| Digital twin-driven intelligent assessment of gear surface degradation K Feng, JC Ji, Y Zhang, Q Ni, Z Liu, M Beer Mechanical Systems and Signal Processing 186, 109896, 2023 | 346 | 2023 |
| A review of vibration-based gear wear monitoring and prediction techniques K Feng, JC Ji, Q Ni, M Beer Mechanical Systems and Signal Processing 182, 109605, 2023 | 303 | 2023 |
| A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis Q Ni, JC Ji, K Feng, B Halkon Mechanical Systems and Signal Processing 164, 108216, 2022 | 208 | 2022 |
| Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics Q Ni, JC Ji, B Halkon, K Feng, AK Nandi Mechanical Systems and Signal Processing 200, 110544, 2023 | 196 | 2023 |
| Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing Y Zhang, JC Ji, Z Ren, Q Ni, F Gu, K Feng, K Yu, J Ge, Z Lei, Z Liu Reliability Engineering & System Safety 234, 109186, 2023 | 183 | 2023 |
| Physics-Informed LSTM hyperparameters selection for gearbox fault detection Y Chen, M Rao, K Feng, MJ Zuo Mechanical Systems and Signal Processing 171, 108907, 2022 | 158 | 2022 |
| Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network Q Ni, JC Ji, K Feng IEEE Transactions on Industrial Informatics 19 (2), 1301-1311, 2022 | 153 | 2022 |
| Vibration-based updating of wear prediction for spur gears K Feng, P Borghesani, WA Smith, RB Randall, ZY Chin, J Ren, Z Peng Wear 426, 1410-1415, 2019 | 146 | 2019 |
| Use of cyclostationary properties of vibration signals to identify gear wear mechanisms and track wear evolution K Feng, WA Smith, P Borghesani, RB Randall, Z Peng Mechanical Systems and Signal Processing 150, 107258, 2021 | 129 | 2021 |
| A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy Y Li, K Feng, X Liang, MJ Zuo Journal of Sound and Vibration 439, 271-286, 2019 | 129 | 2019 |
| A systematic review on imbalanced learning methods in intelligent fault diagnosis Z Ren, T Lin, K Feng, Y Zhu, Z Liu, K Yan IEEE Transactions on Instrumentation and Measurement, 2023 | 116 | 2023 |
| CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery Y Xu, K Feng, X Yan, R Yan, Q Ni, B Sun, Z Lei, Y Zhang, Z Liu Information Fusion, 2023 | 113 | 2023 |
| Vibration-based monitoring and prediction of surface profile change and pitting density in a spur gear wear process K Feng, WA Smith, RB Randall, H Wu, Z Peng Mechanical Systems and Signal Processing 165, 108319, 2022 | 112 | 2022 |
| Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit Q Ni, JC Ji, K Feng, Y Zhang, D Lin, J Zheng Reliability Engineering & System Safety 242, 109753, 2023 | 102 | 2023 |
| A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system K Feng, JC Ji, Q Ni, Y Li, W Mao, L Liu Wear, 204697, 2023 | 99 | 2023 |
| Universal source-free domain adaptation method for cross-domain fault diagnosis of machines Y Zhang, Z Ren, K Feng, K Yu, M Beer, Z Liu Mechanical Systems and Signal Processing 191, 2023 | 95 | 2023 |
| WavCapsNet: An Interpretable Intelligent Compound Fault Diagnosis Method by Backward Tracking W Li, H Lan, J Chen, K Feng, R Huang IEEE Transactions on Instrumentation and Measurement, 2023 | 94 | 2023 |
| Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing Y Zhang, Z Ren, S Zhou, K Feng, K Yu, Z Liu IEEE/ASME Transactions on Mechatronics 27 (6), 5371-5380, 2022 | 85 | 2022 |
| Digital twin enabled domain adversarial graph networks for bearing fault diagnosis K Feng, Y Xu, Y Wang, S Li, Q Jiang, B Sun, J Zheng, Q Ni IEEE Transactions on Industrial Cyber-Physical Systems, 2023 | 78 | 2023 |
| Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning Y Zhang, K Yu, Z Lei, J Ge, Y Xu, Z Li, Z Ren, K Feng Expert Systems with Applications 232, 120854, 2023 | 73 | 2023 |
Michael BeerLeibniz Universität HannoverE-mailová adresa ověřena na: irz.uni-hannover.de
