Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Abstract

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.

Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

“…Large‐scale real‐time tests are necessary to investigate the dynamic performance of rate‐dependent devices or the influence of coupled vibration. Among existing experimental techniques, real‐time hybrid simulation (RTHS) integrates numerical modeling of analytical substructures and physical testing of experimental substructures, thus enabling large‐ or full‐scale tests in size limited structural labs 4–12 . Instead of traditional static actuators, dynamic actuators are utilized to apply desired displacements or forces in a real‐time manner to experimental substructures 13–17 …”

Hybrid Testing of a Cantilever Beam with Two Controlled Degrees of Freedom

“…Examples of accuracy measures are the synchronisation subspace plot (SSP) proposed in [7] to visualise the amplitude and phase mismatch between the NS and PS sides of the interface, the hybrid simulation error monitor (HSEM) defined in [11] as the energy introduced in the experiment by the control system, and the predictive performance indicator (PPI) introduced in [12], where adjustments to the interface location are also investigated to increase the fidelity of the response. The interested reader is referred to [13,14] for more detailed reviews of accuracy measures for RTHT.…”