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

TL;DR

This paper introduces ITST, a transformer-based deep learning model that improves fault diagnosis accuracy in optical fiber amplifiers, enabling predictive maintenance and reducing network downtime.

Contribution

The paper presents a novel transformer-based model with an encoder-decoder architecture for fault diagnosis in optical fiber amplifiers, outperforming existing models.

Findings

ITST achieves higher classification accuracy than state-of-the-art models.

The model enables effective predictive maintenance for optical fiber networks.

ITST reduces network downtime and maintenance costs.

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.