用Transformer诊断光纤放大器故障，提升预测维护精度。
A Transformer-Based Approach for Diagnosing Fault Cases in Optical Fiber Amplifiers
- 设计三路特征提取的编码器-解码器结构，融合自注意力机制
- 分类准确率优于现有模型，支持故障早期识别
- 适合通信网络运维人员用于降低宕机和维护成本
提出一种基于Transformer的深度学习方法，利用状态监测时序数据诊断光纤放大器故障。模型采用编码器-解码器架构，编码器包含三条特征提取路径，解码器使用特征工程数据与自注意力机制。实验表明，该模型在分类准确率上优于当前最优方法，可实现对光纤放大器的预测性维护，有效减少网络中断时间和运维成本。
原文摘要 · Abstract (English)
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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