# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 1 Introduction  
The track is one of the basic elements of railroads. Since the weight of trains is discharged on a very small portion of rail surfaces, tracks require careful maintenance [1]. It is well known that: (a) rails are subject to intense bending and shear stresses, plastic deformation and wear, leading to progressive degradation of their structural integrity [2]; (b) they may contain internal fabrication defects undetected by quality control. All of this may result in rail breakage and, consequently, in train derailment with potential catastrophic consequences (traffic interruption, possible losses of rolling stock material and even lives, etc.).  
According to the European Union agency for railways (ERA) safety overview for 2017 [3], in the years 2011-2015, broken rails have represented the main form of precursors to accidents, i.e., of inconveniences that, under other circumstances, could have led to an accident (other precursors to accidents include track buckles, signals passed at danger, wrong-side signaling failures, broken wheels, and axles). On the average, 4445 broken rails per year have been detected over a total of 11,222 precursors to accidents in the whole European Union (EU-28) in the same period. This explains why track inspection and maintenance play a fundamental role; moreover, these aspects are expected to become more and more crucial since operative loads, traffic, and speeds are progressively increasing [1].  
Over the years, various systems have been developed to monitor the health of rails. Since all the currently available solutions do not fully match the requirements set by railway infrastructure management companies, substantial research efforts are still required in this field. In particular, an important challenge is represented by the development of reliablemethods for detecting potentially hazardous rail defects before they evolve in rail breakages. In fact, the availability of rail diagnostics methods allow for introducing predictive maintenance procedures instead of the preventive maintenance strategy commonly used nowadays. This will lead to more reliable railway networks, with a substantial increase of the efficiency and economic sustainability of maintenance procedures [4].  
In the last two decades, few review articles about the monitoring of rail tracks have been published [4; 5; 6]. Moreover, in recent years, new technologies and approaches have been developed in this field, and the interest of authorities and companies has grown. This has motivated the writing of this manuscript that aims at providing a self-contained and comprehensive overview of the techniques and systems for rail defect detection based on _ultrasonic guided waves_ (UGWs). This technology is relevant since it allows for accomplishing _non destructive testing_ (NDT) and to sound a large area by means of a single transducer [7]. Two technical solutions are available for track monitoring based on UGWs. The first solution is based on a measuring equipment placed on board a moving diagnostic vehicle. It offers the important advantage of an increased inspection speed with respect to conventional bulk ultrasound or eddy currents methods; it is worth remembering that conventional methods suffer from slow inspection speed and, consequently, reduce the infrastructure availability for commercial services. The second solution, instead, relies on a monitoring equipment placed on the ground; this makes possible a constant monitoring of rail status if the employed sensors are distributed along the considered rail network.  
The remaining part of this manuscript is organized as follows. In Section 2, a taxonomy of rail defects and the main techniques for their detection are described. A classification of the techniques based on guided ultrasound waves and a description of their architecture is provided in Sections 2 and 4, respectively. Various details about their implementation are illustrated in Sections 5-8, whereas their performance is analyzed in Section 9. Finally, some conclusions are offered in Section 10.