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.

Railway Automatic Switch (RAS) plays a crucial role in Turnout Switching System (TSS). The size of RAS’s Stationary Contacts (SCs) directly affects connectivity of the pivotal control and feedback circuit, which further influences the remaining useful life of TSS. However, it is impossible to avoid normal wear and tear or fractures of SC during daily operation, resulting in size change of SCs. Therefore, it is vital to monitor the size of SCs. However, due to lack of wear samples, it is hard to design automatic algorithms for this task, especially for developing currently popular deep learning. To this end, this paper proposes a computer vision method for railway automatic switch stationary contacts wear detection under few-shot occasions. Our method includes two key modules: a Few Shot SC DETection (FSDet) module and a Contour-based Size MEAsurement (CSMea) module, which together form a system that achieves accurate SC detection and size monitoring. The FSDet module formulates a multi-template deep feature matching pipeline, which plays the role of detecting all SCs in an image under the few shot manner. Then, the CSMea module takes the above detected SC patches as input and detects wear regions utilizing contour features and key point features. Finally, size of SCs can be calculated in image level by computing average pixels distance in wear regions and rescaled into real world level using image calibration tools. Experimental results demonstrate that the proposed method can accurately and robustly detect and measure the size of different SC structures in few-shot occasions.

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.

In the railway transportation industry, fault diagnosis of railway point machines (RPMs) is vital. Because operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signal-based fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently robust. To overcome these shortcomings, we propose a double-scale wide first-layer kernel convolutional neural network (DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for low-sampling-rate signals. Meanwhile, the proposed double-scale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison, were conducted to demonstrate the robustness and accuracy of the proposed algorithm.