Supervised Time Series Classification for Anomaly Detection in Subsea Engineering
This work addresses anomaly detection for subsea engineering monitoring, but it is incremental as it applies existing methods to simulated data without claiming major breakthroughs.
The paper tackled anomaly detection in subsea engineering by applying supervised machine learning classification to simulated time series data with intact and broken states, concluding that machine learning techniques offer advantages in decision-making based on performance metrics.
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.