Supervised Time Series Classification for Anomaly Detection in Subsea Engineering

• URL: http://arxiv.org/abs/2403.08013v1
• Date: Tue, 12 Mar 2024 18:25:10 GMT
• Title: Supervised Time Series Classification for Anomaly Detection in Subsea Engineering
• Authors: Ergys \c{C}okaj, Halvor Snersrud Gustad, Andrea Leone, Per Thomas Moe, Lasse Moldestad
• Abstract summary: 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 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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