Fault diagnosis for an automotive suspension using particle filters

D.H. Alcantara; R. Morales-Menendez; L. Amezquita-Brooks

doi:10.1109/ECC.2016.7810568

Fault diagnosis for an automotive suspension using particle filters

D.H. Alcantara, R. Morales-Menendez, L. Amezquita-Brooks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

To diagnose faults in automotive suspension systems a particle filters based approach was evaluated. The look-ahead Rao-Blackwell Particle Filter was tested to online monitor different oil leaks in a magneto-rheological shock absorber. The non-linear semi-active suspension was modelled through the Jump Markov Linear Gaussian framework. The feasibility of this approach has been analyzed in a simulation environment using different road profiles. Early results with high precision and low variance are promised; however, the computing time is a hard constraint for an online application.

Original language	English
Title of host publication	2016 European Control Conference, ECC 2016
Pages	1898-1903
Number of pages	6
ISBN (Electronic)	9781509025916
DOIs	https://doi.org/10.1109/ECC.2016.7810568
Publication status	Published - 2016
Externally published	Yes

Publication series

Name	2016 European Control Conference, ECC 2016

Bibliographical note

Publisher Copyright:
© 2016 EUCA.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Control and Optimization

Access to Document

10.1109/ECC.2016.7810568

Cite this

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title = "Fault diagnosis for an automotive suspension using particle filters",

abstract = "To diagnose faults in automotive suspension systems a particle filters based approach was evaluated. The look-ahead Rao-Blackwell Particle Filter was tested to online monitor different oil leaks in a magneto-rheological shock absorber. The non-linear semi-active suspension was modelled through the Jump Markov Linear Gaussian framework. The feasibility of this approach has been analyzed in a simulation environment using different road profiles. Early results with high precision and low variance are promised; however, the computing time is a hard constraint for an online application.",

author = "D.H. Alcantara and R. Morales-Menendez and L. Amezquita-Brooks",

year = "2016",

doi = "10.1109/ECC.2016.7810568",

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isbn = "9781509025916",

series = "2016 European Control Conference, ECC 2016",

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AU - Alcantara, D.H.

AU - Morales-Menendez, R.

AU - Amezquita-Brooks, L.

PY - 2016

Y1 - 2016

N2 - To diagnose faults in automotive suspension systems a particle filters based approach was evaluated. The look-ahead Rao-Blackwell Particle Filter was tested to online monitor different oil leaks in a magneto-rheological shock absorber. The non-linear semi-active suspension was modelled through the Jump Markov Linear Gaussian framework. The feasibility of this approach has been analyzed in a simulation environment using different road profiles. Early results with high precision and low variance are promised; however, the computing time is a hard constraint for an online application.

AB - To diagnose faults in automotive suspension systems a particle filters based approach was evaluated. The look-ahead Rao-Blackwell Particle Filter was tested to online monitor different oil leaks in a magneto-rheological shock absorber. The non-linear semi-active suspension was modelled through the Jump Markov Linear Gaussian framework. The feasibility of this approach has been analyzed in a simulation environment using different road profiles. Early results with high precision and low variance are promised; however, the computing time is a hard constraint for an online application.

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U2 - 10.1109/ECC.2016.7810568

DO - 10.1109/ECC.2016.7810568

M3 - Conference contribution

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EP - 1903

BT - 2016 European Control Conference, ECC 2016

ER -

Fault diagnosis for an automotive suspension using particle filters

Abstract

Publication series

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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