Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

Faulty automotive systems significantly degrade the performance and efficiency of vehicles and are often major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data-reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: 1. class-specific Bayesian fusion; 2. joint optimization of the fusion center and individual classifiers; and 3. dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that the proposed fusion techniques outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.