Deep multi-modal imaging temperature measurement method for detecting temperature rise in electrical equipment faults - Jinxuan Wen; Jiangjiang Li; Yachao Zhang (Journal of Applied Science and Engineering, 2025)
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.