Sensor Fault Detection and Isolation via Networked Estimation: Full-Rank Dynamical Systems

TL;DR

This paper presents a networked estimation approach for detecting and isolating sensor faults in linear full-rank dynamical systems, utilizing consensus and measurement innovation without upper-bound noise assumptions.

Contribution

It introduces a novel fault detection and isolation method based on probabilistic thresholds for Gaussian noise and a graph-theoretic sensor replacement strategy for maintaining observability.

Findings

Effective fault detection with probabilistic thresholds

Sensor replacement improves networked observability

Comparison shows advantages over existing methods

Abstract

This paper considers the problem of simultaneous sensor fault detection, isolation, and networked estimation of linear full-rank dynamical systems. The proposed networked estimation is a variant of single time-scale protocol and is based on (i) consensus on \textit{a-priori} estimates and (ii) measurement innovation. The necessary connectivity condition on the sensor network and stabilizing block-diagonal gain matrix is derived based on our previous works. Considering additive faults in the presence of system and measurement noise, the estimation error at sensors is derived and proper residuals are defined for fault detection. Unlike many works in the literature, no simplifying upper-bound condition on the noise is considered and we assume Gaussian system/measurement noise. A probabilistic threshold is then defined for fault detection based on the estimation error covariance norm. Finally, a graph-theoretic sensor replacement scenario is proposed to recover possible loss of networked observability due to removing the faulty sensor. We examine the proposed fault detection and isolation scheme on an illustrative academic example to verify the results and make a comparison study with related literature.