Ensembles of neural networks with generalization capabilities for vehicle fault diagnostics

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.