Fault Cause Estimation Using Simplified Digital Twin Based on 1D-CAE

Abstract

Digital Twin is a technology that creates simulation models of machines or processes. When combined with sensing data, it enables real-time monitoring and analysis of the target system. However, challenges remain in creating high-precision models and in the associated costs. In this study, we propose a simplified Digital Twin based on 1D-CAE models generated through Model Based Development (MBD), for anomaly cause estimation. This approach does not produce a complete model of the target but rather a partial one. The model contains errors due to unmodeled factors and noise. Nevertheless, this method can estimate the causes of faults.