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Model-based fault detection in Diesel engines air-path

Riccardo Ceccarelli 1, 2, 3, 4
2 GIPSA-Services - GIPSA-Services
GIPSA-lab - Grenoble Images Parole Signal Automatique
4 NECS - Networked Controlled Systems
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : The study of model-based fault detection for mass production Diesel engines is the aim of this thesis. The necessity of continuous vehicles health monitoring is now enforced by the Euro VI pollutant legislation, which will probably be tightened in its future revisions. In this context developing a robust strategy that could be easily calibrated and work with different systems (due to production variability) would be a tremendous advantage for car manufacturers. The study developed here tries to answer to those necessities by proposing a generic methodology based on local adaptive observers for scalar nonlinear state-affine systems. The fault detection, isolation and estimation problems are thus solved in a compact way. Moreover, the uncertainties due to measurement or model biases and time drifts lead to the necessity of improving the detection methodology by the use of robust thresholds that could avoid undesired false alarms. In this thesis a variable threshold is proposed based on the observability condition and the sensitivity analysis of the parameter impacted by the fault with respect to input or model uncertainties. This approach allows, among other things, to be used as an analysis tool for the individuation of the system operating points for which the diagnosis is more reliable and more robust to inputs uncertainties. The discussed approach has been successfully implemented and experimentally tested on a real Diesel engine for the intake leak detection and for the turbine efficiency loss drift detection in a co-simulation environment showing its advantages in term of detection reliability, calibration effort and engines diagnosis operating condition analysis.
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Submitted on : Tuesday, November 27, 2012 - 10:52:29 AM
Last modification on : Thursday, January 20, 2022 - 5:29:15 PM
Long-term archiving on: : Thursday, February 28, 2013 - 3:42:47 AM


  • HAL Id : tel-00757525, version 1



Riccardo Ceccarelli. Model-based fault detection in Diesel engines air-path. Automatic Control Engineering. Institut National Polytechnique de Grenoble - INPG, 2012. English. ⟨tel-00757525⟩



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