| Machinery health prognostics: A systematic review from data acquisition to RUL prediction Y Lei, N Li, L Guo, N Li, T Yan, J Lin Mechanical systems and signal processing 104, 799-834, 2018 | 2335 | 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 | 1141 | 2018 |
| Deep separable convolutional network for remaining useful life prediction of machinery B Wang, Y Lei, N Li, T Yan Mechanical systems and signal processing 134, 106330, 2019 | 354 | 2019 |
| Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery B Wang, Y Lei, T Yan, N Li, L Guo Neurocomputing 379, 117-129, 2020 | 286 | 2020 |
| Machinery health indicator construction based on convolutional neural networks considering trend burr L Guo, Y Lei, N Li, T Yan, N Li Neurocomputing 292, 142-150, 2018 | 280 | 2018 |
| A Wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability N Li, Y Lei, T Yan, N Li, T Han IEEE Transactions on Industrial Electronics 66 (3), 2092-2101, 2018 | 206 | 2018 |
| XJTU-SY rolling element bearing accelerated life test datasets: A tutorial 雷亚国， 韩天宇， 王彪， 李乃鹏， 闫涛， 杨军 Journal of Mechanical Engineering 55 (16), 1-6, 2019 | 191 | 2019 |
| Remaining useful life prediction based on a multi-sensor data fusion model N Li, N Gebraeel, Y Lei, X Fang, X Cai, T Yan Reliability Engineering & System Safety 208, 107249, 2021 | 127 | 2021 |
| Remaining useful life prediction based on a general expression of stochastic process models N Li, Y Lei, L Guo, T Yan, J Lin IEEE Transactions on Industrial Electronics 64 (7), 5709-5718, 2017 | 116 | 2017 |
| Degradation modeling and remaining useful life prediction for dependent competing failure processes T Yan, Y Lei, N Li, B Wang, W Wang Reliability Engineering & System Safety 212, 107638, 2021 | 95 | 2021 |
| Residual convolution long short-term memory network for machines remaining useful life prediction and uncertainty quantification W Wang, Y Lei, T Yan, N Li, A Nandi Journal of Dynamics, Monitoring and Diagnostics 1 (1), 2-8, 2022 | 70 | 2022 |
| Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions T Yan, Y Lei, B Wang, T Han, X Si, N Li Reliability Engineering & System Safety 202, 106994, 2020 | 62 | 2020 |
| Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery Y Wang, Y Lei, N Li, T Yan, X Si Reliability Engineering & System Safety 231, 109006, 2023 | 27 | 2023 |
| Ice detection method by using SCADA data on wind turbine blades LI Ningbo, Y Tao, LI Naipeng, K Detong, LIU Qingchao, LEI Yaguo Power Generation Technology 39 (1), 58, 2018 | 27 | 2018 |
| Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method T Yan, Y Lei, N Li, X Si, L Pintelon, R Dewil Mechanical Systems and Signal Processing 173, 109053, 2022 | 21 | 2022 |
| Machinery health prognostics with multimodel fusion degradation modeling N Li, Y Lei, X Liu, T Yan, P Xu IEEE Transactions on Industrial Electronics 70 (11), 11764-11773, 2022 | 15 | 2022 |
| Joint optimization of maintenance and spare parts inventory for multi-unit systems with a generalized structure T Yan, Y Lei, N Li, L Pintelon, R Dewil Journal of Manufacturing Science and Engineering 145 (4), 041001, 2023 | 5 | 2023 |
| Remaining Useful Life Prediction Based on Multi-channel Attention Bidirectional Long Short-term Memory Network W Wang, B Wang, N Li, Y Lei, T Yan 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and …, 2021 | 5 | 2021 |
| Remaining useful life prediction of machinery subjected to two-phase degradation process T Yan, Y Lei, N Li 2018 IEEE International Conference on Prognostics and Health Management …, 2018 | 4 | 2018 |