Chao Liu
Chao Liu
Department of Energy and Power Engineering, Tsinghua University, China
Verified email at - Homepage
Cited by
Cited by
Fault diagnosis of wind turbine based on Long Short-term memory networks
J Lei, C Liu, D Jiang
Renewable Energy 133, 422-432, 2019
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
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
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
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
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
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
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
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
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
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
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
Machine condition classification using deterioration feature extraction and anomaly determination
D Jiang, C Liu
Reliability, IEEE Transactions on 60 (1), 41-48, 2011
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
W Yang, C Liu, D Jiang
Renewable Energy 127, 230-241, 2018
Bridge damage detection using spatiotemporal patterns extracted from dense sensor network
C Liu, Y Gong, S Laflamme, B Phares, S Sarkar
Measurement Science and Technology 28 (1), 014011, 2017
An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling
C Liu, S Ghosal, Z Jiang, S Sarkar
Cyber-Physical Systems 3 (1-4), 66-102, 2017
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
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
Linked read technology for assembling large complex and polyploid genomes
A Ott, JC Schnable, CT Yeh, L Wu, C Liu, HC Hu, CL Dalgard, S Sarkar, ...
BMC genomics 19 (1), 1-15, 2018
Energy prediction using spatiotemporal pattern networks
Z Jiang, C Liu, A Akintayo, GP Henze, S Sarkar
Applied Energy 206, 1022-1039, 2017
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