TY - JOUR
T1 - Wind turbine gearbox fault diagnosis using SAE-BP transfer neural network
AU - Wang, Yu
AU - Yang, Shuai
AU - Sánchez, RenéVinicio
N1 - Publisher Copyright:
© 2019 Totem Publisher, Inc. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The gearbox is a key component in wind turbines, and the fault diagnosis of gearboxes in wind turbines is a significant process of reliability management. Therefore, a SAE-BP transfer neural network is proposed in this paper for fault diagnosis of gearboxes in wind turbines. The proposed method is conducted by two processes. Firstly, a source task data is served as the training process to pretrain the SAE-BP neural network. The final learned network structure is the transferable weights or parameters that contain the feature information. Then, the learned weights are transferred into a target task with different working and fault conditions as the initial weight of a neural network model. To extract more fault-sensitive features, fast Fourier transform (FFT) is introduced to transform the raw data into a frequency domain. Several comparison experiments are conducted to validate the proposed method, and the results show that the proposed method achieves higher classification accuracy.
AB - The gearbox is a key component in wind turbines, and the fault diagnosis of gearboxes in wind turbines is a significant process of reliability management. Therefore, a SAE-BP transfer neural network is proposed in this paper for fault diagnosis of gearboxes in wind turbines. The proposed method is conducted by two processes. Firstly, a source task data is served as the training process to pretrain the SAE-BP neural network. The final learned network structure is the transferable weights or parameters that contain the feature information. Then, the learned weights are transferred into a target task with different working and fault conditions as the initial weight of a neural network model. To extract more fault-sensitive features, fast Fourier transform (FFT) is introduced to transform the raw data into a frequency domain. Several comparison experiments are conducted to validate the proposed method, and the results show that the proposed method achieves higher classification accuracy.
KW - BP algorithm
KW - Gearbox
KW - Intelligent fault diagnosis
KW - Sparse autoencoder
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85073692874&partnerID=8YFLogxK
U2 - 10.23940/ijpe.19.09.p24.25042514
DO - 10.23940/ijpe.19.09.p24.25042514
M3 - Article
AN - SCOPUS:85073692874
SN - 0973-1318
VL - 15
SP - 2504
EP - 2514
JO - International Journal of Performability Engineering
JF - International Journal of Performability Engineering
IS - 9
ER -