🤖 AI Summary
This study addresses the challenge of fault prediction in optical amplifiers by proposing a lightweight Transformer-based edge intelligence approach that leverages operational monitoring data to achieve high-accuracy remaining useful life estimation. The proposed method introduces, for the first time, a lightweight Transformer architecture into optical network operations and maintenance, enabling low-latency and efficient predictive maintenance. Experimental results demonstrate that the approach significantly enhances the availability and reliability of optical networks, while also validating the practical feasibility and effectiveness of deploying AI-driven models in autonomous optical networks.
📝 Abstract
We enhance optical network availability and reliability through a lightweight transformer model that predicts optical fiber amplifier lifetime from condition-based monitoring data, enabling real-time, edge-level predictive maintenance and advancing deployable AI for autonomous network operation.