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

2025-04-28 · Dominic Schneider, Lutz Rapp, Christoph Ament

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.

📄 PDF Abstract BibTeX arXiv:2505.06245

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DecoderTime Series

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