| 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 | 1559 | 2018 |
| Machine health monitoring using local feature-based gated recurrent unit networks R Zhao, D Wang, R Yan, K Mao, F Shen, J Wang IEEE Transactions on Industrial Electronics 65 (2), 1539-1548, 2017 | 740 | 2017 |
| Learning to monitor machine health with convolutional bi-directional LSTM networks R Zhao, R Yan, J Wang, K Mao Sensors 17 (2), 273, 2017 | 717 | 2017 |
| 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 | 447 | 2019 |
| Machine vision intelligence for product defect inspection based on deep learning and Hough transform J Wang, P Fu, RX Gao Journal of Manufacturing Systems 51, 52-60, 2019 | 245 | 2019 |
| Machine health monitoring with LSTM networks R Zhao, J Wang, R Yan, K Mao 2016 10th international conference on sensing technology (ICST), 1-6, 2016 | 233 | 2016 |
| A new paradigm of cloud-based predictive maintenance for intelligent manufacturing J Wang, L Zhang, L Duan, RX Gao Journal of Intelligent Manufacturing 28, 1125-1137, 2017 | 214 | 2017 |
| Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing J Wang, J Xie, R Zhao, L Zhang, L Duan Robotics and computer-integrated manufacturing 45, 47-58, 2017 | 196 | 2017 |
| Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction J Wang, J Yan, C Li, RX Gao, R Zhao Computers in Industry 111, 1-14, 2019 | 185 | 2019 |
| Physics guided neural network for machining tool wear prediction J Wang, Y Li, R Zhao, RX Gao Journal of Manufacturing Systems 57, 298-310, 2020 | 154 | 2020 |
| An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples J Wang, Y Liang, Y Zheng, RX Gao, F Zhang Renewable energy 145, 642-650, 2020 | 151 | 2020 |
| Digital twin for machining tool condition prediction Q Qiao, J Wang, L Ye, RX Gao Procedia CIRP 81, 1388-1393, 2019 | 147 | 2019 |
| On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on transfer component analysis J Xie, L Zhang, L Duan, J Wang 2016 ieee international conference on prognostics and health management …, 2016 | 143 | 2016 |
| Multilevel information fusion for induction motor fault diagnosis J Wang, P Fu, L Zhang, RX Gao, R Zhao IEEE/ASME Transactions on Mechatronics 24 (5), 2139-2150, 2019 | 138 | 2019 |
| Enhanced particle filter for tool wear prediction J Wang, P Wang, RX Gao Journal of Manufacturing Systems 36, 35-45, 2015 | 136 | 2015 |
| A multi-scale convolution neural network for featureless fault diagnosis J Wang, J Zhuang, L Duan, W Cheng 2016 international symposium on flexible automation (isfa), 65-70, 2016 | 127 | 2016 |
| A new support vector data description method for machinery fault diagnosis with unbalanced datasets L Duan, M Xie, T Bai, J Wang Expert Systems with Applications 64, 239-246, 2016 | 122 | 2016 |
| Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain F Zhang, D Johnson, M Johnson, D Watkins, R Froese, J Wang Renewable Energy 85, 740-748, 2016 | 114 | 2016 |
| Integration of EEMD and ICA for wind turbine gearbox diagnosis J Wang, RX Gao, R Yan Wind Energy 17 (5), 757-773, 2014 | 99 | 2014 |
| Current envelope analysis for defect identification and diagnosis in induction motors J Wang, S Liu, RX Gao, R Yan Journal of Manufacturing Systems 31 (4), 380-387, 2012 | 85 | 2012 |
Rui Zhao(赵锐)Head of Data & Quant, PluangDirección de correo verificada de e.ntu.edu.sg
