# Evaluating Short-Wave Effects in Railway Track Using the Rail Surface Signal
## 1 Introduction
[PERSON] gives an example for the detection of squats [11], and [PERSON] describes the possibility for evaluating the health condition of insulated rail joints [12]. Axle box accelerators can be applied at very high speeds and still provide high sampling rates. Additional track unavailability can be avoided by mounting the devices on axles of in-service trains or regular measuring cars. Axle box accelerations are a direct consequence of short-wave effects in the wheel-rail contact point. On the other hand, accelerations are not only triggered by rail condition but also by the condition of the wheels. While a wheel that is flat produces periodic peaks that can be handled relatively well, the influence of wheel out-of-roundness and indentations is rather unpredictable. In addition, the speed of the vehicle has an influence on the amplitude of the measurements making it difficult to compare the signal characteristics [13]. High-resolution images of rails are another method for rail defect detection with great potential, but has not yet been successfully implemented beyond trial operations. This procedure needs intelligent image processing algorithms. [PERSON] and [PERSON] give an overview on the principles of this method [14; 15]. Despite a great deal of research, no system has been fully proven in operation yet. It is also possible to evaluate short-wave effects using lasers. There are different possibilities of laser arrangement and subsequent data processing. The measurement system of the rail surface described in detail in the method section corresponds to one of these designs. [PERSON] could show that it is also possible to use Fibre Optic Sensing for the detection of short-wave defects [16]. In his thesis, [PERSON] demonstrated that the system provides information on the condition of insulated rail joints and turnout crossings. Research on distributed acoustic sensing is ongoing. There are several other concepts conceivable. [PERSON] gives an overview on measuring principles like ultrasonic transducers, magnetic induction, eddy current technologies, radiography, long-range ultrasonics and laser ultrasonics [17]. Although all the described methods are fundamentally suitable for the assessment of short-wave effects, they have not been able to establish themselves in practice for a variety of reasons. This is partly due to the fact that the achievable measurement speeds are clearly too low, which makes a network-wide implementation unfeasible. This applies, for example, to many of the methods described by [PERSON]. For various technical reasons, fiber optic sensing and image recognition tools are not yet developed far enough to make reliable statements. It is also not expected that all relevant effects can be evaluated with these systems. The same applies to axle box accelerations. These have already been intensively researched and provide reliable results for certain scenarios. However, they do not only react to the effects of the rail, but also to vehicle properties and stiffness changes of the track. A reliable classification of effects only with axle box accelerations is not considered possible, at least not at present. In addition, many of the methods raise the question of the relative and absolute positioning of the data. Exact positioning of the measurement data is required for all measurement principles. [PERSON] distinguishes absolute positioning errors and relative positioning errors [18]. Since absolute positioning errors describe the deviation between the positioning and the absolute position of a point on the track, they are particularly important for planning maintenance activities. Relative errors, on the other hand, describe the deviation of the positioning between different measurement runs and are relevant for the investigation of time series. For short-wave effects, relative errors are primarily relevant, but the absolute error can also be very important. Additionally, [PERSON] describes a way to significantly decrease the relative positioning errors using big-data fusion and incremental learning [18].