Machine Learning-Enabled Fault Detection in High-Speed Fiber-Optic Networks

Abstract

High-speed fiber-optic networks constitute the backbone of contemporary digital society, supporting cloud computing, financial settlement, scientific collaboration, mobile backhaul, and increasingly time-critical industrial applications. As transmission rates rise and optical infrastructure becomes more complex, fault detection can no longer rely solely on threshold alarms, periodic testing, or operator experience. Machine learning offers a pathway toward earlier, more granular, and more context-aware identification of degradation, misconfiguration, and physical-layer anomalies. This paper examines machine learning-enabled fault detection in high-speed fiber-optic networks from a system-level perspective. It analyzes the architectural conditions that shape optical faults, the telemetry and sensing infrastructure required for data-driven monitoring, and the trade-offs among detection accuracy, inference latency, interpretability, deployment cost, and operational trust. The discussion emphasizes that fault detection is not merely a model-selection problem but a socio-technical challenge involving governance, workforce capacity, vendor interoperability, sustainability, and equitable access to resilient infrastructure. Through cross-domain comparisons with cloud anomaly detection, power-grid monitoring, and industrial predictive maintenance, the paper argues that successful deployment depends on aligning machine learning pipelines with optical-layer realities, institutional incentives, and public-interest obligations. It concludes with a forward-looking research agenda focused on robust multimodal sensing, uncertainty-aware diagnosis, human-in-the-loop operations, and policy frameworks that treat network resilience as critical infrastructure.