Resilience Quantification and its Support for Operational Resilience

TL;DR

This paper introduces a method to quantify and support system resilience using machine learning and active sampling, demonstrated on a manufacturing case study.

Contribution

It proposes an application-agnostic resilience metric and an efficient high-dimensional capacity approximation method combining classifiers and active sampling.

Findings

Resilience capacity can be quantified in degradation space.

Machine learning reduces the cost of high-dimensional capacity estimation.

The approach supports diagnosis, prognostics, and reconfiguration for resilience.

Abstract

We present a method to quantify a system's resilience capacity, i.e., the set of degradation magnitudes for which all functional requirements remain satisfied. These requirements come from human stakeholders (e.g., operators, planners) who define the acceptable performance envelope. By representing the resilience capacity in degradation space, we obtain an application -- agnostic resilience metric (e.g., capacity volume). To approximate the capacity efficiently in high-dimensional spaces, we pair machine-learning classifiers with entropy-based active sampling, reducing costly feasibility tests. The learned model then drives diagnosis (current health estimation) and prognostics (health-state forecasting) that estimates useful life. These two steps can be complemented by a reconfiguration step implemented by human operators to prolong the system's functionality. An illustrative case study, i.e., a manufacturing production line meeting weekly human set part demand, demonstrates the proposed workflow.