A Review of Defect Detection on Electrical Components Using Image Processing Technology

Asiegbu, Geoffrey O.; Haidar, Ahmed M. A.; Hawari, Kamarul

doi:10.1007/978-81-322-0997-3_23

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…It is hard to compare the proposed method with these methods due to the difference in the data capturing process. However, in these and other methods , thresholding techniques including global thresholding and local thresholding were usually used to extract the defect candidates, then based on the size of candidate area the candidate was classified into defects or normal surface. In this article, we, therefore, used the well‐known thresholding technique as a conventional method to compare it with our method for evaluating the performance of the proposed method.…”

Section: Resultsmentioning

confidence: 99%

Automatic visual inspection method for a coated surface having an orange peel effect

Yimit

Itou

Matsui

et al. 2018

IEEJ Transactions Elec Engng

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In many production processes, defect detection has played an important role in preventing the outflow of defective products. As such, there are still many production lines that rely on an inspector's visual observation. In this study, using fluorescent tubes as an irradiation light source, we propose a simple and effective visual inspection method for detecting convex defect on painted surfaces having an orange peel effect. The proposed method detects and distinguishes painted surface defects in an area illuminated by fluorescent tubes, using the combination of local segmented results and gradient information computed with different differential operations. The inspection device for detecting the defect can be easily installed in the production line without changing the production line, and can be constructed at a low cost without using additional adaptations. The performance of the proposed method was evaluated with the data of real products captured with an inspection device installed on the production line. Experimental results show that this method can provide a better detection in the inspection region with a low false‐detection rate. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Resultsmentioning

confidence: 99%

Automatic visual inspection method for a coated surface having an orange peel effect

Yimit

Itou

Matsui

et al. 2018

IEEJ Transactions Elec Engng

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“…Image processing technology has been extensively adopted in abnormal defect detection and diagnosis of electrical equipment because of the high accuracy and rich processing content [1,2]. Of varied methods, X-ray digital imaging technology can observe the internal structure abnormality of electrical equipment under live operation conditions by non-contact and nondestructive testing [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Detection and Diagnosis of Defect in GIS Based on X-ray Digital Imaging Technology

Pang

Jia

et al. 2020

Energies

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For better application of X-ray digital imaging technology in defect detection in Gas Insulated Switchgear (GIS), it is essential to investigate the typical defect and establish the defect database, which has not been adequately performed in previous work. Systematic experimental research is also needed to accumulate data and experience. In this research, an experimental platform, including Computed Radiography (CR) imaging system and a GIS model, is built, and extensive tests of different kinds of typical defects are studied. The influence X-ray irradiation on SF6 under different tube voltage levels is firstly examined, which proves that the withstand voltage of SF6 gas has not been affected and no dissociation has been found. Then, several kinds of defects are tested by X-ray digital imaging technology. The successful application examples of “visual” detection of defects further prove the practicability and validity of the X-ray digital imaging technique. Finally, the image database of typical defects inside of GIS is established and the defect risk is also analyzed in three levels, which would be useful for the defect severity diagnosis and risk assessment.

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“…Several methods have been employed to detect and analyze these anomalies. Geoffrey et al presented the review of some of these methods to identify faults and classify their severity in power equipment using different image analysis approaches [1]. Also, Shawal et al reviewed different methods for classifying the level of faults in electrical equipment [2].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data

Jalil

Leone

Martinelli

et al. 2019

Sensors

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The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

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A Review of Defect Detection on Electrical Components Using Image Processing Technology

Cited by 5 publications

References 14 publications

Automatic visual inspection method for a coated surface having an orange peel effect

Automatic visual inspection method for a coated surface having an orange peel effect

Detection and Diagnosis of Defect in GIS Based on X-ray Digital Imaging Technology

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data

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