Sparse Error Localization in Complex Dynamic Networks

TL;DR

This paper develops a mathematical framework for localizing sparse errors in large dynamic networks using convex optimization, enabling accurate fault detection and measurement strategies even with limited data.

Contribution

It introduces a novel theory for error localization in complex networks, leveraging sparsity and convex control to improve fault detection and measurement selection.

Findings

The method accurately localizes errors with limited measurements.

Strategies are proposed to refine error location when measurements are insufficient.

The approach is applicable to various complex dynamic systems.

Abstract

Understanding the dynamics of complex systems is a central task in many different areas ranging form biology via epidemics to economics and engineering. Unexpected behaviour of dynamic systems or even systems failure is sometimes difficult to comprehend. Such unexpected dynamics can be caused by systematic model errors, unknown inputs from the environment and systems faults. Localizing the root cause of these errors or faults and reconstructing their dynamics is only possible if the measured outputs of the system are sufficiently informative. Here, we present a mathematical theory for the measurements required to localize the position of error sources in large dynamic networks. We assume, that faults or errors occur at a limited number of positions in the network. This sparsity assumption facilitates the accurate reconstruction of the dynamic timecourses of the errors by solving a convex optimal control problem. For cases, where the sensor measurements are not sufficiently informative to pinpoint the error position exactly, we provide methods to restrict the error location to a smaller subset of network nodes. We also suggest strategies to efficiently select additional measurements for narrowing down the error location.