Abstract
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.
Original language | English |
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Pages (from-to) | 2504-2514 |
Number of pages | 11 |
Journal | International Journal of Performability Engineering |
Volume | 15 |
Issue number | 9 |
DOIs | |
State | Published - 1 Jan 2019 |
Bibliographical note
Publisher Copyright:© 2019 Totem Publisher, Inc. All rights reserved.
Keywords
- BP algorithm
- Gearbox
- Intelligent fault diagnosis
- Sparse autoencoder
- Transfer learning