Fault Detection for Point Machines: A Review, Challenges, and Perspectives

• Published in: Actuators Vol. 12; no. 10; p. 391
• Main Authors: Hu, Xiaoxi, Tang, Tao, Tan, Lei, Zhang, Heng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Actuators; Algorithms; Anomalies; anomaly detection; Condition monitoring; Control equipment; Efficiency; Energy consumption; Fault detection; Frogs; Hydraulics; Light rail transportation; point machines; R&D; Railroad accidents & safety; Railroad crossings; Railroads; Research & development; Sensors; Trains; China; Germany
• ISSN: 2076-0825; 2076-0825
• DOI: 10.3390/act12100391

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection.