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

• URL: http://arxiv.org/abs/2505.06245v2
• Date: Mon, 23 Jun 2025 06:06:01 GMT
• Title: A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers
• Authors: Dominic Schneider, Lutz Rapp, Christoph Ament,
• Abstract summary: 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.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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