Abstract
Currently, there are many different methods of monitoring extended facilities. However, the most accurate, efficient, and more accessible methods are using fiber-optic sensors. This study examines existing methods based on the application of optical time-domain reflectometry (OTDR). Data from three main databases, namely Web of Science, Scopus, and Google Scholar, were considered as existing solutions. Among the existing types, the possibility of using interferometers was also taken into account. However, such systems are expensive and very sensitive. At the same time, OTDR systems have huge disadvantages, such as the relatively low sensitivity of such systems, the closeness of the solution, and the lack of integration. However, all the disadvantages, except for the proprietarity, can be eliminated by using a neural network. Therefore, a system based on an open architecture is proposed with the possibility of application on new and already installed monitoring systems using a neural network for classification and an expert system for assessing the situation and recommendations for the implementation of restoration work. A universal intelligent hardware–software complex is proposed, which includes modules for signal preprocessing based on Fourier transform, statistical filtering using the three-sigma method, event classification, and interpretation. The suggested developed system enables noise suppression, event recognition (vibration, bending, cable breakage), and generation of recommendations through artificial intelligence. A convolutional neural network was used as a neural network for event classification. Recommendations and evaluation were provided using an expert evaluation module based on the use of Copilot, which reduces decision-making time and prevents possible breakdowns.