Classifier Fusion for Acoustic Emission Based Tool Wear Monitoring

Abstract

It is often difficult for a single classifier to achieve perfect classification during process monitoring. Sensor fusion enables the final decision to be improved, but uses voting methods, which usually do not perform well when there is a tie vote. In this paper, classifier fusion with class-weighted voting is investigated to further enhance the performance of monitoring systems. The overall performances of individual classifiers are used as the weighting factors to classifier fusion based on majority voting. When applied to tool wear monitoring of the coroning process, the classifier that was based on overall performance weighting improved the classification rate to 95.6% and the one based on state performance weighting showed 98.5% classification, compared to 87.7% for classifier fusion with unity weighting. A classifier fusion further increased performance from 98.5% to 99.7% by applying a penalty vote on the weighting factor.