Research on pipeline signal noise reduction method based on improved INGO-VMD

Chun Liu; Zenghui Li

doi:10.1117/12.3035613

16 August 2024 Research on pipeline signal noise reduction method based on improved INGO-VMD

Chun Liu, Zenghui Li

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 1323002 (2024) https://doi.org/10.1117/12.3035613
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

Due to the complex working environment of the pipeline, the collected pipeline signals contain a lot of noise signals, which seriously affects the extraction of pipeline signals. A denoising method based on improved INGO-VMD joint energy entropy in this paper. First of all, The VMD parameters were optimized by the improved Northern Goshawk algorithm , then a variational mode decomposition is carried out on the noisy signal. Finally, the boundary line between noise and signal is determined according to energy entropy, and the signal is reconstructed to the denoised signal is obtained. The results can see the proposed method has the highest signal-to-noise ratio (SNR) of 24.22dB. The RMSE error is the lowest, 0.079. It is proved that the method not only can filter noise signal, but also has better noise reduction effect and stability.

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

Citation Download Citation

Chun Liu and Zenghui Li "Research on pipeline signal noise reduction method based on improved INGO-VMD", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 1323002 (16 August 2024); https://doi.org/10.1117/12.3035613

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