A Comparative Study of MLP Networks Using Backpropagation Algorithms in Electrical Equipment Thermography

Nowadays, infrared thermographic inspection has increasingly been utilized for fault diagnosis tools of electrical equipment, because it maintains non-interrupting operation of power system and ensures early diagnosis of faults. The article discusses the performance comparison of multilayer perceptron (MLP) networks using various backpropagation algorithms for thermal imaging-based condition monitoring of electrical hotspots. The training algorithms are Levenberg–Marquardt, Broyden–Fletcher–Goldfarb–Shanno quasi-Newton, resilient backpropagation, gradient descent with momentum and adaptive learning rate and standard gradient descent training algorithms. The performance of the training algorithms are evaluated in terms of percentage of accuracy, sensitivity, specificity, false-positive and false-negative results. In the beginning, thermal images of electrical equipment are captured using infrared camera. Six statistical intensity features, namely mean, maximum, minimum, median, standard deviation and variance extracted from each hotspot of equipment thermal image are used as the inputs of multilayered perceptron networks to classify the thermal conditions of hotspots into two classes, namely normal and defective. The comparison of MLP networks using training algorithms proves that the MLP network trained with resilient backpropagation algorithm gives the best performance in thermal condition recognition.