A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers
This addresses fault diagnosis for optical fiber amplifier maintenance, but it is incremental as it applies a transformer variant to a specific domain.
The paper tackles fault diagnosis in optical fiber amplifiers using a transformer-based deep learning model called ITST, which achieves higher classification accuracy than state-of-the-art models, enabling predictive maintenance to reduce downtimes and costs.
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.