System Resilience through Health Monitoring and Reconfiguration

TL;DR

This paper presents an integrated framework combining digital twins, fault diagnosis, prognostics, and reconfiguration to enhance the resilience of complex systems against unforeseen events, demonstrated on a fuel system model.

Contribution

The paper introduces a comprehensive, real-time resilience framework utilizing physics-based digital twins and modular diagnosis, prognostics, and reconfiguration components, advancing system resilience strategies.

Findings

Resilience metric improved using the framework

Fault diagnosis achieved real-time performance

System operation impact minimized through reconfiguration

Abstract

We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based digital twin. The prognostics module tracks the fault progressions and trains the online degradation models to compute remaining useful life of system components. In addition, we use the degradation models to assess the impact of the fault progression on the operational requirements. The reconfiguration module uses PDDL-based planning endowed with semantic attachments to adjust the system controls so that the fault impact on the system operation is minimized. We define a resilience metric and use the example of a fuel system model to demonstrate how the metric improves with our framework.