  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.
