Enhanced Fault Analysis for Electrical Equipment Using Optimized Support Vector Machines and Thermal Imaging

Considering the continuous improvement of the performance of electronic and electrical equipment and the overall system architecture and the increasing complexity of its functions, it is crucial to design a reliable and stable monitoring system. The rapid diagnosis and effective elimination of equipment failures play a decisive role in ensuring the stable operation and safe use of the equipment. Traditional electrical equipment fault diagnosis methods mostly rely on the feature extraction and model analysis, which has certain limitations. Therefore, this study proposes an electrical equipment fault analysis framework based on thermal imaging technology and optimized support vector machine. The study first used infrared thermal imaging technology to monitor the temperature changes on the surface of the equipment in real time. Specifically, the fault characteristics of the equipment were analyzed through thermal imaging images. At the same time, in order to reduce the noise in the thermal imaging data, the study used a noise removal optimization algorithm. This operation improved the clarity and accuracy of the image. Subsequently, this study used an improved support vector machine algorithm to identify insulation aging of electrical equipment. The algorithm analyzed data by combining information entropy and local density optimization. This operation enhanced the classification ability of SVM when processing complex data. The results show that the improved SVM has higher accuracy and stronger adaptability in identifying insulation aging of electrical equipment than the traditional SVM. The accuracy of the improved SVM model in electrical equipment fault identification has reached more than 90%, which is significantly better than the traditional method.