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

4 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
Number of pages	1903
DOIs	https://doi.org/10.1109/ECC.2016.7810568
Publication status	Published - 2017
Externally published	Yes

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10.1109/ECC.2016.7810568

http://www.scopus.com/inward/record.url?eid=2-s2.0-85015033497&partnerID=MN8TOARS

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Alcantara, D. H., Morales-Menendez, R., & Amezquita-Brooks, L. (2017). Fault diagnosis for an automotive suspension using particle filters. In 2016 European Control Conference, ECC 2016 (pp. 1898). [7810568] https://doi.org/10.1109/ECC.2016.7810568

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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.",

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