Jinxuan Wen, Jiangjiang LiThis email address is being protected from spambots. You need JavaScript enabled to view it., and Yachao Zhang
School of Electronic and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China
Received: May 2, 2025 Accepted: June 22, 2025 Publication Date: June 28, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In modern industry and power systems, electrical equipment serves as the backbone of numerous operations and infrastructure. Temperature rise is one of the primary indicators of emerging faults in electrical equipment. However, traditional temperature measurement methods face significant challenges. To overcome these limitations, this paper proposes a deep multi-modal image fusion method. The method integrates data from multiple imaging modalities and uses deep learning algorithms to fuse and analyze this information. By applying Bayes theorem and introducing a variational distribution, the method approximates the true posterior. A weight vector is used to aggregate the complementary and consistent information of hidden variables from various modalities, allowing the model to emphasize more informative modalities while incorporating information from others. Experimental results on two datasets demonstrate the effectiveness of our method in comparison with methods, showing the best detection results about accuracy, precision, recall, and F1 score.
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