Intelligent Agent-based Metaheuristic for Predicting Vibration Response of a Catamaran Digital Twin

Abstract

Embedded structural health monitoring (SHM) systems are complex and costly. Mostly, the monitoring task can only be executed using a limited set of sensors due to the inaccessibility of measurement locations. On the other hand, intelligent agent-based metaheuristic approaches are an emergent tool in modelling complex systems. Therefore, this work proposes an integrating machine learning (ML) and agent-based metaheuristic framework for predicting (virtual sensing) vibration responses at unmeasured locations. This framework combines autonomous agents and a decision tree (ML model) to search for the optimal modal basis and vibration measurements to predict responses accurately at unmeasured locations. As a case study, virtual sensing of vibration responses on the main deck of a catamaran, using a sparse set of accelerometers, was performed. Those measurements were obtained during a sea trial. Statistical analysis was performed using the ANOVA test to evaluate the robustness and accuracy of the proposed methodology. In addition, three metrics in time and frequency domains were also used: Root Mean Square Error (RMSE), Time-Response Assurance Criterion (TRAC) and Frequency-Response Assurance Criterion (FRAC). Results showed significant accuracy in the prediction task. In summary, this framework is a viable solution for online structural health monitoring (SHM) applications without requiring a high computational cost.