Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering

TL;DR

This paper introduces a fuzzy clustering-based anomaly detection framework for EDFA pump current time series, combining entropy analysis and PCA to detect early degeneration signs and improve maintenance strategies.

Contribution

The paper presents a novel change detection framework that integrates entropy analysis, PCA, and fuzzy clustering for early anomaly detection in EDFA pump currents.

Findings

Effective early detection of pump current anomalies.

Improved generalization over state-of-the-art alarms.

Potential for decentralized predictive maintenance.

Abstract

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.