Operational efficiency is one of the key performance indicators for all railroad systems. Infrastructure inspection and maintenance engineers are tasked with the responsibility of ensuring the reliability, availability, maintainability and safety of the railroad network. However, as rolling stock traffic density increases throughout the network, inspection and maintenance opportunities become less readily available. Inspection and maintenance activities normally take place at night, when there is little or no train movement to avoid disruption of normal railroad network operation. In addition, conventional inspection methodologies fail to deliver the efficiency required for the optimization of maintenance decisions, particularly with respect to track renewals, due to their defect detection sensitivity and level of resolution limitations. The fact that critical structural components such as rails and crossings (frogs) are randomly loaded increases the degree of uncertainty when trying to estimate their remaining service lifetime. Maintenance decisions are predominantly based on the feedback received from inspection engineers, coupled with empirical knowledge that has been gained over the years. The use of structural degradation models is too risky due to the uncertainty arising from the variable dynamic loads sustained by the rail track. The use of structural health monitoring techniques offers significant advantages over conventional approaches. First of all, it is non-intrusive and does not interrupt normal rail traffic operations. Secondly, defects can be detected and evaluated in real-time whilst their evolution can be monitored continuously, enabling maintenance to be scheduled in advance and at times where the need for rail network availability at the section concerned is at its lowest. This paper analyzes the potential risks and benefits of a gradual shift from traditional inspection approaches to advanced structural health monitoring techniques.
Augmented Reality (AR)-Based Sensor Location Recognition and Data Visualization Technique for Structural Health Monitoring
