Model-based Imitation Learning from Observation for input estimation in monitored systems

In the context of structural and industrial asset monitoring and twinning, the estimation of unknown inputs – typically reflecting the loads acting onto a system – stands as a critical factor in ensuring both the safety and performance of engineered systems. This research introduces a novel approach for inferring such unknown inputs from observed outputs, focusing particularly on dynamical systems, whether characterized by known or learned dynamics. Our proposed approach is situated within the framework of Imitation Learning from Observation (ILfO), which is here recast in the context of dynamical systems’ estimation. Our primary objective is to infer estimates of input signals on the basis of real-world measured outputs. The problem is formulated as a Partially Observable Markov Decision Process (POMDP), and we address it by utilizing established planning algorithms specifically tailored for ILfO applications. To address the POMDP, we harness the effectiveness and robustness of the cross-entropy method. We first verify the efficacy of the proposed approach on simulated case studies involving dynamical systems possessing well-defined dynamics. The proposed method is further validated on a practical scenario involving experimental data from a scaled wind turbine blade, leveraging dynamics learned through the Neural Extended Kalman Filter (Neural EKF) technique; an approach which leverages deep learning for inferring the dynamics of a system when this is imprecisely known. These examples demonstrate the flexibility of the proposed approach for use with systems featuring different degrees of prescribed dynamics. • A new perspective of input estimation, embedded within Imitation Learning from Observation methodology. • Recast the input estimation problem as a Partially Observable Markov Decision Process. • Integration of learning-based dynamics models.