Health level verification for electric control valve based on BiLSTM with multihead attention mechanism

Wenqiang Duan; Jingyu Zhang; Bing Yang; Baohong Wei; Ying Li

doi:10.1117/12.3050898

27 September 2024 Health level verification for electric control valve based on BiLSTM with multihead attention mechanism

Wenqiang Duan, Jingyu Zhang, Bing Yang, Baohong Wei, Ying Li

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810Y (2024) https://doi.org/10.1117/12.3050898
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

The failure of electric valves represents a significant safety hazard in industrial systems. Traditional manual detection and regular replacement strategies are insufficient to address this issue. This study proposes an online verification method combining convolutional neural networks (CNN), two-way long short-term memory networks (BiLSTM) and multi-head attention mechanism (MHA), which is capable of identifying weak fault characteristics such as plugging and twitching caused by regulator wear. Transfer learning addresses the issue of data scarcity and enhances the model's adaptability in the target domain. The CNN-BiLSTM-MHA-TL model exhibited high prediction accuracy, with MAE, RMSE, and MAPE values of 0.4451, 0.5722, and 0.0132, respectively. This online verification method offers notable advantages and promising prospects for application in the field of valve health monitoring.

Citation Download Citation

Wenqiang Duan, Jingyu Zhang, Bing Yang, Baohong Wei, and Ying Li "Health level verification for electric control valve based on BiLSTM with multihead attention mechanism", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810Y (27 September 2024); https://doi.org/10.1117/12.3050898

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