Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

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dc.contributor.authorDamira Mussina
dc.contributor.authorAidana Irmanova
dc.contributor.authorPrashant K. Jamwal
dc.contributor.authorMehdi Bagheri
dc.date.accessioned2025-08-20T04:03:58Z
dc.date.available2025-08-20T04:03:58Z
dc.date.issued2020-01-01
dc.description.abstractThis research proposes a novel Fusion Convolutional Network (FCN) combining a CNN with a binary multilayer neural network (MNN) sub-classifier, forming a multi-modal information fusion system (MMIF) for real-time monitoring of high-voltage insulator surface condition via UAV-captured imagery and leakage current data. The fusion of image-based classification and leakage current readings allowed classification accuracy to increase from 92 % to 99.76 %. Traditional classifiers based on wavelet transform and PCA, as well as various CNN architectures, were benchmarked, and the hardware implementation potential on UAV edge devices was discussed., en
dc.identifier.citationMussina, D.; Irmanova, A.; Jamwal, P.K.; Bagheri, M. (2020). IEEE Access, 8, 184486–184496. https://doi.org/10.1109/ACCESS.2020.3027825en
dc.identifier.doi10.1109/ACCESS.2020.3027825
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3027825
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/9642
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofIEEE Accessen
dc.rightsOpen accessen
dc.sourceIEEE Access, 8, 184486–184496, (2020)en
dc.subjectUAV inspectionen
dc.subjectdeep learningen
dc.subjectinsulator conditionen
dc.subjectdata fusionen
dc.titleMulti-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulatoren
dc.typeJournal Articleen

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