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Weihua Li
Weihua Li
Prof. with School of Mechanical & Automotive Engineering, South China University of Technology
Dirección de correo verificada de scut.edu.cn
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Año
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
8232017
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
4212022
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
3832019
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
3442020
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
2932019
Bearing performance degradation assessment using long short-term memory recurrent network
B Zhang, S Zhang, W Li
Computers in Industry 106, 14-29, 2019
2882019
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
F Yang, S Zhang, W Li, Q Miao
Energy 201, 117664, 2020
2742020
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
2472019
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
2022020
Deep decoupling convolutional neural network for intelligent compound fault diagnosis
R Huang, Y Liao, S Zhang, W Li
Ieee Access 7, 1848-1858, 2018
1742018
A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults
J Li, R Huang, G He, Y Liao, Z Wang, W Li
IEEE/ASME Transactions on Mechatronics 26 (3), 1591-1601, 2020
1422020
Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed
Y Liao, R Huang, J Li, Z Chen, W Li
IEEE Transactions on Instrumentation and Measurement 69 (10), 8064-8075, 2020
1392020
Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task
R Huang, J Li, Y Liao, J Chen, Z Wang, W Li
IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2020
1322020
A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery
W Li, Z Chen, G He
IEEE Transactions on Industrial Informatics 17 (3), 1753-1762, 2020
1312020
Semisupervised distance-preserving self-organizing map for machine-defect detection and classification
W Li, S Zhang, G He
IEEE Transactions on Instrumentation and Measurement 62 (5), 869-879, 2013
1102013
A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique
G He, K Ding, W Li, X Jiao
Renewable Energy 87, 364-375, 2016
1012016
A deep adversarial transfer learning network for machinery emerging fault detection
J Li, R Huang, G He, S Wang, G Li, W Li
IEEE Sensors Journal 20 (15), 8413-8422, 2020
1002020
Feature denoising and nearest–farthest distance preserving projection for machine fault diagnosis
W Li, S Zhang, S Rakheja
IEEE Transactions on Industrial Informatics 12 (1), 393-404, 2016
1002016
A robust weight-shared capsule network for intelligent machinery fault diagnosis
R Huang, J Li, S Wang, G Li, W Li
IEEE Transactions on Industrial Informatics 16 (10), 6466-6475, 2020
992020
A multi-source weighted deep transfer network for open-set fault diagnosis of rotary machinery
Z Chen, Y Liao, J Li, R Huang, L Xu, G Jin, W Li
IEEE Transactions on Cybernetics 53 (3), 1982-1993, 2022
842022
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