| Summary: | The EDEN Roadmap at DLR aims at building a Bio-regenerative Life Support System (BLSS) for future space missions within the current decade. To ensure the safe and stable operation of the BLSS, the need for automated system monitoring in general and, in particular, robust anomaly detection is apparent. While the abundance of available methods makes it difficult to choose the most appropriate method for a specific application, each method has its strengths in detecting anomalies of different types. The decision becomes even more difficult if annotated data is not available that could be used for model selection. To address this challenge, we compared six unsupervised anomaly detection methods of varying complexity on the UCR anomaly archive benchmark. The goal was to determine whether more complex methods perform better and if certain methods are better suited to specific anomaly types. To validate our findings in the BLSS domain, we applied the best-performing methods to telemetry data collected from the EDEN ISS research greenhouse, which operated from 2018 - 2021 in Antarctica.
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