Ensembles of neural networks with generalization capabilities for vehicle fault diagnostics

Authors:
Yi L. Murphey;Zhihang Chen;Mahmoud Abou-Nasr;Ryan Baker;Timothy Feldkamp;Ilya Kolmanovsky
Affiliations:
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Ford Motor company;Ford Motor company;Ford Motor company;Ford Motor company
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper presents a two-step ensemble approach for vehicle fault diagnostics, an ensemble selection algorithm BFES and an analog Bayesian ensemble decision function, A-Bayesian-Entropy. We show through experiments that a neural network ensemble designed and trained by the proposed methodology, and selected by BFES with A-Bayesian-Entropy as the ensemble decision function can generalize well to vehicle models that are different from the vehicles used to generate training data.