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This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local(More)
This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, an adaptive approximation technique is used for learning online the modeling uncertainty. Then, local SFDI modules are designed(More)
This paper presents the design of a methodology for distributed detection and isolation of multiple sensor faults in heating, ventilation and air-conditioning (HVAC) systems. The proposed methodology is developed in a distributed framework with the HVAC system modeled as a set of interconnected, nonlinear subsystems. A local sensor fault diagnosis (LSFD)(More)
This paper presents the design of a methodology for diagnosing sensor faults in heating, ventilation and air-conditioning (HVAC) systems, and compensating their effects on the distributed control architecture. The proposed methodology is developed in a distributed framework, considering a multi-zone HVAC system as a set of interconnected, nonlinear(More)
Fault detection in micro-electrostatic actuators caused primarily by their mechanical components (combs, thin air-damping) is investigated in this article. The system is assumed to be linearly parameterizable and the parameter vector contains the quantities that are susceptible to faults. While the system is operating in a closed-loop configuration, a(More)
This paper proposes a distributed methodology for detecting and isolating multiple sensor faults in interconnected cyberphysical systems. The distributed sensor fault detection and isolation process is conducted in the cybersuperstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a(More)
In this article, a set membership (SM) identification technique is tailored to detect faults in microelectromechanical systems. The SM-identifier estimates an orthotope which contains the system's parameter vector. Based on this orthotope, the system's output interval is predicted. If the actual output is outside of this interval, then a fault is detected.(More)