Time series classification is of significant importance in monitoring structural systems. In this work, we investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocessing of temporal data, using measures of statistical dispersion and dimension reduction techniques. We present an intuitive baseline method and discuss its efficiency. We conclude with a comparison of the various methods based on different performance metrics, showing the advantage of using machine learning techniques as a tool in decision making.
@article{arxiv.2403.08013,
title = {Supervised Time Series Classification for Anomaly Detection in Subsea Engineering},
author = {Ergys Çokaj and Halvor Snersrud Gustad and Andrea Leone and Per Thomas Moe and Lasse Moldestad},
journal= {arXiv preprint arXiv:2403.08013},
year = {2024}
}