TY - GEN
T1 - Fault diagnosis for an automotive suspension using particle filters
AU - Alcantara, D.H.
AU - Morales-Menendez, R.
AU - Amezquita-Brooks, L.
PY - 2017
Y1 - 2017
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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85015033497&partnerID=MN8TOARS
U2 - 10.1109/ECC.2016.7810568
DO - 10.1109/ECC.2016.7810568
M3 - Conference contribution
SP - 1898
BT - 2016 European Control Conference, ECC 2016
ER -