Real-Time Predictive Condition Monitoring Using Multivariate Data.

This article presents an algorithmic framework for real-time condition monitoring and state forecasting using multivariate data demonstrated on thermal imagery data of a ship's engine. The proposed method aims to improve the accuracy, efficiency, and robustness of condition monitoring and state predictions by identifying the most informative sampling locations of high-dimensional datasets and extracting the underlying dynamics of the system. The method is based on a combination of Proper Orthogonal Decomposition (POD), Optimal Sampling Location (OSL), and Dynamic Mode Decomposition (DMD), allowing the identification of key features in the system's behavior and predicting future states.