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.

中文翻译：

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基于模型的观察模仿学习，用于监测系统中的输入估计


在结构和工业资产监测和孪生的背景下，未知输入的估计（通常反映作用在系统上的负载）是确保工程系统安全和性能的关键因素。这项研究引入了一种从观察到的输出中推断此类未知输入的新方法，特别关注动力系统，无论是以已知动力学还是学习动力学为特征。我们提出的方法位于从观察中模仿学习 （ILfO） 的框架内，这里在动力系统估计的背景下进行了重铸。我们的主要目标是根据实际测量的输出推断输入信号的估计值。该问题被表述为部分可观察马尔可夫决策过程 （POMDP），我们利用专为 ILfO 应用程序量身定制的既定规划算法来解决它。为了解决 POMDP，我们利用了交叉熵方法的有效性和稳健性。我们首先验证了所提出的方法在涉及具有明确定义动力学的动力学系统的模拟案例研究中的有效性。所提出的方法在涉及来自缩放风力涡轮机叶片的实验数据的实际场景中进一步验证，利用通过神经扩展卡尔曼滤波 （Neural EKF） 技术学习的动力学;一种利用深度学习来推断系统动态的方法，当系统动态不精确时。这些例子证明了所提出的方法在具有不同程度的指定动力学系统方面的灵活性。