Characteristic frequency analysis of valve internal leakage detection based on fiber optic acoustic wave sensor

Yibin Wang; Changjia Wang; Qingguo Du; Kai Lin

doi:10.1117/12.3034287

5 July 2024 Characteristic frequency analysis of valve internal leakage detection based on fiber optic acoustic wave sensor

Yibin Wang, Changjia Wang, Qingguo Du, Kai Lin

Author Affiliations +

Proceedings Volume 13183, International Conference on Optoelectronic Information and Functional Materials (OIFM 2024); 131831E (2024) https://doi.org/10.1117/12.3034287
Event: The 3rd International Conference on Optoelectronic Information and Functional Materials (OIFM 2024), 2024, Wuhan, China

Abstract

To address the challenge of detecting the extremely weak acoustic signals caused by valve internal leakage, this study investigates a valve internal leakage detection method based on fiber optic acoustic sensors, utilizing characteristic frequencies for valve status determination. Based on shell theory, it is concluded that characteristic frequencies are related to pipeline material and radius. Simulation analysis of the characteristic frequencies of valve internal leakage acoustic signals is conducted, determining the frequency range of leakage acoustic signals. Subsequently, the size of the fiber optic acoustic sensor is optimized according to this frequency band and applied in experiments comparing characteristic frequencies of leakage acoustic signals for different pipeline materials. Results indicate that the higher the Young's modulus of the pipeline material, the higher the characteristic frequency of the valve internal leakage acoustic signal.

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

Citation Download Citation

Yibin Wang, Changjia Wang, Qingguo Du, and Kai Lin "Characteristic frequency analysis of valve internal leakage detection based on fiber optic acoustic wave sensor", Proc. SPIE 13183, International Conference on Optoelectronic Information and Functional Materials (OIFM 2024), 131831E (5 July 2024); https://doi.org/10.1117/12.3034287

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