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Yaguo Lei / 雷亚国
Yaguo Lei / 雷亚国
Professor of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University
Dirección de correo verificada de mail.xjtu.edu.cn - Página principal
Título
Citado por
Citado por
Año
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Y Lei, J Lin, Z He, MJ Zuo
Mechanical systems and signal processing 35 (1-2), 108-126, 2013
14722013
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
F Jia, Y Lei, J Lin, X Zhou, N Lu
Mechanical systems and signal processing 72, 303-315, 2016
13372016
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Y Lei, N Li, L Guo, N Li, T Yan, J Lin
Mechanical systems and signal processing 104, 799-834, 2018
11062018
An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data
Y Lei, F Jia, J Lin, S Xing, SX Ding
IEEE Transactions on Industrial Electronics 63 (5), 3137-3147, 2016
8512016
Applications of machine learning to machine fault diagnosis: A review and roadmap
Y Lei, B Yang, X Jiang, F Jia, N Li, AK Nandi
Mechanical Systems and Signal Processing 138, 106587, 2020
7332020
A recurrent neural network based health indicator for remaining useful life prediction of bearings
L Guo, N Li, F Jia, Y Lei, J Lin
Neurocomputing 240, 98-109, 2017
7112017
Condition monitoring and fault diagnosis of planetary gearboxes: A review
Y Lei, J Lin, MJ Zuo, Z He
Measurement 48, 292-305, 2014
5742014
Application of the EEMD method to rotor fault diagnosis of rotating machinery
Y Lei, Z He, Y Zi
Mechanical Systems and Signal Processing 23 (4), 1327-1338, 2009
5642009
Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data
L Guo, Y Lei, S Xing, T Yan, N Li
IEEE Transactions on Industrial Electronics 66 (9), 7316-7325, 2018
5532018
A new approach to intelligent fault diagnosis of rotating machinery
Y Lei, Z He, Y Zi
Expert Systems with applications 35 (4), 1593-1600, 2008
4472008
Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs
Y Lei, Z He, Y Zi, Q Hu
Mechanical systems and signal processing 21 (5), 2280-2294, 2007
4472007
A hybrid prognostics approach for estimating remaining useful life of rolling element bearings
B Wang, Y Lei, N Li, N Li
IEEE Transactions on Reliability 69 (1), 401-412, 2018
4462018
Application of an improved kurtogram method for fault diagnosis of rolling element bearings
Y Lei, J Lin, Z He, Y Zi
Mechanical systems and signal processing 25 (5), 1738-1749, 2011
4192011
An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
B Yang, Y Lei, F Jia, S Xing
Mechanical Systems and Signal Processing 122, 692-706, 2019
3952019
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
F Jia, Y Lei, L Guo, J Lin, S Xing
Neurocomputing 272, 619-628, 2018
3562018
An improved exponential model for predicting remaining useful life of rolling element bearings
N Li, Y Lei, J Lin, SX Ding
IEEE Transactions on Industrial Electronics 62 (12), 7762-7773, 2015
3532015
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
F Jia, Y Lei, N Lu, S Xing
Mechanical Systems and Signal Processing 110, 349-367, 2018
3442018
A model-based method for remaining useful life prediction of machinery
Y Lei, N Li, S Gontarz, J Lin, S Radkowski, J Dybala
IEEE Transactions on reliability 65 (3), 1314-1326, 2016
3142016
Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods
Z Zhang, X Si, C Hu, Y Lei
European Journal of Operational Research 271 (3), 775-796, 2018
3072018
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Y Lei, Z He, Y Zi
Expert Systems with Applications 38 (6), 7334-7341, 2011
3052011
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20