Research on Fault Detection of Electrical Equipment Based on Infrared Image

Huang, Tongyuan; Lv, Xiangling; Yang, Yibo; Min, Cheng

doi:10.1109/icbaie52039.2021.9389938

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Traditional fault detection approaches for electrical equipment have a low accuracy, because infrared images contain interference points, and lack obvious edge features [14][15][16][17][18][19]. Huang et al [20] combined residual network (ResNet) with improved watershed algorithm to extract the abnormal areas and fault types of electrical equipment. To accurately segment overheated regions and narrow down the range of fault diagnosis, Fan et al [21] proposed a novel overheated area detection algorithm of electrical equipment, which adopts Otsu's method to remove the background, leaving the general areas of electrical equipment.…”

Section: Introductionmentioning

confidence: 99%

Thermal Fault Diagnosis of Electrical Equipment in Substations Based on Image Fusion

Lu¹,

Liu²,

Yuan³

2021

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Infrared thermal imaging can diagnose whether there are faults in electrical equipment during non-stop operation. However, the existing thermal fault diagnosis algorithms fail to consider an important fact: the infrared image of a single band cannot fully reflect the true temperature information of the target. As a result, these algorithms fail to achieve desired effects on target extraction from low-quality infrared images of electrical equipment. To solve the problem, this paper explores the thermal fault diagnosis of electrical equipment in substations based on image fusion. Specifically, a registration and fusion algorithm was proposed for infrared images of electrical equipment in substations; a segmentation and recognition model was established based on mask region-based convolutional neural network (R-CNN) for the said images; the steps of thermal fault diagnosis were detailed for electrical equipment in substations. The proposed model was proved effective through experiments.

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