Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27730/25:10259168
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Spectrogram-Based Fault Detection in Covered Conductors Using ResNet50V2 with SHAP and Grad-CAM Analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
7
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Istočno Sarajevo
Event date
May 19, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001480997700093