A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance

TL;DR

This paper introduces a scalable, probabilistic fault detection and isolation method for high-dimensional nonlinear systems, leveraging randomized optimization and statistical disturbance information to improve robustness and early intrusion detection.

Contribution

It proposes a novel, relaxed design approach for FDI filters that is applicable to high-dimensional nonlinear systems, with theoretical performance guarantees based on randomized optimization.

Findings

Effective detection of cyber-physical attacks in power systems

Robust FDI filter design with probabilistic performance guarantees

Scalable methodology applicable to high-dimensional nonlinear systems

Abstract

This article presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, shifting attention from the system dynamics to the disturbance inputs, we propose a relaxed design perspective to train a linear residual generator given some statistical information about the disturbance patterns. That is, we propose an optimization-based approach to robustify the filter with respect to finitely many signatures of the nonlinearity. We then invoke recent results in randomized optimization to provide theoretical guarantees for the performance of the proposed filer. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to detect such an intrusion before the functionality of the power system is disrupted.