Proposed FDII for Nonlinear Systems with Full-State Measurement

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 383)

In this monograph, a new integrated solution to the problem of fault detection, isolation and identification (FDII) for nonlinear systems is proposed. The proposed fault diagnosis methodology benefits from both a priori mathematical model information of the system and the nonlinear function approximation and adaptation capability of neural networks in a hybrid framework. More specifically, mathematical model of the system is used to construct a bank of parameterized fault models, which enables fault isolation.


Fault Diagnosis Extended Kalman Filter Recursive Little Square Unscented Kalman Filter Parallel Scheme 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.GobVision Inc., St. LaurentMontréalCanada
  2. 2.Dept. Electrical & Computer EngineeringConcordia UniversityMontrealCanada

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