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Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Pertu...

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Detalles Bibliográficos
Autores principales: Castaldi, Paolo, Mimmo, Nicola, Simani, Silvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677446/
https://www.ncbi.nlm.nih.gov/pubmed/28946673
http://dx.doi.org/10.3390/s17102202
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author Castaldi, Paolo
Mimmo, Nicola
Simani, Silvio
author_facet Castaldi, Paolo
Mimmo, Nicola
Simani, Silvio
author_sort Castaldi, Paolo
collection PubMed
description Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system.
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spelling pubmed-56774462017-11-17 Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach Castaldi, Paolo Mimmo, Nicola Simani, Silvio Sensors (Basel) Article Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. MDPI 2017-09-25 /pmc/articles/PMC5677446/ /pubmed/28946673 http://dx.doi.org/10.3390/s17102202 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Castaldi, Paolo
Mimmo, Nicola
Simani, Silvio
Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title_full Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title_fullStr Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title_full_unstemmed Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title_short Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
title_sort avionic air data sensors fault detection and isolation by means of singular perturbation and geometric approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677446/
https://www.ncbi.nlm.nih.gov/pubmed/28946673
http://dx.doi.org/10.3390/s17102202
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