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Detection and Quantitative Evaluation of Internal Bubble Defects in Basin Insulators Using Infrared Thermography

Internal bubble defects in basin insulators may cause power system failures, making accurate detection crucial. To address background noise interference, boundary blurring, and challenges in quantitative depth evaluation during detection, this study proposes a defect detection and quantitative evalu...

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Principais autores: Jihong Lian, Bing Cheng, Shiwei Tao, Yulu Zhang, Shuo Hou, Hao Yang
Formato: Artigo
Idioma:Inglês
Publicado em: IEEE 2026-01-01
coleção:IEEE Access
Assuntos:
Bubble defects
basin insulators
defect detection
infrared thermography
deep learning
Peak Second-order Derivative Time (PSDT)
Acesso em linha:https://ieeexplore.ieee.org/document/11339483/
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