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

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{schneider2025_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 }
}