Supervised Time Series Classification for Anomaly Detection in Subsea Engineering

Abstract

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.