Scanning the Issue

Abstract

Beginning with an emphasis on data modeling, this article presents a systematic review, challenges, and perspectives of data-driven fault detection and diagnosis (FDD) methods for traction systems in high-speed trains. Along with these notable FDD research, an explosive growth of data-driven FDD methods would be witnessed to provide exceptional capabilities for performing FDD tasks in the coming years. The authors believe that this survey article is fundamental to understand the basics of these FDD methods, and also provides researchers and engineers with valuable guidance.