  The detection of abnormal or critical system states is essential in condition
monitoring. While much attention is given to promptly identifying anomalies, a
retrospective analysis of these anomalies can significantly enhance our
comprehension of the underlying causes of observed undesired behavior. This
aspect becomes particularly critical when the monitored system is deployed in a
vital environment. In this study, we delve into anomalies within the domain of
Bio-Regenerative Life Support Systems (BLSS) for space exploration and analyze
anomalies found in telemetry data stemming from the EDEN ISS space greenhouse
in Antarctica. We employ time series clustering on anomaly detection results to
categorize various types of anomalies in both uni- and multivariate settings.
We then assess the effectiveness of these methods in identifying systematic
anomalous behavior. Additionally, we illustrate that the anomaly detection
methods MDI and DAMP produce complementary results, as previously indicated by
research.
