Fault Detection of Railway Point Machine Using Diagnostic Models

Abstract

Railway point devices are critical elements of railway infrastructure. Point device failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. The article presents a concept of a simple fault detection system dedicated to health monitoring of point machines in a remote and automatic way. A fundamental element of the system is a fault detection module, which operates on the basis of residual signal calculated on the basis of output of object models. Autoregressive (ARX) and neural (ANN) models were identified using real data collected during 1 year at the turnout localized in Poland. Obtained results show that simple analytical ARX model is a sufficient solution for efficient fault detection of the investigated point machine and can be easily applied in the proposed condition monitoring system.