Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Parameterized LMIs in Control Theory
SIAM Journal on Control and Optimization
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fault diagnosis for a class of chemical batch processes
ACC'09 Proceedings of the 2009 conference on American Control Conference
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In this paper a scheme for sensor fault diagnosis in chemical batch reactors is proposed. The scheme is based on a bank of two observers aimed at generating a set of residuals sensitive to faults occurrence. The observers are designed via a H"~ approach, so as to guarantee faults detection and isolation even in the presence of external disturbances and modeling errors. The heat released by the reaction is estimated via an universal approximator, based on radial basis functions, whose weights are adjusted on-line. Finally, the estimates provided by the observers and the sensor measures are processed to provide information about the faulty sensor as well as an healthy measure. In order to test the effectiveness of the proposed approach, a simulation case study is developed.