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Fault Detection and Isolation of the Multi-Sensor Inertial System

In order to solve the problem that the generalized likelihood test method cannot isolate the single fault of the four-gyro system and the double faults of the six-gyro system, a fault detection and isolation method combining the generalized likelihood test method with the residual error of the metab...

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Autores principales: Liang, Hao, Guo, Yu, Zhao, Xingfa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224025/
https://www.ncbi.nlm.nih.gov/pubmed/34064020
http://dx.doi.org/10.3390/mi12060593
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author Liang, Hao
Guo, Yu
Zhao, Xingfa
author_facet Liang, Hao
Guo, Yu
Zhao, Xingfa
author_sort Liang, Hao
collection PubMed
description In order to solve the problem that the generalized likelihood test method cannot isolate the single fault of the four-gyro system and the double faults of the six-gyro system, a fault detection and isolation method combining the generalized likelihood test method with the residual error of the metabolism grey model is presented. The problem of isolating the single fault of the four-gyro system and the double faults of the six-gyro system using the generalized likelihood test method is analyzed. The method and process of fault detection and isolation are designed. The validity of the method presented in this paper is verified by simulation tests of the single fault of the four-gyro system and the double faults of the six-gyro system. By comparing the isolation performance with the generalized likelihood test method, it is proved that the isolation performance of the method proposed in this paper is better than that of the generalized likelihood test method. The method mentioned in this paper can effectively realize fault detection and isolation of the multi-gyro system and improve the inertial system’s reliability.
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spelling pubmed-82240252021-06-25 Fault Detection and Isolation of the Multi-Sensor Inertial System Liang, Hao Guo, Yu Zhao, Xingfa Micromachines (Basel) Article In order to solve the problem that the generalized likelihood test method cannot isolate the single fault of the four-gyro system and the double faults of the six-gyro system, a fault detection and isolation method combining the generalized likelihood test method with the residual error of the metabolism grey model is presented. The problem of isolating the single fault of the four-gyro system and the double faults of the six-gyro system using the generalized likelihood test method is analyzed. The method and process of fault detection and isolation are designed. The validity of the method presented in this paper is verified by simulation tests of the single fault of the four-gyro system and the double faults of the six-gyro system. By comparing the isolation performance with the generalized likelihood test method, it is proved that the isolation performance of the method proposed in this paper is better than that of the generalized likelihood test method. The method mentioned in this paper can effectively realize fault detection and isolation of the multi-gyro system and improve the inertial system’s reliability. MDPI 2021-05-21 /pmc/articles/PMC8224025/ /pubmed/34064020 http://dx.doi.org/10.3390/mi12060593 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, Hao
Guo, Yu
Zhao, Xingfa
Fault Detection and Isolation of the Multi-Sensor Inertial System
title Fault Detection and Isolation of the Multi-Sensor Inertial System
title_full Fault Detection and Isolation of the Multi-Sensor Inertial System
title_fullStr Fault Detection and Isolation of the Multi-Sensor Inertial System
title_full_unstemmed Fault Detection and Isolation of the Multi-Sensor Inertial System
title_short Fault Detection and Isolation of the Multi-Sensor Inertial System
title_sort fault detection and isolation of the multi-sensor inertial system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224025/
https://www.ncbi.nlm.nih.gov/pubmed/34064020
http://dx.doi.org/10.3390/mi12060593
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