Intelligent Hybrid Machine Learning and Fuzzy Decision System for Predictive Fiber Monitoring

Abstract

Large-scale fiber optic telecommunication networks operate under heterogeneous and uncertain environmental conditions, which makes early fault detection a challenging task. Conventional OTDR-based monitoring approaches are mainly reactive and rely solely on optical signal analysis, providing limited capability to model uncertainty and gradual degradation effects. To address these limitations, this paper proposes an intelligent distributed sensing framework that integrates OTDR-based fiber monitoring with environmental sensor networks using a hybrid fuzzy–machine learning approach. In the proposed framework, optical fibers function as continuous sensing elements, while distributed sensors supply complementary temperature and soil moisture measurements. OTDR and sensor outputs are fused into a unified mixed trace and analyzed in both time and frequency domains. Discriminative features are extracted and reduced using principal component analysis to improve fault repairability. A fuzzy inference system is employed to model uncertainty, vagueness, and nonlinear relationships in the fused mixed trace, enabling robust reasoning under noisy and incomplete data conditions. Supervised and unsupervised machine learning models are combined with fuzzy decision rules to enhance fault classification and early degradation detection. The proposed fuzzy-enhanced framework is validated through real-world deployment on a national-scale telecom network, achieving fault classification accuracy of up to 94.1% and enabling prediction of fiber failures 48–72 h in advance. Compared to conventional OTDR-only monitoring, the proposed approach significantly improves fault detection time, localization accuracy, and decision reliability, demonstrating its effectiveness for intelligent and scalable fiber monitoring.