Distributed Intermittent Fault Detection for Linear Stochastic Systems Over Sensor Network

In this article, the problem of intermittent fault (IF) detection is investigated for linear stochastic systems over sensor networks, where the appearing and disappearing times, and magnitude of IF are all nondeterministic. By utilizing the moving-horizon estimator, a novel residual generator is designed to realize the distributed detection of IFs in sensor networks. Different from the traditional moving horizon estimation algorithms, weight matrices of the quadratic cost function in this article are regulated by an unreliability index of the prior estimate to suppress the smearing effect of IFs. In virtue of the matrix transformation method and statistical theory, estimator parameters are obtained and the detectability of a single IF is analyzed by using the residual. In order to avoid the collisions of detection results from different residuals, the global detectability condition is given for all IFs. A cooperative decision-making strategy is proposed such that the only detection result can be guaranteed, which includes the appearing and disappearing times of IFs, and the nodes suffering from IFs. Finally, an illustrative example is provided to show the feasibility and effectiveness of the derived results.

Automated summary

This article investigates the problem of intermittent fault detection for linear stochastic systems over sensor networks. A novel residual generator is designed using the moving-horizon estimator to realize the distributed detection of IFs. Global detectability conditions and a cooperative decision-making strategy are proposed to ensure only one detection result and avoid collisions of detection results.