Using Autoencoders to Classify EMC Problems in Electronic System Development

Abstract. This paper is a direct continuation of “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development” (Maalouly et al., 2022). The previous paper aimed to classify the electromagnetic compatibility (EMC) problem classification, while this paper addresses two primary issues: the data and the technique. The technique used in the previous study involved a principal component analysis (PCA) (Pearson, 1901) to generate input features for the neural network. However, since PCA only encodes linear relations from the samples, autoencoder (AE) models are now used to encode the data into a latent vector that better represents the data. The latent vectors will ultimately be used as input to classify the EMC problems. A neural network and a random forest classifier were utilized to develop a classification model, wherein the random forest demonstrated superior performance in comparison to the neural network.