Fault diagnosis for an automotive suspension using particle filters

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.