{"ID":6497768,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-14T01:36:59.12045529Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09205","arxiv_id":"2607.09205","title":"Joint-Embedding Predictive Architecture for Solar PV Panel Fault Classification","abstract":"The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is impractical at scale. Thermal infrared (IR) imaging provides a non-contact solution for identifying PV faults; however, accurate classification remains challenging due to class imbalance, limited texture information, and subtle thermal differences. In this work, we investigate the applicability of Joint-Embedding Predictive Architecture (JEPA) for thermal IR PV fault classification across various scenarios and propose JEFFNet (JEPA-EFFicientNet), a multibranch architecture that combines JEPA-based self-supervised representation learning with EfficientNetV2-S-based supervised convolutional feature extraction. JEFFNet fuses semantic representations from a JEPA-pretrained Vision Transformer with convolutional features from EfficientNetV2-S, enabling complementary feature learning. JEFFNet is evaluated on two public thermal IR datasets, PVF-10 and InfraredSolarModules (ISM), for both multiclass and derived binary (healthy/faulty) classification. On PVF-10, JEFFNet achieves an F1-score of $93.21$ and an accuracy of $94.33$ in the 10-class task, and an F1-score of $97.53$ and an accuracy of $96.41$ in the derived 2-class task. On ISM, JEFFNet achieves an F1-score of $72.60$ and an accuracy of $83.88$ in the 12-class task, and an F1-score of $94.69$ and an accuracy of $94.78$ in the derived 2-class task. JEFFNet also uses only 108.6M parameters versus 205.91M for GEPFNet, a 47.2\\% reduction. These results demonstrate that combining self-supervised semantic and supervised convolutional features provides an effective, parameter-efficient solution for thermal IR PV fault classification. The source code is publicly available at https://github.com/Azimi2kht/JEFFNet","short_abstract":"The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is impractical at scale. Thermal infrared (IR) imaging provides a non-contact solution for identifying PV faults; however, accurate classification remains challenging due to...","url_abs":"https://arxiv.org/abs/2607.09205","url_pdf":"https://arxiv.org/pdf/2607.09205v1","authors":"[\"Seyyedhamid Azimidokht\",\"Mehdi Monemi\",\"Abdelhak Kharbouch\",\"Farid Hamzehaghdam\",\"Mehdi Rasti\",\"Jamshid Aghaei\",\"Emil Kurvinen\"]","published":"2026-07-10T08:50:02Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false,"code_links":[{"ID":614109,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-13T01:19:40.13847098Z","DeletedAt":null,"paper_id":6497768,"paper_url":"https://arxiv.org/abs/2607.09205","paper_title":"Joint-Embedding Predictive Architecture for Solar PV Panel Fault Classification","repo_url":"https://github.com/Azimi2kht/JEFFNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}