On-board monitoring for smart assessment of railway infrastructure: A systematic review

The increasing demand in mobility forms a major challenge for modern cities, even more so when examined under the prism of transition from traditional to CO2-free mobility. Railway infrastructure forms a main carrier for the mobility of people and goods and a salient component of critical infrastructures. The increased traffic frequency in urban transport imposes higher capacity demands and leads to more frequent damage and more severe deterioration and associated disruptions to service and availability. Aligning with the spirit of smart cities, and data-driven decision support, infrastructure operators require timely information regarding the current (diagnosis) and future (prognosis) condition of their assets in order to sensibly decide on maintenance and renewal actions. Railway condition assessment has traditionally heavily relied on-site visual inspections. Main measurement parameters for railway tracks are obtained since the 1960s. Quality, accuracy, and precision of measurements heavily evolved since then, including aspects such as storage, analysis, and interpretation of data. In recent years, specialized monitoring vehicles offer an automated means for relaying essential information on condition, obtained from diverse measurements including laser measurements, vibration, image, and ultrasonic information. Powered by this information diagnostic vehicles have shifted assessment from a reactive to a predictive mode. More recently, in-service vehicles equipped with low-cost on-board monitoring (OBM) measuring devices, such as accelerometers, have been introduced on railroad networks, traversing the network at higher frequencies than the specialized diagnostic vehicles. The collected information includes position, acceleration, and in some cases force measurements. The measured data require interpretation into quantifiable track-quality indicators, before it can be meaningfully incorporated in asset management tools. These indicators form the basis for real-time forecasting of condition evolution and asset management, which are essential traits of a transport infrastructure that fits the vision of smart cities. This chapter explores the state of the art of OBM for railway infrastructure condition assessment, conducting a thorough review of data-processing methodologies, which is further complemented with application examples.