A transformer-based deep learning approach is presented that enables the diagnosis of fault cases in optical fiber amplifiers using condition-based monitoring time series data. The model, Inverse Triple-Aspect Self-Attention Transformer (ITST), uses an encoder-decoder architecture, utilizing three feature extraction paths in the encoder, feature-engineered data for the decoder and a self-attention mechanism. The results show that ITST outperforms state-of-the-art models in terms of classification accuracy, which enables predictive maintenance for optical fiber amplifiers, reducing network downtimes and maintenance costs.
@article{arxiv.2505.06245,
title = {A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers},
author = {Dominic Schneider and Lutz Rapp and Christoph Ament},
journal= {arXiv preprint arXiv:2505.06245},
year = {2025}
}
Comments
This paper has been accepted for publication at the 25th International Conference on Transparent Optical Networks (ICTON) 2025