Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259168" target="_blank" >RIV/61989100:27240/25:10259168 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10259168

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10959257/" target="_blank" >https://ieeexplore.ieee.org/document/10959257/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INFOTEH64129.2025.10959257" target="_blank" >10.1109/INFOTEH64129.2025.10959257</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis

Popis výsledku v původním jazyce

Partial discharges (PDs) in XLPE-covered conductors are critical precursors to insulation failure in mediumvoltage networks. Despite advancements in radiometric PD detection using deep learning, the classification of multiple fault types remains underexplored, and model interpretability challenges hinder practical deployment. This study presents a spectrogram-based deep learning framework for classifying 12 PD fault types and background conditions using data from a BONI-WHIP antenna. By converting time-domain signals into spectrograms and employing a ResNet50V2 classifier, the framework achieves high multi-class classification accuracy. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive Explanations (SHAP) identify the spectral features influencing predictions, aligning with known PD phenomena such as high-frequency emissions during discharges. The results demonstrate the potential for explainable AI in condition monitoring, with further validation under field conditions recommended to confirm its applicability.

Název v anglickém jazyce

Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis

Popis výsledku anglicky

Partial discharges (PDs) in XLPE-covered conductors are critical precursors to insulation failure in mediumvoltage networks. Despite advancements in radiometric PD detection using deep learning, the classification of multiple fault types remains underexplored, and model interpretability challenges hinder practical deployment. This study presents a spectrogram-based deep learning framework for classifying 12 PD fault types and background conditions using data from a BONI-WHIP antenna. By converting time-domain signals into spectrograms and employing a ResNet50V2 classifier, the framework achieves high multi-class classification accuracy. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive Explanations (SHAP) identify the spectral features influencing predictions, aligning with known PD phenomena such as high-frequency emissions during discharges. The results demonstrate the potential for explainable AI in condition monitoring, with further validation under field conditions recommended to confirm its applicability.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 24th International Symposium INFOTEH-JAHORINA, INFOTEH : proceedings : March 19-21, 2025, Jahorina, East Sarajevo, Republic of Srpska, Bosnia and Herzegovina

ISBN

979-8-3315-1580-5

ISSN

2767-9454

e-ISSN

2767-9470

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Istočno Sarajevo

Datum konání akce

19. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001480997700093