1 Multi-agent decision fusion for motor fault diagnosis

Abstract

Improvement of recognition rate is the ultimate aim for fault diagnosis researchers using pattern recognition techniques. However, unique recognition method can only recognize a limited classification capability which is insufficient for application. An ongoing strategy is the decision fusion techniques. In order to avoid the shortage of single information source coupled with unique decision method, a new approach is required to generate better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources of different types of sensors and decisions of multiple classifiers. First, non-commensurate sensors data sets are combined using an improved sensor fusion method at a decision-level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to