A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers

Dominic Schneider, Lutz Rapp, Christoph Ament
4/28/2025
eess.SPcs.LG

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Abstract

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.

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Cite this paper

@article{schneider2025transformerbased,
  title  = {A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers},
  author = {Dominic Schneider and Lutz Rapp and Christoph Ament},
  year   = {2025},
  eprint = {2505.06245},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.06245v2}
}

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