With a focus on service interruptions occurring in optical networks, we propose a failure prognostics scheme based on a bi-directional gated recurrent unit (BiGRU) from the perspective of time-series processing, which leverages actual datasets from the network operator. BiGRU neural networks can capture the temporal features of multi-sourced data and incorporate contextual information. A principal component analysis is introduced to reduce the data dimensionality. Experimental results show that the average accuracy of the prognostics, F1 score, false positive rate, and false negative rate of our method are 99.61%, 99.63%, 0.29%, and 0.84%, respectively, which proves the feasibility of the proposed scheme for failure prognostics of equipment used in optical networks.

中文翻译：

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使用BiGRU进行光网络的时间数据驱动的故障预测

考虑到光网络中发生的服务中断，我们从时间序列处理的角度提出了一种基于双向门控循环单元（BiGRU）的故障预测方案，该方案利用了网络运营商的实际数据集。BiGRU神经网络可以捕获多源数据的时间特征并合并上下文信息。引入主成分分析以减少数据维数。实验结果表明，所提方法的预测准确率，F1评分，假阳性率和假阴性率的平均准确率分别为99.61％，99.63％，0.29％和0.84％，证明了该方案的可行性。光网络中使用的设备的故障预测。