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.

翻译：高速数据需求正呈指数级增长。为应对这一挑战，光网络经历了重大变革，变得愈发复杂和多功能化。日益增长的复杂性要求故障管理更具自适应性，以增强网络保障能力。本文实验比较了不同机器学习算法在软故障管理中的性能，并进一步提出了一种基于机器学习的软故障管理框架。该框架利用基于变分自编码器的生成对抗网络（VAE-GAN），处理由光谱分析仪获取的光谱数据。该框架不仅能在少量训练数据上可靠运行，还能识别未知故障类型。研究表明，在识别任务中仅使用总训练数据的10%时，VAE-GAN的性能优于其他机器学习算法。此外，针对GAN的高级训练机制在未知光谱识别中表现出高F1分数。故障定位比较显示，将低复杂度神经网络与VAE结合使用，相较于现有机器学习算法具有显著优势。