Data-driven technology of fault diagnosis in railway point machines: review and challenges. Issue 4 (5th December 2022)

• Record Type: Journal Article
• Authors: Hu, Xiaoxi; Cao, Yuan; Tang, Tao; Sun, Yongkui
• Is Part Of: Transportation safety and environment. Volume 4:Issue 4(2022)
• Publication Date: 2022-12-05
• Subjects: Railway point machine (RPM) -- fault diagnosis -- data-driven technology -- condition monitoring; Transportation engineering -- Periodicals; Transportation -- Safety measures -- Periodicals; 629.04
• DOI: 10.1093/tse/tdac036
• Languages: English
• ISSNs: 2631-4428

Abstract

Abstract: Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs' characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.