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A statistical monitoring approach for automotive on‐board diagnostic systems
Author(s) -
Barone Stefano,
D'Ambrosio Paolo,
Erto Pasquale
Publication year - 2007
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.834
Subject(s) - kalman filter , reliability (semiconductor) , automotive industry , recursion (computer science) , autoregressive model , computer science , set (abstract data type) , reliability engineering , statistical model , statistical process control , time series , engineering , artificial intelligence , machine learning , statistics , algorithm , power (physics) , physics , mathematics , process (computing) , quantum mechanics , programming language , aerospace engineering , operating system
Abstract The current generation of vehicle models are increasingly being equipped with on‐board diagnostic (OBD) systems aimed at assessing the ‘state of health’ of important anti‐pollution subsystems and components. In order to promptly diagnose and fix quality and reliability problems that may potentially affect such complex diagnostic systems, even during advanced development prior to mass production, some vehicle prototypes undergo a testing phase under realistic conditions of use (a mileage accumulation campaign). The aim of this work is to set up a statistical tool for improving the reliability of the OBD system by monitoring its operation during the mileage accumulation campaign of a new vehicle model. A dedicated software program was developed by the authors to filter the large experimental database recorded during the mileage accumulation campaign and to extract the time series of the diagnostic indices to be analysed. A model‐based monitoring approach, using continuous time autoregressive (CAR) models for the time‐series structure and traditional control charts for the estimated residuals, is adopted. A Kalman recursion procedure for the estimation of the unknown CAR model parameters is described. An application of the proposed approach is presented for a diagnostic index related to the state of health of the oxygen sensor. Copyright © 2006 John Wiley & Sons, Ltd.