Research on failure state identification method of wellhead device based on acoustic emission technology

Jinsong Li; Lei Lei; Honggang Zuo; Ting Ma

doi:10.1117/12.3055334

12 December 2024 Research on failure state identification method of wellhead device based on acoustic emission technology

Jinsong Li, Lei Lei, Honggang Zuo, Ting Ma

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 1343915 (2024) https://doi.org/10.1117/12.3055334
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

Aiming at the difficulty of identifying the failure state of wellhead structure, a method of monitoring and identifying the failure state of wellhead device is proposed. The minimum envelope entropy is used as the fitness function of GWO to optimize VMD parameters K and a. The retained IMF is selected according to the spectral characteristics, energy and correlation, and the interference of noise and irrelevant components is removed. The time domain, frequency domain and entropy eigenvalues were extracted from the signal after de-noising reconstruction, and the extracted eigenvalues were dimensionally reduced by PCA to obtain the main eigenvalues containing signal information. Finally, the signal was classified and recognized by support vector machine (SVM). The experimental results show that this method can identify the damage of wellhead equipment effectively.

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

Citation Download Citation

Jinsong Li, Lei Lei, Honggang Zuo, and Ting Ma "Research on failure state identification method of wellhead device based on acoustic emission technology", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 1343915 (12 December 2024); https://doi.org/10.1117/12.3055334

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