Edge-filter technique and dominant frequency analysis for high-speed railway monitoring with fiber Bragg gratings

Structural health and operation monitoring are of growing interest in the development of railway networks. Conventional systems of infrastructure monitoring already exist (e.g. axle counters, track circuits) but present some drawbacks. Alternative solutions are therefore studied and developed. In this field, optical fiber sensors, and more particularly fiber Bragg grating (FBG) sensors, are particularly relevant due to their immunity to electromagnetic fields and simple wavelength-division-multiplexing capability. Field trials conducted up to now have demonstrated that FBG sensors provide useful information about train composition, positioning, speed, acceleration and weigh-in-motion estimations. Nevertheless, for practical deployment, cost-effectiveness should be ensured, specifically at the interrogator side that has also to be fast (>1 kHz repetition rate), accurate (~1 pm wavelength shift) and reliable. To reach this objective, we propose in this paper to associate a low cost and high-speed interrogator coupled with an adequate signal-processing algorithm to dynamically monitor cascaded wavelength-multiplexed FBGs and to accurately capture the parameters of interest for railway traffic monitoring. This method has been field-tested with a Redondo Optics Inc. interrogator based on the well-known edge-filter demodulation technique. To determine the train speed from the raw data, a dominant frequency analysis has been implemented. Using this original method, we show that we can retrieve the speed of the trains, even when the time history strain signature is strongly affected by the measurement noise. The results are assessed by complimentary data obtained from a spectrometer-based FBG interrogator.