Angewandte Physik
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We introduce the optical frequency domain reflectometry (OFDR) technique based on intensity modulation frequency sweep measurement for distributed disturbance measurement in optical fibres. By evaluating interferometric Rayleigh scattering changes along the fibre, strain and temperature changes are detected with 100 nε sensitivity and 10 mK resolution. The vibration frequencies for low frequencies and up to the kHz-range can be obtained from power change Evaluation in the spatial domain. This novel OFDR approach is a low-cost alternative for distributed disturbance measurement up to distances of several kilometres.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.