Literature DB >> 28946673

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

Paolo Castaldi1, Nicola Mimmo2, Silvio Simani3.   

Abstract

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.

Entities:  

Keywords:  NonLinear Geometric Approach; air data sensors; aircraft; autopilot avionics; fault detection and isolation; singular perturbation

Year:  2017        PMID: 28946673      PMCID: PMC5677446          DOI: 10.3390/s17102202

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  6 in total

1.  Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft.

Authors:  Guillermo Heredia; Aníbal Ollero
Journal:  Sensors (Basel)       Date:  2010-03-17       Impact factor: 3.576

2.  Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems.

Authors:  Gang Huang; Yi-Ping Luo; Chang-Fan Zhang; Yi-Shan Huang; Kai-Hui Zhao
Journal:  Sensors (Basel)       Date:  2015-05-11       Impact factor: 3.576

3.  Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

Authors:  Namju Jeon; Hyeongcheol Lee
Journal:  Sensors (Basel)       Date:  2016-12-12       Impact factor: 3.576

4.  Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries.

Authors:  José-Luis Casteleiro-Roca; José Luis Calvo-Rolle; Juan Albino Méndez Pérez; Nieves Roqueñí Gutiérrez; Francisco Javier de Cos Juez
Journal:  Sensors (Basel)       Date:  2017-01-18       Impact factor: 3.576

5.  Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

Authors:  Feng Lu; Jinquan Huang; Yaodong Xing
Journal:  Sensors (Basel)       Date:  2012-08-09       Impact factor: 3.576

6.  An SVM-based solution for fault detection in wind turbines.

Authors:  Pedro Santos; Luisa F Villa; Aníbal Reñones; Andres Bustillo; Jesús Maudes
Journal:  Sensors (Basel)       Date:  2015-03-09       Impact factor: 3.576

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.