This article proposes a novel fuzzy clustering based anomaly detection method for pump current time series of EDFA systems. The proposed change detection framework (CDF) strategically combines the advantages of entropy analysis (EA) and principle component analysis (PCA) with fuzzy clustering procedures. In the framework, EA is applied for dynamic selection of features for reduction of the feature space and increase of computational performance. Furthermore, PCA is utilized to extract features from the raw feature space to enable generalization capability of the subsequent fuzzy clustering procedures. Three different fuzzy clustering methods, more precisely the fuzzy clustering algorithm, a probabilistic clustering algorithm and a possibilistic clustering algorithm are evaluated for performance and generalization. Hence, the proposed framework has the innovative feature to detect changes in pump current time series at an early stage for arbitrary points of operation, compared to state-of-the-art predefined alarms in commercially used EDFAs. Moreover, the approach is implemented and tested using experimental data. In addition, the proposed framework enables further approaches of applying decentralized predictive maintenance for optical fiber networks.

翻译：本文提出了一种新颖的基于模糊聚类的EDFA系统泵浦电流时间序列异常检测方法。所提出的变化检测框架（CDF）策略性地结合了熵分析（EA）和主成分分析（PCA）与模糊聚类过程的优势。在该框架中，EA被用于动态选择特征，以缩减特征空间并提高计算性能。此外，PCA被用于从原始特征空间中提取特征，以使后续的模糊聚类过程具备泛化能力。本文评估了三种不同的模糊聚类方法，即模糊聚类算法、概率聚类算法和可能性聚类算法的性能与泛化能力。因此，与商用EDFA中最先进的预定义警报相比，所提出的框架具有创新性特征，能够在任意操作点早期检测泵浦电流时间序列的变化。此外，该方法已使用实验数据进行了实现与测试。同时，所提出的框架为实现光纤网络的分布式预测性维护提供了进一步的应用途径。