A review on existing technologies used in the maintenance of railway infrastructure

Abstract

The railway system is essential to transportation as it transports products and passengers safely. Due to increased demand for train service, the railway track will deteriorate and fail. Frequent track inspection is necessary to ensure the safety and security of cargo and passengers and prevent losses. Traditionally, railway track inspections are carried out once every few months. It is done by a human inspector or an automated vehicle, a rare, complex operation that takes too long to find and track flaws quickly, which is far from optimal. As a result, condition-based monitoring and predictive maintenance are critical because they allow railway companies to do preventative maintenance before problems and failures occur. Many sensing methods have emerged, and sensors have become much more affordable. As a result, in recent years, sophisticated monitoring equipment has been used to keep an eye on the railway track and collect data in real time. This research aims to review the critical role of wireless sensor networks in monitoring the condition of railway facilities: what kinds of sensing devices are employed in structural monitoring, and why. We will also identify their benefits and limitations. Additionally, researchers have employed AI algorithms for rail defect identification and prognosis in recent years to help the railway sector respond quickly to failures. This study also discusses the current AI techniques in diagnosing rail problems by utilizing inspection device data to create railway degradation prediction models. These models predict flaws.