# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 5 Conclusions and Discussion
To be more specific in the case of the latter, the wheel condition of a train, the train composition, loading status and speed certainly influence the backscattered intensity and therefore also affect the signal pattern. In the case of the infrastructure, its condition, superstructure and substructure will certainly have an impact on the signal. Further factors regarding the signal characteristics emerge due to random effects (phase in the sinus wave) and sensor effects (especially sensor noise). The data volume is always critical as further aspects need to be covered when using DAS in operation. These include considerations in the contexts of data mining, data processing and finally data storage. The question to be answered here is which values should be stored. The sensing principle shows the response of the infrastructure to the load induced by train movements, all of which vary among the different train types and compositions. Linked to this issue is also the integration of DAS data into existing system architectures and, probably the more important aspect--what data needs to be stored. Considering the huge data quantity that must be dealt with (a terabyte of raw data for just one day), it may be expedient to delete all processed/raw data and to retain only some statistical values. Additionally, also connected to this issue is the automated starting procedure for the measurements. If DAS is used for condition monitoring, it is feasible to record only those train movements that are of interest and, consequently, to reduce the data volumes on a precisely selected basis. In general, examinations focusing on the frequency content of a signal and applying several digital filters make it possible to distinguish between the condition of different assets and sections. The results presented in this work point out that the system can distinguish between different asset conditions and detect track defects. The shortcomings of the applied technology are the inability of assigning a signal pattern explicitly to a specific failure/defect and to make a conclusion about the actual track geometry. Nevertheless, the latter is not necessarily mandatory, since track recording vehicles are designed to fulfil this task. In order to identify a specific failure, more defects of the same type are necessary and allow for an unequivocal identification. In combination with asset data, this permits one to draw conclusions regarding the reason for signal anomalies and to respond by carrying out the maintenance tasks required. The more sophisticated approach poses phase-sensitive systems, as they show promising results in fault detection and the identification of such. Furthermore, as phase-sensitive systems (quantitative measurements) are considered state-of-the art systems, most of the research and development focusses on these systems. However, quantitative systems have also some disadvantages, namely a shorter sensing length (compared to qualitative systems) and increased data amount, but suppliers and researching institutes are focusing on solving these issues at the moment. However, it does not matter whether a qualitative or a quantitative system is used and applied, the results prove the ability of distributed acoustic sensing for the monitoring of infrastructure conditions. Conceptualization, I.V., S.M. ; methodology, I.V. ; validation, I.V. and S.M. ; formal analysis, I.V. ; investigation, I.V. ; resources, I.V. ; writing--original draft preparation, I.V. ; writing--review and editing, I.V., S.M. ; visualization, I.V. ; supervision, S.M. ; project administration, I.V. and S.M. ; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript. Open Access Funding by the Graz University of Technology. We would like to acknowledge our project partner Swiss Federal Railways (SBB). The authors declare no conflict of interest.