Deep Learning-Based Classification of Distorted Current Waveforms for Electrical Fault Detection

Abstract

Electrical current signals provide essential information about the health and performance of electrical systems. Identifying distorted signals is critical for the early detection of faults in electrical systems, and in turn, helps prevent damage, instability, and loss of efficiency. This paper presents a study on classifying healthy and faulty sine-wave signals using convolutional neural networks. A dataset of 200 images was constructed, to provide diverse waveform variations, and used to train and test a customized Convolutional Neural Networks (CNN) in addition to three pretrained CNNs: SqueezeNet, GoogLeNet, and ResNet-50. Each pretrained network was fine-tuned through transfer learning, and data augmentation was applied to improve generalization. Experimental results show that ResNet-50 achieved the highest validation accuracy of 98.33%, while SqueezeNet and GoogLeNet reached 96.67%. Testing on unseen current signal images confirmed that deeper models were more effective in detecting small waveform distortions. The results demonstrate the suitability of CNN-based approaches for waveform classification and highlight the importance of model depth and dataset variation. This study contributes to the field of predictive maintenance by providing an exploration of a simple, cost-effective, and accurate method for fault detection in single phase induction motors. It opens the door for further research into machine learning applications in fault diagnosis in other types of motors and electrical systems.