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A Model-Free Diagnosis Approach for Intake Leakage Detection and Characterization in Diesel Engines

by Ghaleb Hoblos *,† and Mourad Benkaci
ESIGELEC-IRSEEM, Avenue Galilée, 76801 Saint Etienne du Rouvray, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: David Mba
Machines 2015, 3(3), 157-172; https://doi.org/10.3390/machines3030157
Received: 2 October 2014 / Revised: 15 June 2015 / Accepted: 21 July 2015 / Published: 31 July 2015
Feature selection is an essential step for data classification used in fault detection and diagnosis processes. In this work, a new approach is proposed, which combines a feature selection algorithm and a neural network tool for leak detection and characterization tasks in diesel engine air paths. The Chi square classifier is used as the feature selection algorithm and the neural network based on Levenberg-Marquardt is used in system behavior modeling. The obtained neural network is used for leak detection and characterization. The model is learned and validated using data generated by xMOD. This tool is used again for testing. The effectiveness of the proposed approach is illustrated in simulation when the system operates on a low speed/load and the considered leak affecting the air path is very small. View Full-Text
Keywords: leak detection; automotive diagnosis; feature selection; neural data classification; diesel air path leak detection; automotive diagnosis; feature selection; neural data classification; diesel air path
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Hoblos, G.; Benkaci, M. A Model-Free Diagnosis Approach for Intake Leakage Detection and Characterization in Diesel Engines. Machines 2015, 3, 157-172.

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