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. In present optical transmission links, optical fiber amplifiers are key components in long-haul and metro fiber optical networks. Aging of these devices can result in slowly but permanently increasing performance degradation, but also complete outage of the affected link, resulting in cost-intensive maintenance and high financial loss of income.