State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

Abstract

In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Railway track surface faults dataset

“…These faults can give rise to railway accidents by underestimating the critical importance of condition monitoring and predictive maintenance of railway infrastructure. To mitigate potential risks to railway operations and enhance safety, it becomes imperative to identify the specific types of railway track faults accurately [ 1 , 2 ]. Considering importance of the railway track faults detection and identification, this dataset is established which could serve for development of efficient condition monitoring systems for railway tracks.…”

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes: A Review

“…In fact, this technology has found extensive application in the critical systems of railway vehicles, including the bogie system, traction system, brake system, train electrical system, and information control system [7]. Existing review articles primarily focus on bearings and wheels [2], [8], [9], [10], [11]. While some review articles have covered railway vehicle gearboxes, they are relatively superficial [5], [7].…”

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

“…These two methods have their own advantages and disadvantages, and the selection of the method is based on a comprehensive consideration of the inspection duration, fault severity level, and so on [24][25][26]. In the onboard method, sensors are mounted on the axle box or wheels of a train, and different types of signals can be obtained by corresponding sensors such as accelerometers, microphones, etc., for analysis [27][28][29][30][31][32][33][34][35]. The flat signal obtained by the on-board method has strong periodicity and better robustness, which is conducive to signal processing and wheel flat detection.…”