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Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system.
Geometric phase measured per beat period in a ϕ-OTDR based on coherent heterodyne detection is used to measure strain. Proposed method is robust to polarisation mismatch fading as a polarisation mismatch between interfering beams is not a hindrance to the measurement of the geometric phase. The Geometric phase is a function of the intensities of the interfering beams as well as the envelope of the beat signal. Its calculation does not require phase unwrapping and accordingly does not suffer the phase unwrapping errors. It is required to be equated with the traditionally measured phase by applying a scaling factor. The spatial resolution of the measured strain is reduced as it is calculated per beat period. Results are verified using a piezo-electric transducer inline a fiber-under-test.
In this paper, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is presented. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is developed for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and cost-effective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
A distributed acoustic sensor (DAS) based on wavelength-scanning coherent optical time domain reflectometer (WS-COTDR) is tested in an underground gas storage based on a multimode fiber for the first time to the best of our knowledge. The WS-COTDR acquires the reflection spectrum by scanning the wavelength of the laser and uses the spectra to calibrate strain. It overcomes the fading problem which is common for all DAS systems, and possesses the advantage of simple configuration and low-cost. The working principle of the system is well explained and the measurement results are presented. Various artificial and natural acoustic events have been successfully detected by the WS-COTDR. Such a sensor demonstrates several advantages over the traditional DAS systems, so it is supposed to be widely used in the oil and gas industry.