Comparison of One-Dimensional and Two-Dimensional Reference Signal Representation for Insulation Aging State Recognition

This paper compares the performance of one-dimensional and two-dimensional convolutional neural networks in the task of analyzing a reference signal while determining the degradation level of single-core polymer-insulated cable. In this work was designed the set of reference signals and several forms of representing of these signals in the form of one-dimensional and two-dimensional tensors. Then, an experimental determination of the most effective version of the reference signal is carried out in terms of classification accuracy and the most effective form of representation of this signal was found, as well as most efficient type of neural network.