Diagnosis of a Truck Engine using Nonlinear Filtering Techniques
Scania CV AB is a large manufacturer of heavy duty trucks that, with
an increasingly stricter emission legislation, have a rising demand
for an effective On Board Diagnosis (OBD) system. One idea for
improving the OBD system is to employ a model for the construction of
an observer based diagnosis system. The proposal in this report is,
because of a nonlinear model, to use a nonlinear filtering method for
improving the needed state estimates. Two nonlinear filters are
tested, the Particle Filter (PF) and the Extended Kalman Filter
(EKF). The primary objective is to evaluate the use of the PF for
Fault Detection and Isolation (FDI), and to compare the result against
the use of the EKF.
With the information provided by the PF and the EKF, two residual
based diagnosis systems and two likelihood based diagnosis systems are
created. The results with the PF and the EKF are evaluated for both
types of systems using real measurement data. It is shown that the
four systems give approximately equal results for FDI with the
exception that using the PF is more computational demanding than using
the EKF. There are however some indications that the PF, due to the
nonlinearities, could offer more if enough CPU time is available.
Fredrik Nilsson
2007
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Senast uppdaterad: 2021-11-10
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