| Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network Z Chen, W Li IEEE Transactions on instrumentation and measurement 66 (7), 1693-1702, 2017 | 902 | 2017 |
| A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges W Li, R Huang, J Li, Y Liao, Z Chen, G He, R Yan, K Gryllias Mechanical Systems and Signal Processing 167, 108487, 2022 | 605 | 2022 |
| State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network F Yang, W Li, C Li, Q Miao Energy 175, 66-75, 2019 | 458 | 2019 |
| A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks Z Chen, A Mauricio, W Li, K Gryllias Mechanical Systems and Signal Processing 140, 106683, 2020 | 402 | 2020 |
| State-of-charge estimation of lithium-ion batteries using LSTM and UKF F Yang, S Zhang, W Li, Q Miao Energy 201, 117664, 2020 | 367 | 2020 |
| Mechanical fault diagnosis using convolutional neural networks and extreme learning machine Z Chen, K Gryllias, W Li Mechanical systems and signal processing 133, 106272, 2019 | 343 | 2019 |
| Bearing performance degradation assessment using long short-term memory recurrent network B Zhang, S Zhang, W Li Computers in Industry 106, 14-29, 2019 | 323 | 2019 |
| Viscoelastic properties of MR elastomers under harmonic loading WH Li, Y Zhou, TF Tian Rheologica acta 49, 733-740, 2010 | 316 | 2010 |
| Microstructures and viscoelastic properties of anisotropic magnetorheological elastomers L Chen, XL Gong, WH Li Smart materials and structures 16 (6), 2645, 2007 | 299 | 2007 |
| Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network Z Chen, K Gryllias, W Li IEEE Transactions on Industrial Informatics 16 (1), 339-349, 2019 | 296 | 2019 |
| Recent progress of particle migration in viscoelastic fluids D Yuan, Q Zhao, S Yan, SY Tang, G Alici, J Zhang, W Li Lab on a Chip 18 (4), 551-567, 2018 | 253 | 2018 |
| A hybrid deep-learning model for fault diagnosis of rolling bearings Y Xu, Z Li, S Wang, W Li, T Sarkodie-Gyan, S Feng Measurement 169, 108502, 2021 | 252 | 2021 |
| Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery Z Chen, G He, J Li, Y Liao, K Gryllias, W Li IEEE Transactions on Instrumentation and Measurement 69 (11), 8702-8712, 2020 | 242 | 2020 |
| Inertial particle separation by differential equilibrium positions in a symmetrical serpentine micro-channel J Zhang, S Yan, R Sluyter, W Li, G Alici, NT Nguyen Scientific reports 4 (1), 4527, 2014 | 241 | 2014 |
| A highly adjustable magnetorheological elastomer base isolator for applications of real-time adaptive control Y Li, J Li, T Tian, W Li Smart Materials and Structures 22 (9), 095020, 2013 | 240 | 2013 |
| Development of an MR-brake-based haptic device B Liu, WH Li, PB Kosasih, XZ Zhang Smart materials and structures 15 (6), 1960, 2006 | 239 | 2006 |
| Semi-active variable stiffness vibration control of vehicle seat suspension using an MR elastomer isolator H Du, W Li, N Zhang Smart materials and structures 20 (10), 105003, 2011 | 237 | 2011 |
| Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system J Wang, Z Lu, M Li, N Lior, W Li Energy 175, 1246-1258, 2019 | 205 | 2019 |
| Deep decoupling convolutional neural network for intelligent compound fault diagnosis R Huang, Y Liao, S Zhang, W Li Ieee Access 7, 1848-1858, 2018 | 195 | 2018 |
| Experimental study and modeling of a novel magnetorheological elastomer isolator J Yang, H Du, W Li, Y Li, J Li, S Sun, HX Deng Smart Materials and Structures 22 (11), 117001, 2013 | 187 | 2013 |
Zhuyun Chen / 陈祝云School of ElectroMechanical Engineering, Guangdong University of TechnologyPotvrđena adresa e-pošte na gdut.edu.cn
