The demand for high-speed data is exponentially growing. To conquer this,
optical networks underwent significant changes getting more complex and
versatile. The increasing complexity necessitates the fault management to be
more adaptive to enhance network assurance. In this paper, we experimentally
compare the performance of soft-failure management of different machine
learning algorithms. We further introduce a machine-learning based soft-failure
management framework. It utilizes a variational autoencoder based generative
adversarial network (VAE-GAN) running on optical spectral data obtained by
optical spectrum analyzers. The framework is able to reliably run on a fraction
of available training data as well as identifying unknown failure types. The
investigations show, that the VAE-GAN outperforms the other machine learning
algorithms when up to 10\% of the total training data is available in
identification tasks. Furthermore, the advanced training mechanism for the GAN
shows a high F1-score for unknown spectrum identification. The failure
localization comparison shows the advantage of a low complexity neural network
in combination with a VAE over established machine learning algorithms.

我们通过实验比较了不同机器学习算法在软故障管理方面的性能，并引入了一种基于机器学习的软故障管理框架。该框架利用光谱分析仪获取的光谱数据，并运行在变分自动编码器生成对抗网络（VAE-GAN）上。研究结果表明，VAE-GAN 在识别任务中，当可用的训练数据仅为总量的 10％时，表现优于其他机器学习算法。此外，该框架通过先进的训练机制，在未知频谱识别方面显示出很高的 F1 分数。故障定位比较还表明，低复杂度神经网络与 VAE 结合相比于已有的机器学习算法具有优势。