AI-Enabled Predictive Maintenance for Wavelength-Switched Optical Communication Systems
Keywords:
predictive maintenance, wavelength-switched optical networks, artificial intelligence, optical network operations, telemetry, system resilience, governance, sustainabilityAbstract
Wavelength-switched optical communication systems have become central to high-capacity data center interconnection, metro aggregation, and long-haul transport because they allow capacity to be reassigned dynamically across wavelengths, ports, and routes. This flexibility, however, creates a complex failure ecology in which anomalies emerge from interactions among optical amplifiers, wavelength-selective switches, transceivers, fiber plants, synchronization subsystems, and software control planes. Traditional maintenance practices, whether reactive or strictly interval-based, struggle to anticipate such failures without either overprovisioning or exposing services to avoidable outages. This paper examines artificial intelligence enabled predictive maintenance for wavelength-switched optical communication systems from a system-level perspective. It argues that predictive maintenance should not be understood as a narrow anomaly-detection task but as a socio-technical capability that couples optical telemetry, data infrastructure, machine learning, operations governance, and sustainability policy. The paper analyzes architectural requirements for monitoring wavelength-switched fabrics, including the tension between telemetry granularity and network overhead, the role of clock and latency stability, and the need for robust decision support under uncertainty. It further discusses trade-offs involving sensitivity and false alarms, centralized and edge intelligence, vendor autonomy and operator oversight, fairness in maintenance prioritization, and the environmental implications of extending asset lifetimes while increasing computational demand. Drawing on cross-domain comparisons with manufacturing, aviation, and energy infrastructure, the paper identifies deployment, governance, and policy challenges that must be addressed before predictive maintenance can become a trusted layer of optical network operations. The conclusion proposes a research agenda centered on interoperable telemetry, uncertainty-aware models, auditable decision systems, and sustainable lifecycle management.
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