| Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression Z Xue, Y Zhang, C Cheng, G Ma Neurocomputing 376, 95-102, 2020 | 378 | 2020 |
| Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network G Ma, Y Zhang, C Cheng, B Zhou, P Hu, Y Yuan 🔋 Applied Energy 253, 113626, 2019 | 309 | 2019 |
| Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data C Cheng, B Zhou, G Ma, D Wu, Y Yuan Neurocomputing 409, 35-45, 2020 | 264 | 2020 |
| A deep learning-based remaining useful life prediction approach for bearings C Cheng, G Ma, Y Zhang, M Sun, F Teng, H Ding, Y Yuan IEEE/ASME Transactions on Mechatronics 25 (3), 1243-1254, 2020 | 198 | 2020 |
| Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE Y Zhang, Y Xin, Z Liu, M Chi, G Ma* Reliability Engineering & System Safety 220, 108263, 2022 | 177 | 2022 |
| A general end-to-end diagnosis framework for manufacturing systems Y Yuan, G Ma, C Cheng, B Zhou, H Zhao, HT Zhang, H Ding ⚙ National Science Review 7 (2), 418-429, 2020 | 155 | 2020 |
| Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning G Ma, S Xu, B Jiang, C Cheng, X Yang, Y Shen, T Yang, Y Huang, H Ding, ... 🚗 Energy & Environmental Science 15 (10), 4083-4094, 2022 | 113 | 2022 |
| An overview of data-driven battery health estimation technology for battery management system M Chen, G Ma, W Liu, N Zeng, X Luo Neurocomputing 532, 152-169, 2023 | 86 | 2023 |
| A transfer learning-based method for personalized state of health estimation of lithium-ion batteries G Ma, S Xu, T Yang, Z Du, L Zhu, H Ding, Y Yuan IEEE Transactions on Neural Networks and Learning Systems 35 (1), 759-769, 2022 | 72 | 2022 |
| Data fusion generative adversarial network for multi-class imbalanced fault diagnosis of rotating machinery Q Liu, G Ma, C Cheng IEEE Access 8, 70111-70124, 2020 | 52 | 2020 |
| A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries G Ma, Z Wang, W Liu, J Fang, Y Zhang, H Ding, Y Yuan Knowledge-Based Systems 259, 110012, 2023 | 48 | 2023 |
| Fault detection of lithium-ion battery packs with a graph-based method G Ma, S Xu, C Cheng Journal of Energy Storage 43, 103209, 2021 | 44 | 2021 |
| Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach G Ma, Z Wang, W Liu, J Fang, Y Zhang, H Ding, Y Yuan IEEE/CAA Journal of Automatica Sinica 10 (7), 1530-1543, 2023 | 33 | 2023 |
| Remaining useful life prediction of lithium-ion battery based on attention mechanism with positional encoding B Zhou, C Cheng, G Ma, Y Zhang IOP Conference Series: Materials Science and Engineering 895 (1), 012006, 2020 | 32 | 2020 |
| Data-driven discovery of stochastic differential equations Y Wang, H Fang, J Jin, G Ma, X He, X Dai, Z Yue, C Cheng, HT Zhang, ... Engineering 17, 244-252, 2022 | 28 | 2022 |
| A novel gan-based fault diagnosis approach for imbalanced industrial time series W Jiang, C Cheng, B Zhou, G Ma, Y Yuan arXiv preprint arXiv:1904.00575, 2019 | 25 | 2019 |
| State of health estimation for lithium-ion batteries with dynamic time warping and deep kernel learning model P Hu, G Ma, Y Zhang, C Cheng, B Zhou, Y Yuan ECC 2020, 602-607, 2020 | 13 | 2020 |
| Generative Adversarial Network Based Multi-class Imbalanced Fault Diagnosis of Rolling Bearing Q Liu, G Ma, C Cheng ICSRS 2019, 318-324, 2019 | 5 | 2019 |
| ERMN: An enhanced meta-learning approach for state of health estimation of lithium-ion batteries G Ma, X Yang, S Xu, C Cheng, X He Journal of Energy Storage 72, 108628, 2023 | 4 | 2023 |
| RMDA: A Regressive Multiple-Source Domain Adaption Approach for Early Prediction of Lithium-ion Battery Lifetime S Xu, X Yang, G Ma, Y Yuan CCC 2023, 6981-6986, 2023 | 2 | 2023 |
Cheng ChengHuazhong university of science and technologyE-mailová adresa ověřena na: hust.edu.cn