| A systematic review of deep transfer learning for machinery fault diagnosis C Li, S Zhang, Y Qin, E Estupinan Neurocomputing 407, 121-135, 2020 | 338 | 2020 |
| Bearing performance degradation assessment using long short-term memory recurrent network B Zhang, S Zhang, W Li Computers in Industry 106, 14-29, 2019 | 288 | 2019 |
| State-of-charge estimation of lithium-ion batteries using LSTM and UKF F Yang, S Zhang, W Li, Q Miao Energy 201, 117664, 2020 | 283 | 2020 |
| Deep decoupling convolutional neural network for intelligent compound fault diagnosis R Huang, Y Liao, S Zhang, W Li Ieee Access 7, 1848-1858, 2018 | 177 | 2018 |
| Evolving deep echo state networks for intelligent fault diagnosis J Long, S Zhang, C Li IEEE Transactions on Industrial Informatics 16 (7), 4928-4937, 2019 | 156 | 2019 |
| Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots J Long, J Mou, L Zhang, S Zhang, C Li Journal of manufacturing systems 61, 736-745, 2021 | 111 | 2021 |
| Semisupervised distance-preserving self-organizing map for machine-defect detection and classification W Li, S Zhang, G He IEEE Transactions on Instrumentation and Measurement 62 (5), 869-879, 2013 | 111 | 2013 |
| Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor D Cabrera, A Guamán, S Zhang, M Cerrada, RV Sanchez, J Cevallos, ... Neurocomputing 380, 51-66, 2020 | 106 | 2020 |
| Feature denoising and nearest–farthest distance preserving projection for machine fault diagnosis W Li, S Zhang, S Rakheja IEEE Transactions on Industrial Informatics 12 (1), 393-404, 2015 | 100 | 2015 |
| Deep fuzzy echo state networks for machinery fault diagnosis S Zhang, Z Sun, M Wang, J Long, Y Bai, C Li IEEE Transactions on Fuzzy Systems 28 (7), 1205-1218, 2019 | 92 | 2019 |
| A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem J Long, Z Sun, PM Pardalos, Y Hong, S Zhang, C Li Information Sciences 478, 40-61, 2019 | 92 | 2019 |
| A novel sparse echo autoencoder network for data-driven fault diagnosis of delta 3-D printers J Long, Z Sun, C Li, Y Hong, Y Bai, S Zhang IEEE Transactions on Instrumentation and Measurement 69 (3), 683-692, 2019 | 75 | 2019 |
| Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine R Wang, S Zhang, Z Chen, W Li Measurement 180, 109467, 2021 | 71 | 2021 |
| Generative adversarial networks selection approach for extremely imbalanced fault diagnosis of reciprocating machinery D Cabrera, F Sancho, J Long, RV Sánchez, S Zhang, M Cerrada, C Li IEEE Access 7, 70643-70653, 2019 | 68 | 2019 |
| Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders S Zhang, Z Sun, J Long, C Li, Y Bai Computers in Industry 105, 164-176, 2019 | 56 | 2019 |
| Mechanical fault time series prediction by using EFMSAE-LSTM neural network J Guo, Z Lao, M Hou, C Li, S Zhang Measurement 173, 108566, 2021 | 52 | 2021 |
| Deep hybrid state network with feature reinforcement for intelligent fault diagnosis of delta 3-D printers S Zhang, Z Sun, C Li, D Cabrera, J Long, Y Bai IEEE Transactions on Industrial Informatics 16 (2), 779-789, 2019 | 46 | 2019 |
| Fault diagnosis of delta 3D printers using transfer support vector machine with attitude signals J Guo, J Wu, Z Sun, J Long, S Zhang IEEE Access 7, 40359-40368, 2019 | 43 | 2019 |
| Dual-attention generative adversarial networks for fault diagnosis under the class-imbalanced conditions R Wang, Z Chen, S Zhang, W Li IEEE Sensors Journal 22 (2), 1474-1485, 2021 | 41 | 2021 |
| Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion network Z Pu, C Li, S Zhang, Y Bai IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2020 | 35 | 2020 |
Weihua LiProf. with School of Mechanical & Automotive Engineering, South China University of Technology在 scut.edu.cn 的电子邮件经过验证
