Multiple sensor fault detection and isolation for large-scale interconnected nonlinear systems

Abstract

This paper presents the design of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. For each of the interconnected subsystems, we design a local sensor fault diagnosis (LSFD) agent responsible for multiple sensor fault detection and isolation in the local sensor set. The multiple sensor fault detection is realized through a bank of modules, monitoring smaller groups of sensors that belong to the local sensor set. The detection of faults in sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The isolation of multiple faulty sensors in the local sensor set is realized by integrating the decisions of the LSFD agent's modules and applying a reasoning-based combinatorial decision logic. The simulation example of an automated highway system is used to illustrate the application of the multiple SFDI methodology.