Wind turbine gearbox fault diagnosis using SAE-BP transfer neural network

Yu Wang, Shuai Yang, RenéVinicio Sánchez

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)2504-2514
Number of pages11
JournalInternational Journal of Performability Engineering
Volume15
Issue number9
DOIs
StatePublished - 1 Jan 2019

Bibliographical note

Funding Information:
This work was funded by the Program of Chongqing Municipal Education Commission (No. KJZH17123), Research Start-Up Funds of Chongqing Technology and Business University (No. 1856018), Program of Chongqing Municipal Education Commission (No. KJQN201800830), MOST Science and Technology Partnership Program (No. KY201802006), and National Key Research & Development Program of China (No. 2016YFE0132200).

Publisher Copyright:
© 2019 Totem Publisher, Inc. All rights reserved.

Keywords

  • BP algorithm
  • Gearbox
  • Intelligent fault diagnosis
  • Sparse autoencoder
  • Transfer learning

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