Haidong Shao
Haidong Shao
Associate Professor, College of Mechanical and Vehicle Engineering, Hunan University,
Потвърден имейл адрес: hnu.edu.cn
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
H Shao, H Jiang, H Zhao, F Wang
Mechanical Systems and Signal Processing 95, 187-204, 2017
Rolling bearing fault diagnosis using an optimization deep belief network
H Shao, H Jiang, X Zhang, M Niu
Measurement Science and Technology 26 (11), 115002, 2015
Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network
H Shao, H Jiang, H Zhang, T Liang
IEEE Transactions on Industrial Electronics 65 (3), 2727-2736, 2017
A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
H Shao, H Jiang, Y Lin, X Li
Mechanical Systems and Signal Processing 102, 278-297, 2018
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
H Shao, H Jiang, H Zhang, W Duan, T Liang, S Wu
Mechanical systems and signal processing 100, 743-765, 2018
Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
S Haidong, J Hongkai, L Xingqiu, W Shuaipeng
Knowledge-Based Systems 140, 1-14, 2018
Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images
H Shao, M Xia, G Han, Y Zhang, J Wan
IEEE Transactions on Industrial Informatics 17 (5), 3488-3496, 2020
An enhancement deep feature fusion method for rotating machinery fault diagnosis
H Shao, H Jiang, F Wang, H Zhao
Knowledge-Based Systems 119, 200-220, 2017
Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet
H Shao, H Jiang, F Wang, Y Wang
ISA transactions 69, 187-201, 2017
Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder
H Zhiyi, S Haidong, J Lin, C Junsheng, Y Yu
Measurement 152, 107393, 2020
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
Z He, H Shao, X Zhong, X Zhao
Knowledge-Based Systems 207, 106396, 2020
Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning
M Xia, H Shao, D Williams, S Lu, L Shu, CW de Silva
Reliability Engineering & System Safety 215, 107938, 2021
An adaptive deep convolutional neural network for rolling bearing fault diagnosis
W Fuan, J Hongkai, S Haidong, D Wenjing, W Shuaipeng
Measurement Science and Technology 28 (9), 095005, 2017
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
Z He, H Shao, P Wang, JJ Lin, J Cheng, Y Yang
Knowledge-Based Systems 191, 105313, 2020
Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
S Haidong, C Junsheng, J Hongkai, Y Yu, W Zhantao
Knowledge-Based Systems 188, 105022, 2020
A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation
M Xia, H Shao, X Ma, CW de Silva
IEEE Transactions on Industrial Informatics 17 (10), 7050-7059, 2021
Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain
Y Xiao, H Shao, SY Han, Z Huo, J Wan
IEEE/ASME Transactions on Mechatronics 27 (6), 5254-5263, 2022
A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
H Shao, J Lin, L Zhang, D Galar, U Kumar
Information Fusion 74, 65-76, 2021
Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
H Cao, H Shao, X Zhong, Q Deng, X Yang, J Xuan
Journal of Manufacturing Systems 62, 186-198, 2022
Rolling bearing fault detection using continuous deep belief network with locally linear embedding
H Shao, H Jiang, X Li, T Liang
Computers in Industry 96, 27-39, 2018
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