Data coverage assessment on neural network based digital twins for autonomous control system. (March 2023) - Wang, Longcong; Lin, Linyu; Dinh, Nam
Abstract: In a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds. Highlights: Assess the data coverage of NN-based DTs to determine if they are extrapolated. Test cases on FNNs and RNNs to demonstrate the adaptability of the framework. The framework can distinguish unseen data with proper UQ algorithms and thresholds.