Research on Intelligent Recognition Technology for Micro-Defects in Post Insulators Based on Ultrasonic Guided Waves

DOI

10.2991/978-94-6463-902-5_42How to use a DOI?

Keywords

Ultrasonic guided waves; Post insulators; Defect detection; Empirical Mode Decomposition (EMD); Wavelet denoising

Abstract

This study proposes an ultrasonic guided wave-based method for internal defect detection of post insulators. Signal quality is enhanced using Empirical Mode Decomposition (EMD) and wavelet denoising, while defect classification is achieved through a Convolutional Neural Network (CNN). Experiments confirm accurate recognition of cracks, porosity, delamination, and debonding, with strong robustness under low signal-to-noise ratio (SNR) conditions. The method provides a reliable foundation for developing intelligent monitoring systems for power equipment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jianpeng Jie
AU  - Xu Yan
PY  - 2025
DA  - 2025/12/16
TI  - Research on Intelligent Recognition Technology for Micro-Defects in Post Insulators Based on Ultrasonic Guided Waves
BT  - Proceedings of the 2025 7th International Conference on Civil Engineering, Environment Resources and Energy Materials (CCESEM 2025)
PB  - Atlantis Press
SP  - 424
EP  - 430
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-902-5_42
DO  - 10.2991/978-94-6463-902-5_42
ID  - Jie2025
ER  -