Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM

Tao Zhang; Yi Wang

doi:10.1117/12.3050446

24 October 2024 Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM

Tao Zhang, Yi Wang

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960P (2024) https://doi.org/10.1117/12.3050446
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

The working environment of wind turbine gearboxes is complex and variable, with strong noise, which makes traditional fault diagnosis methods inadequate for accurate fault identification. To address this issue, this paper proposes a fault diagnosis method based on Wavelet Packet Denoising combined with CNN-Swin Transformer-LSTM. Firstly, the original signal is decomposed, denoised, and reconstructed using wavelet packets to highlight the effective periodic impact components within the signal, and the reconstructed signal is converted into two-dimensional wavelet timefrequency images. Then, Convolutional Neural Networks (CNN) are used to extract basic feature information from the images. The feature maps are then input into a Swin Transformer model to automatically extract multi-scale feature information based on the self-attention mechanism. Following this, Long Short-Term Memory (LSTM) networks are employed to capture temporal features of the data. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to enhance feature representation capability. Finally, the method classifies different fault types. Experimental verification shows that the proposed method achieves an accuracy of 99.62% and 99.46% on two working condition datasets, respectively. Under conditions of strong noise and variable working conditions, the fault diagnosis accuracy reaches 92.24% and 96.16%. The experimental results demonstrate that this model possesses strong feature learning capabilities, robust anti-interference ability, and good generalization performance. Compared to other existing diagnosis techniques, it exhibits superior diagnostic performance and reliability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Zhang and Yi Wang "Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960P (24 October 2024); https://doi.org/10.1117/12.3050446

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Wavelets

Data modeling

Transformers

Denoising

Education and training

Wind turbine technology

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years