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Transformer-Based Prognostics: Enhancing Network Availability by Improved Monitoring of Optical Fiber Amplifiers

Dominic Schneider, Lutz Rapp, Christoph Ament

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.

Transformer-Based Prognostics: Enhancing Network Availability by Improved Monitoring of Optical Fiber Amplifiers

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.

Paper Structure

This paper contains 4 sections, 2 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology and Experimental Setup
  3. Results
  4. Conclusion

Figures (3)

  • Figure 1: Proposed transformer architecture SLAT for predicting the RUL of OFAs, derived from multivariate time series data using sliding time window technique.
  • Figure 2: Data acquisition setup, illustrating the collection of multivariate time series data from OFAs, with induced degradation scenarios in the shown EDFA architecture.
  • Figure 3: RTF trajectories for representative test samples of each degradation scenario: (a) pump laser, (b) power detector, (c) variable optical attenuator, and (d) passive components.