Y Zhang, J Wan IEEE Transactions on Industrial Informatics 17 (5), 3488-3496, 2020 | 206 | 2020 |
| Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder H Zhiyi, S Haidong, J Lin, C Junsheng, Y Yu Measurement 152, 107393, 2020 | 170 | 2020 |
| Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing S Haidong, C Junsheng, J Hongkai, Y Yu, W Zhantao Knowledge-Based Systems 188, 105022, 2020 | 166 | 2020 |
| An adaptive deep convolutional neural network for rolling bearing fault diagnosis W Fuan, J Hongkai, S Haidong, D Wenjing, W Shuaipeng Measurement Science and Technology 28 (9), 095005, 2017 | 161 | 2017 |
| Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions Z He, H Shao, X Zhong, X Zhao Knowledge-Based Systems 207, 106396, 2020 | 159 | 2020 |
| Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples Z He, H Shao, P Wang, JJ Lin, J Cheng, Y Yang Knowledge-Based Systems 191, 105313, 2020 | 152 | 2020 |
| Rolling bearing fault detection using continuous deep belief network with locally linear embedding H Shao, H Jiang, X Li, T Liang Computers in Industry 96, 27-39, 2018 | 138 | 2018 |
| Snorkel drybell: A case study in deploying weak supervision at industrial scale SH Bach, D Rodriguez, Y Liu, C Luo, H Shao, C Xia, S Sen, A Ratner, ... Proceedings of the 2019 International Conference on Management of Data, 362-375, 2019 | 133 | 2019 |
| Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network H Jiang, X Li, H Shao, K Zhao Measurement Science and Technology 29 (6), 065107, 2018 | 125 | 2018 |
| A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance H Shao, J Lin, L Zhang, D Galar, U Kumar Information Fusion 74, 65-76, 2021 | 114 | 2021 |
| Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds H Cao, H Shao, X Zhong, Q Deng, X Yang, J Xuan Journal of Manufacturing Systems 62, 186-198, 2022 | 103 | 2022 |
| Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning M Xia, H Shao, D Williams, S Lu, L Shu, CW de Silva Reliability Engineering & System Safety 215, 107938, 2021 | 103 | 2021 |
