The study of model-based fault detection for mass production Diesel engines isthe aim of this thesis. The necessity of continuous vehicles health monitoring is nowenforced by the Euro VI pollutant legislation, which will probably be tightened in itsfuture revisions. In this context developing a robust strategy that could be easilycalibrated and work with different systems (due to production variability) would bea tremendous advantage for car manufacturers. The study developed here tries toanswer to those necessities by proposing a generic methodology based on local adaptiveobservers for scalar nonlinear state-affine systems. The fault detection, isolation andestimation problems are thus solved in a compact way. Moreover, the uncertaintiesdue to measurement or model biases and time drifts lead to the necessity of improvingthe detection methodology by the use of robust thresholds that could avoid undesiredfalse alarms. In this thesis a variable threshold is proposed based on the observabilitycondition and the sensitivity analysis of the parameter impacted by the fault withrespect to input or model uncertainties. This approach allows, among other things, tobe used as an analysis tool for the individuation of the system operating points for whichthe diagnosis is more reliable and more robust to inputs uncertainties. The discussedapproach has been successfully implemented and experimentally tested on a real Dieselengine for the intake leak detection and for the turbine efficiency loss drift detectionin a co-simulation environment showing its advantages in term of detection reliability,calibration effort and engines diagnosis operating condition analysis.
