| Deep learning and its applications to machine health monitoring R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao Mechanical Systems and Signal Processing 115, 213-237, 2019 | 2507 | 2019 |
| Deep learning for smart manufacturing: Methods and applications J Wang, Y Ma, L Zhang, RX Gao, D Wu Journal of manufacturing systems 48, 144-156, 2018 | 1666 | 2018 |
| Wavelets for fault diagnosis of rotary machines: A review with applications R Yan, RX Gao, X Chen Signal processing 96, 1-15, 2014 | 1441 | 2014 |
| PCA-based feature selection scheme for machine defect classification A Malhi, RX Gao IEEE transactions on instrumentation and measurement 53 (6), 1517-1525, 2004 | 729 | 2004 |
| A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests D Wu, C Jennings, J Terpenny, RX Gao, S Kumara Journal of Manufacturing Science and Engineering 139 (7), 071018, 2017 | 661 | 2017 |
| Wavelets: Theory and applications for manufacturing RX Gao, R Yan Springer Science & Business Media, 2010 | 623 | 2010 |
| Approximate entropy as a diagnostic tool for machine health monitoring R Yan, RX Gao Mechanical Systems and Signal Processing 21 (2), 824-839, 2007 | 527 | 2007 |
| Symbiotic human-robot collaborative assembly L Wang, R Gao, J Váncza, J Krüger, XV Wang, S Makris, G Chryssolouris CIRP annals 68 (2), 701-726, 2019 | 504 | 2019 |
| Digital Twin for rotating machinery fault diagnosis in smart manufacturing J Wang, L Ye, RX Gao, C Li, L Zhang International Journal of Production Research 57 (12), 3920-3934, 2019 | 481 | 2019 |
| Cloud-enabled prognosis for manufacturing R Gao, L Wang, R Teti, D Dornfeld, S Kumara, M Mori, M Helu CIRP annals 64 (2), 749-772, 2015 | 460 | 2015 |
| Long short-term memory for machine remaining life prediction J Zhang, P Wang, R Yan, RX Gao Journal of manufacturing systems 48, 78-86, 2018 | 418 | 2018 |
| Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines R Yan, Y Liu, RX Gao Mechanical Systems and Signal Processing 29, 474-484, 2012 | 415 | 2012 |
| Hilbert–Huang transform-based vibration signal analysis for machine health monitoring R Yan, RX Gao IEEE Transactions on Instrumentation and measurement 55 (6), 2320-2329, 2006 | 408 | 2006 |
| Machine learning-based image processing for on-line defect recognition in additive manufacturing A Caggiano, J Zhang, V Alfieri, F Caiazzo, R Gao, R Teti CIRP annals 68 (1), 451-454, 2019 | 392 | 2019 |
| Artificial intelligence in advanced manufacturing: Current status and future outlook JF Arinez, Q Chang, RX Gao, C Xu, J Zhang Journal of Manufacturing Science and Engineering 142 (11), 110804, 2020 | 381 | 2020 |
| Prognosis of defect propagation based on recurrent neural networks A Malhi, R Yan, RX Gao IEEE Transactions on Instrumentation and Measurement 60 (3), 703-711, 2011 | 332 | 2011 |
| DCNN-based multi-signal induction motor fault diagnosis S Shao, R Yan, Y Lu, P Wang, RX Gao IEEE Transactions on Instrumentation and Measurement 69 (6), 2658-2669, 2019 | 322 | 2019 |
| WaveletKernelNet: An interpretable deep neural network for industrial intelligent diagnosis T Li, Z Zhao, C Sun, L Cheng, X Chen, R Yan, RX Gao IEEE Transactions on Systems, Man, and Cybernetics: Systems 52 (4), 2302-2312, 2021 | 319 | 2021 |
| A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing D Wu, S Liu, L Zhang, J Terpenny, RX Gao, T Kurfess, JA Guzzo Journal of Manufacturing Systems 43, 25-34, 2017 | 318 | 2017 |
| Performance enhancement of ensemble empirical mode decomposition J Zhang, R Yan, RX Gao, Z Feng Mechanical Systems and Signal Processing 24 (7), 2104-2123, 2010 | 285 | 2010 |
Ruqiang Yan / 严如强UMass,UConn,SEU,CASE,XJTUยืนยันอีเมลแล้วที่ ieee.org
