Machine learning-Assisted spiral fiber Bragg Grating-Based flexible dual-parameter sensing

Abstract

The ability of flexible sensors to bend or fold freely offers great advantages in sensing ability and adaptability to harsh environments. Moreover, compared with typical electrical effect based flexible sensors, optical based fiber Bragg grating (FBG) flexible sensors offer much greater ease of networking and resistance to electromagnetic interference, making them suitable for distributed multi-point strain measurements in complex environments. In this paper, two FBGs with no overlapping reflective spectra are shallowly embedded in the surface layer of a flexible thin-cylinder substrate to form a dual-parameter flexible strain sensor. However, it is crucial for the changes in direction and curvature parameters of FBG strain sensors under deformation to be accurately understood to characterize the current deformation state of the flexible sensor. Moreover, conventional peak tracking demodulation methods often fail to account for distortion in the reflected spectrum of a spiral FBG under stress. Hence, a multi-output convolutional neural network learning model is constructed to simultaneously identify the bending direction and curvature radius of the flexible sensor using machine learning methods. Experimental results show that the flexible dual-parameter FBG sensor has precisely recognize angles to within 2° across a 360° range, with a curvature recognition accuracy of 99.1%, offering precision sensing performance suitable for highly demanding application scenarios such as bionic robots and flexible medical devices.