Abstract
Fault management of systems is a key component in mission/operation success of each system or technology. Fault management can be implemented into various different applications, power generation, industrial processing, aviation and transportation, and electrical grids with combinations of renewable energy sources. As the complexity of the overall system design increases, reliance on just pure physics-based or pure data-based modeling is shown to be deficient in the accuracy of fault management. This work shows the potential of a combination of digital twin and a fault management algorithm. The algorithm is designed to be robust, accurate, reliable, and fast; it is based on both, physics and data-based model modeling. The algorithm compares physical and data-based approaches to provide the most reliable fault management solution, through a digital twin. The fault management algorithm is designed to use physics-based model validated on real/synthetic data (data-based model).
Exploring autonomic options in an unified fault management architecture through reflex reactions via pulse monitoring