A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914021300502528 |
|---|---|
| author | Schneider, Dominic Rapp, Lutz Ament, Christoph |
| author_facet | Schneider, Dominic Rapp, Lutz Ament, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers Schneider, Dominic Rapp, Lutz Ament, Christoph Signal Processing Machine Learning 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. |
| title | A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2505.06245 |