Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering
This work addresses monitoring degeneration in optical fiber networks for predictive maintenance, but it is incremental as it builds on existing fuzzy clustering and analysis methods.
The paper tackles anomaly detection in pump current time series of EDFA systems to monitor degeneration processes, proposing a fuzzy clustering-based change detection framework that combines entropy analysis and PCA, and it demonstrates early-stage change detection compared to state-of-the-art predefined alarms using experimental data.
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.