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Fault diagnosis for a kind of nonlinear systems by using model-based contribution analysis.
- Source :
-
Journal of the Franklin Institute . Nov2018, Vol. 355 Issue 16, p8158-8176. 19p. - Publication Year :
- 2018
-
Abstract
- Abstract For the purpose of fault detection and isolation (FDI), reconstruction-based contribution (RBC) analysis is carried out in a model-based way. A bank of adaptive observers are designed for a set of potential faults. From these observers, fault estimates and fault signatures are directly available, thus contribution functions are conveniently constructed to accomplish the FDI work. This integrated design of contribution analysis and adaptive observer takes advantages of both data-driven and model-based approaches, and the diagnosis performance is improved. Furthermore, quantitative isolability analysis is also studied by similarity measurement of the obtained fault signatures. Simulation study with a nonlinear unmanned aerial vehicle (UAV) model shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NONLINEAR systems
*DEBUGGING
*DRONE aircraft
*ESTIMATION theory
*LEAST squares
Subjects
Details
- Language :
- English
- ISSN :
- 00160032
- Volume :
- 355
- Issue :
- 16
- Database :
- Academic Search Index
- Journal :
- Journal of the Franklin Institute
- Publication Type :
- Periodical
- Accession number :
- 132365238
- Full Text :
- https://doi.org/10.1016/j.jfranklin.2018.08.014