Model-based health monitoring for a vehicle steering system with multiple faults of unknown types

This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient faul...

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Main Authors: Yu, Ming, Wang, Danwei
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2014
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Online Access:https://hdl.handle.net/10356/103915
http://hdl.handle.net/10220/19329
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1039152020-03-07T14:00:34Z Model-based health monitoring for a vehicle steering system with multiple faults of unknown types Yu, Ming Wang, Danwei School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle. Accepted version 2014-05-15T02:06:21Z 2019-12-06T21:22:59Z 2014-05-15T02:06:21Z 2019-12-06T21:22:59Z 2013 2013 Journal Article Yu, M., & Wang, D. (2014). Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types. IEEE Transactions on Industrial Electronics, 61(7), 3574-3586. 0278-0046 https://hdl.handle.net/10356/103915 http://hdl.handle.net/10220/19329 10.1109/TIE.2013.2281159 en IEEE transactions on industrial electronics © 2013 IEEE. This is the author created version of a work that has been peer reviewed and accepted for publication by IEEE Transactions on Industrial Electronics, IEEE. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI:http://dx.doi.org/10.1109/TIE.2013.2281159]. 8 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems
Yu, Ming
Wang, Danwei
Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
description This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Yu, Ming
Wang, Danwei
format Article
author Yu, Ming
Wang, Danwei
author_sort Yu, Ming
title Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
title_short Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
title_full Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
title_fullStr Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
title_full_unstemmed Model-based health monitoring for a vehicle steering system with multiple faults of unknown types
title_sort model-based health monitoring for a vehicle steering system with multiple faults of unknown types
publishDate 2014
url https://hdl.handle.net/10356/103915
http://hdl.handle.net/10220/19329
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