Supervised Time Series Classification for Anomaly Detection in Subsea Engineering

arXiv:2403.08013v1 Announce Type: new
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 …

abstract algorithms anomaly anomaly detection arxiv classification cs.lg data detection engineering importance machine machine learning math.ds monitoring series simulated data supervised machine learning systems time series type work