Supervised Time Series Classification for Anomaly Detection in Subsea
  Engineering
Ergys Çokaj Halvor Snersrud Gustad Andrea Leone Per Thomas Moe Lasse Moldestad
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.