Open Access iconOpen Access

ARTICLE

crossmark

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

Xiaolei Li*

Beijing Goldwind Science & Goldwind Huineng Technology Co., Ltd., Beijing, 100176, China

* Corresponding Author: Xiaolei Li. Email: email

(This article belongs to this Special Issue: Advancements in Renewable Energy Systems with AI, Big Data, BlockChain, IoT, and Machine Learning Applications)

Energy Engineering 2022, 119(2), 665-680. https://doi.org/10.32604/ee.2022.019292

Abstract

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. To further improve the anti-noise ability of the model, the first layer of the model was set as the Drop-block layer. Finally, the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results. The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

Keywords


Cite This Article

Li, X. (2022). Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network. Energy Engineering, 119(2), 665–680.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1544

    View

  • 914

    Download

  • 0

    Like

Share Link