| A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction J Li, X Li, D He IEEE Access 7, 75464-75475, 2019 | 263 | 2019 |
| Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning LI Xueyi, LI Jialin, QU Yongzhi, HE David Chinese Journal of Aeronautics 33 (2), 418-426, 2020 | 111 | 2020 |
| Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals X Li, J Li, Y Qu, D He Applied Sciences 9 (4), 768, 2019 | 96 | 2019 |
| Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection X Li, J Li, C Zhao, Y Qu, D He Mechanical Systems and Signal Processing 142, 106740, 2020 | 88 | 2020 |
| Effects of hydrogen direct-injection angle and charge concentration on gasoline-hydrogen blending lean combustion in a Wankel engine C Shi, C Ji, S Wang, J Yang, X Li, Y Ge Energy Conversion and Management 187, 316-327, 2019 | 66 | 2019 |
| Attention recurrent autoencoder hybrid model for early fault diagnosis of rotating machinery X Kong, X Li, Q Zhou, Z Hu, C Shi IEEE Transactions on Instrumentation and Measurement 70, 1-10, 2021 | 64 | 2021 |
| Numerical simulation on combustion process of a hydrogen direct-injection stratified gasoline Wankel engine by synchronous and asynchronous ignition modes C Shi, C Ji, S Wang, J Yang, X Li, Y Ge Energy conversion and management 183, 14-25, 2019 | 63 | 2019 |
| Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor J Li, X Li, D He, Y Qu Journal of Intelligent Manufacturing 31, 1899-1916, 2020 | 60 | 2020 |
| Potential improvement in combustion and pollutant emissions of a hydrogen-enriched rotary engine by using novel recess configuration C Shi, Z Zhang, C Ji, X Li, L Di, Z Wu Chemosphere 299, 134491, 2022 | 48 | 2022 |
| A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network J Li, X Li, D He, Y Qu Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2020 | 41 | 2020 |
| A novel method for early gear pitting fault diagnosis using stacked SAE and GBRBM J Li, X Li, D He, Y Qu Sensors 19 (4), 758, 2019 | 36 | 2019 |
| Domain adaptation remaining useful life prediction method based on AdaBN-DCNN J Li, X Li, D He 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-6, 2019 | 32 | 2019 |
| A study on fault diagnosis of bearing pitting under different speed condition based on an improved inception capsule network X Li, X Kong, J Zhang, Z Hu, C Shi Measurement 181, 109656, 2021 | 30 | 2021 |
| Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning X Li, J Li, D He, Y Qu University of Illinois at Chicago, 2019 | 26 | 2019 |
| Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout J Zhang, K Xiangwei, LI Xueyi, HU Zhiyong, L Cheng, YU Mingzhu Chinese Journal of Aeronautics 35 (10), 301-312, 2022 | 20 | 2022 |
| A novel framework for early pitting fault diagnosis of rotating machinery based on dilated CNN combined with spatial dropout X Li, X Kong, Z Liu, Z Hu, C Shi Ieee Access 9, 29243-29252, 2021 | 16 | 2021 |
| Early gear pitting fault diagnosis based on bi-directional LSTM X Li, J Li, C Zhao, Y Qu, D He 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-5, 2019 | 15 | 2019 |
| Fusing joint distribution and adversarial networks: A new transfer learning method for intelligent fault diagnosis X Li, T Yu, X Wang, D Li, Z Xie, X Kong Applied Acoustics 216, 109767, 2024 | 12 | 2024 |
| Research on fault diagnosis of highway Bi-LSTM based on attention mechanism X Li, K Su, Q He, X Wang, Z Xie Eksploatacja i Niezawodność 25 (2), 2023 | 11 | 2023 |
| Unsupervised gear fault diagnosis using raw vibration signal based on deep learning X Li, Z Liu, Y Qu, D He 2018 Prognostics and System Health Management Conference (PHM-Chongqing …, 2018 | 8 | 2018 |
David HeDepartment of Mechanical and Industrial Engineering, The University of Illinois at Chicago在 uic.edu 的电子邮件经过验证
