Fault Detection of Railway Point Machines Using Electrical Signals

Abstract

With the tremendous deployment of different kinds of sensors and actuators, the Internet of Things (IoT) emerges as an advanced method to connect devices and collect the status data. Aided by the use of a large amount of operation data, the data-driven fault diagnosis is considered as a modern technique in Industry 4.0 and has become a research hotspot in recent years. In the area of urban rail transit, the point machine is a critical component that is used to safely switch the train direction. In this article, we propose a novel fault diagnosis scheme for railway point machines (RPMs) using electrical signals. RPMs are devices that move a switch blade from its current position to the opposite position to offer different routes to trains. RPMs failures often lead to service delays or dangerous situations; therefore, detecting early signs of their deteriorating condition is essential. We present a powerful and interpretable method that relies on the construction of the smoothed periodogram, which are estimator of spectral density. Experimental results demonstrate the efficacy of the proposed method.