Abstract
The development of accurate, robust and adaptive methods for monitoring the optical parameters of fiber-optic sensors (FOS) is one of the priority tasks in the field of precision measurements, especially in the context of rapidly growing requirements for intelligent monitoring systems. This paper presents a comprehensive approach to the use of modern deep learning algorithms for analyzing and processing spectral data coming from FOS. The proposed solution is based on the use of convolutional neural networks (CNN) for the automatic extraction of informative features, as well as autoencoders for noise suppression and signal restoration. A hybrid architecture combining CNN and recurrent neural networks (RNN) was developed. The experiments conducted confirmed the effectiveness of the evaluated models. On the independent regression test set, the CNN-only model achieved a macro-averaged R2-based prediction score of 98.2% without added noise and 89.4% under high-noise conditions; on a separate temporal test sequence, the hybrid CNN + RNN model achieved 95.0% compared with 88.0% for CNN alone. The presented approach has high resistance to noise and the ability to scale to various types of FOS. At the conclusion, the prospects for the practical applications of the proposed system are discussed: the structural monitoring of buildings and structures and the automation of processes in industry and energy, with an emphasis on reliability, autonomy and integration with existing platforms.