Fault Detection in Electrical Equipment by Infrared Thermography Images Using Spiking Neural Network Through Hybrid Feature Selection

Abstract

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.