Parameter estimation algorithms for a set-membership description of uncertainty
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Robustness in Identification and Control
Robustness in Identification and Control
Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis
Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis
Neuro-fuzzy networks and their application to fault detection of dynamical systems
Engineering Applications of Artificial Intelligence
Confidence estimation of the multi-layer perceptron and its application in fault detection systems
Engineering Applications of Artificial Intelligence
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Confidence estimation of GMDH neural networks and its application in fault detection systems
International Journal of Systems Science
Brief paper: Interval observer design for consistency checks of nonlinear continuous-time systems
Automatica (Journal of IFAC)
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
International Journal of Applied Mathematics and Computer Science
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
Brief Causal fault detection and isolation based on a set-membership approach
Automatica (Journal of IFAC)
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In this paper, a new passive robust fault detection method is proposed. In virtue of its simple topological structure and universal approximation ability, the RBF neural network is utilized in the system identification for the fault detection. The set membership identification is used to calculate a set of uncertain weights which describes the model uncertainty. This set allows obtaining an adaptive threshold of the residual which is next applied to the robust fault detection. A model structure selection scheme which can delete the redundant hidden nodes is proposed to reduce the conservatism of the uncertain set. A narrower threshold can be generated owing to the contraction of uncertain set and therefore the fault detection sensitivity is increased. Three examples show the satisfying performance of the proposed robust fault detection method.