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Guoqian Jiang / 江国乾
标题
引用次数
引用次数
年份
Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox
G Jiang, H He, J Yan, P Xie
IEEE Transactions on Industrial Electronics 66 (4), 3196-3207, 2018
7342018
Stacked multilevel-denoising autoencoders: A new representation learning approach for wind turbine gearbox fault diagnosis
G Jiang, H He, P Xie, Y Tang
IEEE Transactions on Instrumentation and Measurement 66 (9), 2391-2402, 2017
2632017
Wind turbine fault detection using a denoising autoencoder with temporal information
G Jiang, P Xie, H He, J Yan
IEEE/Asme transactions on mechatronics 23 (1), 89-100, 2017
2622017
A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data
Q He, Y Pang, G Jiang, P Xie
IEEE transactions on industrial informatics 17 (10), 6875-6884, 2020
842020
DeepLab-based spatial feature extraction for hyperspectral image classification
Z Niu, W Liu, J Zhao, G Jiang
IEEE Geoscience and Remote Sensing Letters 16 (2), 251-255, 2018
762018
Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data
Y Pang, Q He, G Jiang, P Xie
Renewable Energy 161, 510-524, 2020
752020
A multi-level-denoising autoencoder approach for wind turbine fault detection
X Wu, G Jiang, X Wang, P Xie, X Li
Ieee Access 7, 59376-59387, 2019
672019
Early fault detection of wind turbines based on operational condition clustering and optimized deep belief network modeling
H Wang, H Wang, G Jiang, J Li, Y Wang
Energies 12 (6), 984, 2019
532019
An unsupervised multiview sparse filtering approach for current-based wind turbine gearbox fault diagnosis
Q He, J Zhao, G Jiang, P Xie
IEEE Transactions on Instrumentation and Measurement 69 (8), 5569-5578, 2020
492020
Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine
J Li, M Li, J Zhang, G Jiang
Measurement 133, 421-432, 2019
382019
Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals
G Jiang, C Jia, S Nie, X Wu, Q He, P Xie
Measurement 196, 111159, 2022
352022
Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds
P Liang, B Wang, G Jiang, N Li, L Zhang
Engineering Applications of Artificial Intelligence 118, 105656, 2023
332023
A multimodal approach for identifying autism spectrum disorders in children
J Han, G Jiang, G Ouyang, X Li
IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 2003-2011, 2022
282022
DeepFedWT: A federated deep learning framework for fault detection of wind turbines
G Jiang, WP Fan, W Li, L Wang, Q He, P Xie, X Li
Measurement 199, 111529, 2022
262022
Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning
GQ Jiang, P Xie, X Wang, M Chen, Q He
Chinese Journal of Mechanical Engineering 30, 1314-1324, 2017
262017
Two-level multi-domain feature extraction on sparse representation for motor imagery classification
C Xu, C Sun, G Jiang, X Chen, Q He, P Xie
Biomedical Signal Processing and Control 62, 102160, 2020
242020
Dual residual attention network for remaining useful life prediction of bearings
G Jiang, W Zhou, Q Chen, Q He, P Xie
Measurement 199, 111424, 2022
232022
M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis
J Cui, P Xie, X Wang, J Wang, Q He, G Jiang
Measurement 204, 112085, 2022
222022
基于 PCA 和多变量极限学习机的轴承剩余寿命预测
何群, 李磊, 江国乾, 谢平
中国机械工程 25 (7), 984, 2014
222014
Intelligent fault diagnosis of gearbox based on vibration and current signals: a multimodal deep learning approach
G Jiang, J Zhao, C Jia, Q He, P Xie, Z Meng
2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-6, 2019
212019
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