Fiber bragg grating-based multi-dimensional sensing and their applications using multi-core fibers

Abstract

The intrinsic characteristics of multi-core fibers (MCFs) comprising of several cores in a single cladding make them helpful in various applications due to spatial multiplexing capabilities, whilst making them promising candidates for the design of multi-dimensional sensors in fiber optic sensing. As a result of the development of multi-core fiber fan-out devices, it is possible to monitor individual cores inside the MCF separately. Therefore, fiber Bragg grating (FBG) based MCF is of great value which can be utilized as a two-dimensional or three-dimensional sensor, depending on the choice of a single set of gratings or an array of gratings inscribed in the fiber. This thesis focuses on inscribing FBGs in MCFs and developing them as multi-dimensional sensors suitable for various applications, such as vibration detection, inclination measurement, and displacement monitoring. A novel orientation-sensitive two-dimensional accelerometer based on FBGs inscribed in a silica seven-core MCF was designed. Performance of the proposed accelerometer in terms of frequency, acceleration and vibration orientation were experimentally investigated. The designed two-dimensional accelerometer is capable of obtaining the vibration frequency, acceleration and orientation, simultaneously. A sensitivity which is strongly dependent on the orientation is achieved, with a best orientation accuracy of 0.127° over a range of 0-180°. In order to verify the stability of the performance, different sets of chosen outer cores were utilized to retrieve the orientation. An all-fiber two-dimensional inclinometer was proposed under the FBG-based MCF structure, with the capability of measuring the azimuthal angle and the inclination angle, simultaneously. The sensor performance was theoretically optimized and experimentally investigated. Excellent agreement between simulated and experimental results was achieved, with sensitivities of 3.42 and 3.41 pm/° for azimuthal and inclination angles, respectively. Through detection of the wavelength shifts of the FBGs inscribed in the central core and two outer cores of a silica seven-core MCF, a minimum error of 0.0056° for the azimuthal angle, and 0.025° for the inclination angle, were obtained. The detection range of the former ranges from 0 to 360°, while the latter ranges from 0 to 90°. This thesis further elaborates on the development of a two-dimensional vector displacement sensor with the capability of distinguishing the direction and amplitude of the displacement simultaneously, while its performance was enhanced by machine learning algorithms. It was designed with a displacement direction range of 0-360°, and the amplitude range related to the length of the sensor body. The displacement information was obtained under a random circumstance, where the performance was investigated under the comparison of a theoretical model as well as a machine learning model. The maximum positive sensitivities are obtained as 11.47, 12.31, and 11.73 pm/mm. The validity of the theoretical model is limited to a linear range (from 0 to 9mm) whereas the sensor enhanced by machine learning model outperformed in two aspects, an enlarged measurement range (from 0 to 45mm) and a reduced measurement error of displacement. Mean absolute errors of direction and amplitude reconstruction were decreased by 60% and 98%, respectively with the help of the machine learning algorithm.