A gearbox fault diagnosis algorithm based on convolutional neural network and fusion coordinate attention mechanism

Hongtao Ni; Leilei Zhang; Chuanjin Wu

doi:10.1117/12.3025772

8 April 2024 A gearbox fault diagnosis algorithm based on convolutional neural network and fusion coordinate attention mechanism

Hongtao Ni, Leilei Zhang, Chuanjin Wu

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902P (2024) https://doi.org/10.1117/12.3025772
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

In response to the challenges of low accuracy, difficult feature extraction, and poor real-time performance in traditional gearbox fault diagnosis algorithms, this paper proposes a gearbox fault diagnosis algorithm based on convolutional neural network (CNN) and fused coordinate attention mechanism (FCAM) stay, a dataset of various gearbox fault images is collected and divided into training, validation, and test sets. Then, a lightweight MobileNetV2-based neural network model with the FCAM is constructed using Pytorch Framework. Subsequently, the training and validation data are input into the CNN with the fused attention mechanism for fault diagnosis model training, and the trained recognition model is saved. Finally, this model is used for the diagnosis and recognition of gearbox fault images in the test set. This algorithm is capable of automatically extracting gearbox fault features and classification, offering advantages in terms of reliability and real-time performance.

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

Citation Download Citation

Hongtao Ni, Leilei Zhang, and Chuanjin Wu "A gearbox fault diagnosis algorithm based on convolutional neural network and fusion coordinate attention mechanism", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902P (8 April 2024); https://doi.org/10.1117/12.3025772

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