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Diagnosis of a Truck Engine using Nolinear Filtering Techniques
Linköping University, Department of Electrical Engineering.
2007 (English)Independent thesis Basic level (professional degree), 20 points / 30 hpStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
Institutionen för systemteknik , 2007. , 78 p.
Keyword [en]
particle filter, diagnosis, extended Kalman filter, likelihood, cusum
National Category
Signal Processing
URN: urn:nbn:se:liu:diva-8959ISRN: LITH-ISY-EX--07/3982--SEOAI: diva2:23671
Available from: 2007-07-16 Created: 2007-07-16

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