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In recent years, infrared imaging has become an important tool, particularly for predicting and preventing electrical equipment failure. Systems for online monitoring of the equipment conditions used in electrical substations are based on computer vision algorithms to perform visual analysis, automatically detect and assess equipment condition. This article describes a developed method that automatically finds defects in high-voltage insulators using infrared images. This method is based on the Otsu method, which is one of the most popular and effective segmentation methods that can be applied to finding defects in infrared images. The result is a comparative analysis of computer vision methods in infrared images used in our research. Automatic condition monitoring to find defects in high-voltage insulators in infrared images can be considered as the base method for an automated thermal imaging system for monitoring electrical substation equipment.

This paper describes a software algorithm for detecting defective insulating structures using infrared images. The defect detection criteria are based on a joint analysis of the mean and standard value of the brightness distribution of a set of insulators. The effectiveness of the developed criteria is substantiated by the results of laboratory tests of a significant number of insulators removed from high-voltage lines according to the results of thermal imaging diagnostics. Simultaneous analysis of thermograms of the same type of insulating structures according to the proposed algorithm is more effective in comparison with the subjective assessment of each of them separately, which was used earlier. In addition, this approach allows to reduce the time of analysis and decision-making based on the results of diagnostics.

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