# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 1 Introduction  
Railway infrastructure is subject to wear and tear. An increasing traffic volume, higher train speeds and thus burgeoning loads enhance the deterioration of infrastructure and challenge infrastructure managers (IM) today in their task of ensuring the required track quality and availability. The latter two are essentially determined by both the condition of the rolling stock and railway infrastructure. While the rolling stock condition lies in the responsibility of railway undertakings (RU), infrastructure managers have the duty of caring for the condition and consequently the availability of railway tracks. The deterioration of, e.g., the ballast and track geometry, as a consequence of the operated trains and their traffic load thus results in maintenance demands that are by no means negligible for keeping track quality at a required standard. A good track quality enables trains to operate at higher speeds and thus increases the capacity (number of operated trains) and track availability. In addition, a well-maintained track improves the safety of railway operations and reduces the risk of accidents. However, an increasing traffic volume leads to higher track possession and shortened track closures for maintenance and consequently fewer time slots available for maintenance. Having said this, it is also clear that more traffic andconsequently higher loads lead to higher maintenance and renewal demands and result in track closures for essential maintenance activities such as tamping or grinding. Therefore, the evaluation and assessment of a railway track is a decisive task for infrastructure managers, and it is becoming even more important to implement a sustainable and condition-based maintenance strategy based on life cycle cost considerations. The evaluation and assessment of a railway track are vital tasks providing essential information to any railway infrastructure manager. Knowledge about track conditions and the ability to distinguish the specific component responsible for the end of service life forms the basis for all sustainable decision making in this context (life cycle management) [1]. Such a strategy can only be effectively implemented when frequent track measurements are carried out since these are the key to data quality and quantity. Either way, for the assessment of railway tracks and rolling stocks, infrastructure managers have access to several data sources providing them with information on the track and rolling stock conditions with varying degrees of data quality and quantity. Condition monitoring and measuring of both the infrastructure and rolling stock can be classified into four groups: (1) vehicle measures infrastructure; (2) vehicle measures vehicle; (3) infrastructure measures infrastructure and (4) infrastructure measures vehicle. Track recording cars (TRCs) are commonly used, primarily for measuring the track geometry of the entire network by almost every infrastructure manager. The measurement data from TRCs are well-known, reliable and state-of-the-art, but are by no means a modern concept. These vehicles do not observe the network continuously, which means the local measurements over a specific distance only take place on a now and then basis. These measurement intervals are adequate of course, for sections deteriorating at a moderate pace, but may be too long for critical sections including isolated track defects (white spots), hollow layers (hanging sleepers), subgrade lowering, (insulated) rail joints or even broken rails. Furthermore, measurements from TRCs are not capable of condition monitoring (CM) for turnouts and bridges in particular. These assets are assessed mainly by visual inspection executed by dedicated experts in cyclic intervals every few weeks. Current developments aim beyond a pure track geometry observation as several additional measuring systems are equipped on vehicles for detection of, e.g., rail and catenary profiles or tunnel clearance measurements [2].