Evidential Reasoning based Digital Twins for Performance Optimization of Complex Systems

Digital twins (DTs) are fast becoming an important technology in manufacturing companies for predicting failures of critical assets. However, such a digital twins is a hybrid representation with multiple parameters which need to be monitored to predict complex phenomena occurring in the asset in real time. This high-fidelity model of the twin makes the computation of the output extensive. Therefore, it is necessary to develop model reduction methods that simplify the high-fidelity model for faster computation with an acceptable degree of error. Such a method was proposed in previous studies to identify important nodes in graph-based DT representation. This article provides an improvement of previous method, considering the uncertainty in important node selection with Dempster-Shafer Theory (DST). The method is demonstrated with a grinding case study.