Fault diagnosis for planetary gearboxes using multi-criterion fusion feature selection framework

Abstract

Feature selection has been used to achieve dimension reduction in the field of fault diagnosis. This article introduces a multi-criterion fusion framework for feature selection that takes into account three aspects of features: effectiveness, correlation, and classification performance. This framework enables a more comprehensive evaluation of features than does a single criterion. The proposed framework is implemented using five effectiveness criteria and a correlation criterion. It is used to diagnose eight failure modes of a planetary gearbox. The experimental results demonstrate that the proposed multi-criterion framework outperforms many well-studied single criteria.