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Faults in electrical facilities may cause severe damages, such as the electrocution of maintenance personnel, which could be fatal, or a power outage. To detect electrical faults safely, electricians disconnect the power or use heavy equipment during the procedure, thereby interrupting the power supply and wasting time and money. Therefore, detecting faults with remote approaches has become important in the sustainable maintenance of electrical facilities. With technological advances, methodologies for machine diagnostics have evolved from manual procedures to vibration-based signal analysis. Although vibration-based prognostics have shown fine results, various limitations remain, such as the necessity of direct contact, inability to detect heat deterioration, contamination with noise signals, and high computation costs. For sustainable and reliable operation, an infrared thermal (IRT) image detection method is proposed in this work. The IRT image technique is used in various engineering fields for diagnosis because of its non-contact, safe, and highly reliable heat detection technology. To explore the possibility of using the IRT image-based fault detection approach, object detection algorithms (Faster R-CNN; Faster Region-based Convolutional Neural Network, YOLOv3; You Only Look Once version 3) are trained using 16,843 IRT images from power distribution facilities. A thermal camera expert from Korea Hydro & Nuclear Power Corporation (KHNP) takes pictures of the facilities regarding various conditions, such as the background of the image, surface status of the objects, and weather conditions. The detected objects are diagnosed through a thermal intensity area analysis (TIAA). The faster R-CNN approach shows better accuracy, with a 63.9% mean average precision (mAP) compared with a 49.4% mAP for YOLOv3. Hence, in this study, the Faster R-CNN model is selected for remote fault detection in electrical facilities.

SUMMARYThough machine vision systems for automatically detecting visual defects, called mura, have been developed for thin fiat transistor liquid crystal display (TFT-LCD) panels, they have not yet reached a level of reliability which can replace human inspectors. To establish an objective criterion for identifying real defects, some index functions for quantifying defect levels based on human perception have been recently researched. However, while these functions have been verified in the laboratory, further consideration is needed in order to apply them to real systems in the fi eld. To begin with, we should correct the distortion occurring through the capturing of panels. Distortion can cause the defect level in the observed image to differ from that in the panel. There are several known methods to restore the observed image in general vision systems. However, TFT-LCD panel images have a unique background degradation composed of background non-uniformity and vignetting effect which cannot easily be restored through traditional methods. Therefore, in this paper we present a new method to correct background degradation of TFT-LCD panel images using principal component analysis (PCA). Experimental results show that our method properly restores the given observed images and the transformed shape of muras closely approaches the original undistorted shape.

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