Infrared Fault Diagnosis of Substation Equipment Based on Improved Efficient ViT and Area Relative Temperature Difference Method

Existing intelligent fault diagnosis techniques exhibit poor precision, low recall, slow inference speed in equipment identification and segmentation, and low diagnostic efficiency in fault diagnosis as well as cannot diagnose and analyze specific equipment categories. To overcome these drawbacks, in this study, we perform infrared fault diagnosis of substation equipment using the self-diagnosis method of segmentation before diagnosis. An efficient ViT network with faster reasoning speed is used to improve instance segmentation. The transformer cascade structure in the backbone is improved to increase the sensitivity of the network to weak targets, and the improved SK attention method is added to increase the network's interest in the substation equipment. The GD mechanism is used in the neck to improve the model's ability to use features of different sizes, and the optimized mask loss function is used in the head to improve the model's ability to diagnose infrared faults in substation equipment. The loss function improves the segmentation ability of the model at the boundary of the substation equipment, providing accurate segmentation regions for subsequent fault diagnosis. In terms of fault diagnosis, the relative temperature difference in the area is used for fault self-diagnosis in the segmented equipment region. The experimental results show that the performance of the improved network improves the detection and segmentation mAP values on the test set by 8.4% and 6.0%, respectively, although a slight decrease in the inference speed is observed. In addition, a fault diagnosis accuracy of up to 91.3% is obtained.