Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.

实时混合子结构（RTHS）测试是研究部件对机械系统动力影响的常用方法。在RTHS中，对动力系统的一部分进行了实验测试，而其余结构则在协同仿真中进行了数值模拟。RTHS循环中存在多个错误源，这些错误源会使测试结果失真。为了研究测试的可靠性，必须对测试的保真度进行量化。但是，在许多工程应用中，没有可用于验证测试结果的参考解决方案。这项工作审查了RTHS中使用的当前现有的准确性度量。此外，提出了在没有参考解决方案时使用人工神经网络（ANN）预测RTHS测试结果的保真度的建议。讨论了网络的适当输入功能，例如系统的动态属性和现有的错误指示器。对来自接触系统的动态系统的虚拟RTHS（vRTHS）模拟的数据集进行了人工神经网络训练。训练过程成功，这意味着ANN预测与真实保真度值之间的相关性> 99％。然后，将该网络应用于同一动力系统的RTHS实验数据，并达到98 ％的相关性，这证明ANN所发现的关系捕获了所选输入特征与误差度量之间的关系。将经过训练的人工神经网络应用于来自线性vRTHS测试的数据表明，需要进一步改善网络并选择输入功能。这项工作表明，在缺乏参考解决方案的情况下，人工神经网络可能是预测RTHS测试结果保真度的有意义的工具，尤其是如果汇总了来自不同RTHS测试的更多数据来训练它们时。