期刊
CONTROL ENGINEERING PRACTICE
卷 14, 期 6, 页码 699-717出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2005.04.015
关键词
detection and isolation; discrete multiple-model observers; neuro-fuzzy networks; discrete unknown input observers for FDI; discrete non-linear observers; robust FDI
This paper presents a new framework for fault detection and isolation (FDI) based on neuro-fuzzy multiple modelling together with robust optimal de-coupling of observers. This new paradigm is called the 'Neuro-Fuzzy and De-coupling Fault Diagnosis Scheme' (NFDFDS). Multiple operating points are taken care of through the NF modelling framework. The structure also provides residuals that are de-coupled to 'unknown inputs', making use of the earlier research on unknown input de-coupling. The NF paradigm exploits the combined abilities of neural networks and fuzzy logic and is an efficient modelling tool for non-linear dynamic systems because of its approximation and reasoning capabilities. The paper also provides a comparative study of NFDFDS with the Extended Unknown Input Observer (EUIO) for FDI, using the DAMADICS benchmark example. (c) 2005 Elsevier Ltd. All rights reserved.
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