Abstract
The thermography camera is widely used to inspect electrical equipment. The existing defect assessment indicators are mainly the hot spot temperature and the relative temperature difference (RTD), and the analyzing process used to calculate the indicators is usually off-site. A smart thermography camera is designed and its application in the diagnosis of electrical equipment is investigated in this article. For the camera, the regional RTD is calculated automatically based on equipment detection and image registration algorithms, and the defects can be judged according to the existing criteria. Firstly, the regional RTD and its implementation method are proposed to reduce the error when the hot spot is unmeasurable. Then the object detection method based on the convolutional neural network (CNN) is modified dedicated to infrared (IR) images. Over 12 000 historical images and 18 000 labels are used for training and tests. The modified model can identify 16 classes of substation equipment with 86.4% mAP. With low latency, the inference speed of this on-site smart camera reaches 30 frames/s. The test results show that the similarity of diagnosis result between our method and the criteria is 92.2%.
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