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The paper presents a drift sensor fault detection scheme for a class of nonlinear uncertain system with partially measurable state variables. A rigorous fault detectability and detection-time interval analysis is illustrated. Moreover, the set of detectable sensor faults is characterized using off-line information and an upper bound on the fault detection interval is derived. Finally, a monotonicity...
Critical infrastructures (CIs) are large scale systems that are essential for the smooth and reliable operation of everyday activities in modern societies. To understand the operation characteristics of CIs, as well as the behavior of interdependencies between different CIs, we usually use modeling and simulation methodologies. In this work we propose an interdependent CI modeling methodology based...
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,...
This paper deals with the problem of designing a robust fault detection methodology for a class of input-output, uncertain dynamical distributed parameter systems, namely mechanical elastodynamic systems, which are representative of a whole class of problems related to on-line health monitoring of mechanical and civil engineering structures. The proposed approach does not require full state measurements...
This paper develops a filtering approach for distributed fault detection of a class of interconnected continuous-time nonlinear systems with modeling and measurement uncertainties. A distributed fault detection scheme and corresponding thresholds are designed based on filtering certain signals so that the effect of high frequency measurement uncertainty is diminished. The analysis of the proposed...
This paper extends very recent results on a distributed fault diagnosis methodology for nonlinear uncertain large-scale discrete-time dynamical systems to the case of partial state measurement. The large scale system being monitored is modeled, following a divide et impera approach, as the interconnection of several subsystems that are allowed to overlap sharing some state components. Each subsystem...
Several empirical studies have demonstrated the feasibility of employing neural networks as models of nonlinear dynamical systems. This paper develops the appropriate mathematical tools for synthesizing and analyzing stable neural network based identification and control schemes. Feedforward network architectures are combined with dynamical elements, in the form of stable filters, to construct a general...
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