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.

通过对生物再生生命保障系统（BLSS）进行异常检测，对南极 EDEN ISS 太空温室的遥测数据进行分析，采用时间序列聚类方法对单变量和多变量设置下的各种异常进行分类，评估这些方法在识别系统异常行为方面的有效性，并证明 MDI 和 DAMP 异常检测方法产生互补的结果。