Akash, R., Sarathi, Ramanujam and Haddad, Manu  ORCID: https://orcid.org/0000-0003-4153-6146
2025.
Deep learning approach for non-invasive tracking detection in silicone rubber insulators using fluorescent fiber sensing.
IEEE Sensors Letters
10.1109/lsens.2025.3621294
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Abstract
This study presents a non-invasive approach for early detection of tracking phenomena in silicone rubber insulating materials during tracking studies, adopting IEC 60587 standards, using an optical fluorescent fiber sensor and a deep learning technique. The sensor captures light emissions during surface discharge, providing a high-resolution, noise-free signal without electrical contact. The signal is transformed into Gramian Angular Field (GAF) images representing temporal correlations to classify the discharge stages effectively. A novel restructuring strategy is employed to extract and reorganize diagonal sub-images from the GAF matrices, reducing the redundancy while emphasizing the key temporal features. These reorganized GAF inputs train a convolutional neural network (CNN) model to classify four distinct discharge stages. The model is evaluated with both single-channel and dual-channel GAF representations. Experimental results show that the proposed method achieves 98.22% classification accuracy, outperforming raw GAF inputs and demonstrating strong potential for real-time condition monitoring of high voltage insulation systems.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Additional Information: | License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 2475-1472 |
| Last Modified: | 27 Oct 2025 14:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181923 |
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