On-board monitoring of air path for automotive IC engines
by Adnan Hamad; Dong-Ya Zhao; Ding-Li Yu; Quan-Min Zhu
International Journal of Modelling, Identification and Control (IJMIC), Vol. 20, No. 1, 2013

Abstract: Fault detection and isolation have become one of the most important aspects of automobile design. A new fault detection and isolation (FDI) scheme is developed for automotive engines in this paper. The method uses a radial basis function (RBF) neural network to model engine dynamics, and the modelling errors are used to form the basis for residual calculation. Furthermore, another RBF network is used as a fault classifier to isolate different faults from the modelling errors. The method is developed and the performance assessed using the engine benchmark, the mean value engine model (MVEM) with MATLAB/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10%-20% change superimposed on the outputs of manifold pressure, temperature and crankshaft speed sensors; one component fault considered is air leakage in intake manifold and exhaust gas recycle (EGR); the actuator fault considered is the malfunction of fuel injector. The simulation results showed that all the simulated faults can be clearly detected and isolated in the dynamic condition throughout the operating range.

Online publication date: Sat, 27-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com