Design of online detection algorithm for bolt fastening state in substation based on deep learning

Zhigang Guo; Zhichao Li; Wei Yang; Peiyong Yu; Xiaolong Zhao

doi:10.1117/12.3038636

23 August 2024 Design of online detection algorithm for bolt fastening state in substation based on deep learning

Zhigang Guo, Zhichao Li, Wei Yang, Peiyong Yu, Xiaolong Zhao

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502D (2024) https://doi.org/10.1117/12.3038636
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

As the core component of the power system, the normal operation of the substation is very important to the reliability of the power supply. As one of the key elements to connect substation equipment, the maintenance of the bolt fastening state has an important impact on the safety and stability of equipment. The purpose of this study is to propose an algorithm based on deep learning, which is used to detect the fastening state of substation bolts in real time to ensure the normal operation and maintenance of equipment. In this study, we collected a large number of substation bolt state data and designed a deep learning model. Firstly, we preprocessed the data with Gramian Angular Field (GAF) and Piecewise Aggregate Approximation (PAA). Then, we put forward a diagnosis model of the acoustic emission signal of substation bolts based on two-dimensional time series and Attention Mechanism (AM), and then use this model to capture the time series information of bolt state. The experimental results show that after 100 iterations, the diagnostic accuracy of the model has reached the highest, with an average accuracy of about 98%. Our deep learning algorithm can highly accurately identify the fastening state of bolts, which provides an effective guarantee for the safety and reliability of substations, and also has the ability to monitor in real-time.

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

Citation Download Citation

Zhigang Guo, Zhichao Li, Wei Yang, Peiyong Yu, and Xiaolong Zhao "Design of online detection algorithm for bolt fastening state in substation based on deep learning", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502D (23 August 2024); https://doi.org/10.1117/12.3038636

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Deep learning

Data modeling

Acoustic emission

Detection and tracking algorithms

Education and training

Windows

Safety

Show All Keywords

Keywords/Phrases

Search In:

Publication Years