Self-Healing Fiber Bragg Grating Sensor System Using Free-Space Optics Link and Machine Learning for Enhancing Temperature Measurement

This research investigates the integration of free-space optics (FSO) with fiber Bragg grating (FBG) sensors in self-healing ring architectures, aiming to improve reliability and signal-to-noise ratio in temperature sensing within sensor systems. The combination of FSO's wireless connectivity and FBG sensors' precision, known for their sensitivity and immunity to electromagnetic interference, is particularly advantageous in demanding environments such as aerospace and structural health monitoring. The self-healing architecture enhances system resilience, automatically compensating for failures to maintain consistent monitoring capabilities. This study emphasizes the use of intensity wavelength division multiplexing (IWDM) to manage the complexities of increasing the multiplexing number of FBG sensors. Challenges arise with the overlapping spectra of FBGs when multiplexing several sensors. To address this, a hybrid approach combining an unsupervised autoencoder (AE) with a convolutional neural network (CNN) is proposed, significantly enhancing the accuracy and efficiency of sensor signal detection. These advancements signify substantial progress in sensor technology, validating the effectiveness of the AE-CNN hybrid model in refining FBG sensor systems and underscoring its potential for robust and reliable applications in critical sectors.