Underwater propeller fault diagnosis based on deep learning

Yonghe Wei; Ze Liu

doi:10.1117/12.3025698

28 February 2024 Underwater propeller fault diagnosis based on deep learning

Yonghe Wei, Ze Liu

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130713F (2024) https://doi.org/10.1117/12.3025698
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

When an underwater robot performs a task, the propeller is most likely to malfunction, such as being entangled by foreign objects or the blades are damaged. At present, its fault diagnosis methods have problems such as relying on manual feature extraction and using neural networks with low accuracy. Therefore, this paper proposes an integration based on an improved one-dimensional convolutional neural network (1D-CNN) and a long short-term memory network (LSTM). Thruster fault diagnosis method. By analyzing thruster data, accurate diagnosis of four different thruster faults can be achieved. A comparative experiment was conducted between the proposed model and some traditional algorithm models. The results show that the proposed method has greatly improved the test accuracy, and this method can effectively diagnose underwater robot thruster faults.

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

Citation Download Citation

Yonghe Wei and Ze Liu "Underwater propeller fault diagnosis based on deep learning", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130713F (28 February 2024); https://doi.org/10.1117/12.3025698

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