Detection of bolt looseness in power tower based on Chirplet decomposition algorithm of laser vibration signal

Guanyu Wang; Yongyu Guan; Jie Yu

doi:10.1117/12.3051949

18 November 2024 Detection of bolt looseness in power tower based on Chirplet decomposition algorithm of laser vibration signal

Guanyu Wang, Yongyu Guan, Jie Yu

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033A (2024) https://doi.org/10.1117/12.3051949
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In response to the problem of difficult detection of loose bolts in transmission tower bolts, this paper proposes a method for detecting loose bolts in power tower bolts based on the Chirplet decomposition algorithm of laser vibration signals. This method obtains vibration signals of power towers through laser vibration measurement technology, and uses the Chirplet decomposition algorithm to finely process and analyze the vibration signals. Chirplet, as a basis function with adaptive time-frequency characteristics, can accurately capture nonlinear and non-stationary features in vibration signals, especially in details such as frequency changes and mode transfer caused by bolt loosening. By extracting and identifying the signal features after Chirplet decomposition, effective monitoring and diagnosis of the loosening status of power tower bolts can be achieved. The experimental results show that this method has high accuracy and reliability, providing strong technical support for the safe operation of power towers.

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

Citation Download Citation

Guanyu Wang, Yongyu Guan, and Jie Yu "Detection of bolt looseness in power tower based on Chirplet decomposition algorithm of laser vibration signal", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033A (18 November 2024); https://doi.org/10.1117/12.3051949

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