Ensembles of neural networks with generalization capabilities for vehicle fault diagnostics

Author

Murphey, Yi L. ; Chen, Zhihang ; Abou-Nasr, Mahmoud ; Baker, Ryan ; Feldkamp, Timothy ; Kolmanovsky, Ilya

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA

fYear

2009

fDate

14-19 June 2009

Firstpage

2188

Lastpage

2194

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.

Keywords

Bayes methods; fault diagnosis; generalisation (artificial intelligence); neural nets; vehicles; A-Bayesian-entropy; BFES; analog Bayesian ensemble decision function; ensemble selection algorithm; generalization capability; neural network ensemble; neural networks; vehicle fault diagnostics; Automotive engineering; Bayesian methods; Biological neural networks; Boosting; Intelligent systems; Iterative algorithms; Neural networks; Training data; Vehicle driving; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Conference_Location

Atlanta, GA

ISSN

1098-7576

Print_ISBN

978-1-4244-3548-7

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2009.5178857

Filename

5178857