Supervised Time Series Classification for Anomaly Detection in Subsea Engineering

2024/03/12 by Ergys Çokaj, Çokaj, Ergys, Halvor Snersrud Gustad +7 · 1 citation
Computer Science · #68T07 #Anomaly Detection Techniques and Applications #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Primary: 62M10 #Secondary: 62P30 #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2403.08013

openalex publication_date 2024/03/12 · openalex created_date 2024/03/15 · openalex updated_date 2026/07/30

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.

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