Fault diagnosis of wind turbine based on Long Short-term memory networks J Lei, C Liu, D Jiang Renewable Energy 133, 422-432, 2019 | 459 | 2019 |
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application T Han, C Liu, W Yang, D Jiang ISA transactions 97, 269-281, 2020 | 453 | 2020 |
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults T Han, C Liu, W Yang, D Jiang Knowledge-Based Systems 165, 474-487, 2019 | 409 | 2019 |
Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions T Han, C Liu, W Yang, D Jiang ISA transactions 93, 341-353, 2019 | 157 | 2019 |
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems T Han, C Liu, L Wu, S Sarkar, D Jiang Mechanical Systems and Signal Processing 117, 170-187, 2019 | 155 | 2019 |
Short-term prediction of wind power using EMD and chaotic theory X An, D Jiang, M Zhao, C Liu Communications in Nonlinear Science and Numerical Simulation 17 (2), 1036-1042, 2012 | 148 | 2012 |
Deep transfer learning with limited data for machinery fault diagnosis T Han, C Liu, R Wu, D Jiang Applied Soft Computing 103, 107150, 2021 | 147 | 2021 |
Wind farm power prediction based on wavelet decomposition and chaotic time series X An, D Jiang, C Liu, M Zhao Expert Systems with Applications 38 (9), 11280-11285, 2011 | 109 | 2011 |
Casing vibration response prediction of dual-rotor-blade-casing system with blade-casing rubbing N Wang, C Liu, D Jiang, K Behdinan Mechanical Systems and Signal Processing 118, 61-77, 2019 | 94 | 2019 |
Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing X An, D Jiang, J Chen, C Liu Journal of Vibration and Control 18 (2), 240-245, 2012 | 84 | 2012 |
Global geometric similarity scheme for feature selection in fault diagnosis C Liu, D Jiang, W Yang Expert Systems with Applications 41 (8), 3585-3595, 2014 | 79 | 2014 |
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring W Yang, C Liu, D Jiang Renewable Energy 127, 230-241, 2018 | 71 | 2018 |
Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network C Liu, A Akintayo, Z Jiang, GP Henze, S Sarkar Applied Energy 211, 1106-1122, 2018 | 71 | 2018 |
Predicting County Level Corn Yields Using Deep Long Short Term Memory Models Z Jiang, C Liu, NP Hendricks, B Ganapathysubramanian, DJ Hayes, ... arXiv preprint arXiv:1805.12044, 2018 | 67 | 2018 |
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS C Liu, S Ghosal, Z Jiang, S Sarkar Proceedings of the 7th International Conference on Cyber-Physical Systems, 1, 2016 | 67 | 2016 |
Damage identification of wind turbine blades with deep convolutional neural networks J Guo, C Liu, J Cao, D Jiang Renewable Energy 174, 122-133, 2021 | 64 | 2021 |
Crack modeling of rotating blades with cracked hexahedral finite element method C Liu, D Jiang Mechanical Systems and Signal Processing 46 (2), 406-423, 2014 | 55 | 2014 |
A hierarchical scheme for remaining useful life prediction with long short-term memory networks T Song, C Liu, R Wu, Y Jin, D Jiang Neurocomputing, https://doi.org/10.1016/j.neucom.2022.02, 2022 | 51 | 2022 |
Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series X Li, J Cao, J Guo, C Liu, W Wang, Z Jia, T Su Ocean Engineering 248, 110689, 2022 | 48 | 2022 |
Machine condition classification using deterioration feature extraction and anomaly determination D Jiang, C Liu Reliability, IEEE Transactions on 60 (1), 41-48, 2011 | 47 | 2011 |