| 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 | 1921 | 2018 |
| Applications of machine learning to machine fault diagnosis: A review and roadmap Y Lei, B Yang, X Jiang, F Jia, N Li, AK Nandi Mechanical Systems and Signal Processing 138, 106587, 2020 | 1721 | 2020 |
| A hybrid prognostics approach for estimating remaining useful life of rolling element bearings B Wang, Y Lei, N Li, N Li IEEE Transactions on Reliability 69 (1), 401-412, 2018 | 1071 | 2018 |
| A recurrent neural network based health indicator for remaining useful life prediction of bearings L Guo, N Li, F Jia, Y Lei, J Lin Neurocomputing 240, 98-109, 2017 | 1066 | 2017 |
| 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 | 942 | 2018 |
| An improved exponential model for predicting remaining useful life of rolling element bearings N Li, Y Lei, J Lin, SX Ding IEEE Transactions on Industrial Electronics 62 (12), 7762-7773, 2015 | 552 | 2015 |
| A model-based method for remaining useful life prediction of machinery Y Lei, N Li, S Gontarz, J Lin, S Radkowski, J Dybala IEEE Transactions on reliability 65 (3), 1314-1326, 2016 | 500 | 2016 |
| 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 | 280 | 2019 |
| 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 | 242 | 2018 |
| A new method based on stochastic process models for machine remaining useful life prediction Y Lei, N Li, J Lin IEEE Transactions on Instrumentation and Measurement 65 (12), 2671-2684, 2016 | 228 | 2016 |
| Applications of stochastic resonance to machinery fault detection: A review and tutorial Z Qiao, Y Lei, N Li Mechanical Systems and Signal Processing 122, 502-536, 2019 | 226 | 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 | 222 | 2020 |
| A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines B Yang, Y Lei, F Jia, N Li, Z Du IEEE Transactions on Industrial Electronics 67 (11), 9747-9757, 2019 | 179 | 2019 |
| Multiscale convolutional attention network for predicting remaining useful life of machinery B Wang, Y Lei, N Li, W Wang IEEE Transactions on Industrial Electronics 68 (8), 7496-7504, 2020 | 177 | 2020 |
| 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 | 175 | 2018 |
| Subdomain adaptation transfer learning network for fault diagnosis of roller bearings Z Wang, X He, B Yang, N Li IEEE Transactions on Industrial Electronics 69 (8), 8430-8439, 2021 | 137 | 2021 |
| Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network Z Wang, W Zhao, W Du, N Li, J Wang Process Safety and Environmental Protection 149, 591-601, 2021 | 111 | 2021 |
| 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 | 104 | 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 | 102 | 2017 |
| A new fault diagnosis method based on adaptive spectrum mode extraction Z Wang, N Yang, N Li, W Du, J Wang Structural Health Monitoring 20 (6), 3354-3370, 2021 | 97 | 2021 |
Yaguo Lei / 雷亚国Professor of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University在 mail.xjtu.edu.cn 的电子邮件经过验证
