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.

翻译：异常或关键系统状态的检测在状态监测中至关重要。尽管及时识别异常受到广泛关注，但对这些异常的回顾性分析能显著增强我们对观测到的不良行为根本原因的理解。当监测系统部署在关键环境中时，这一方面变得尤为重要。在本研究中，我们深入探究了面向太空探索的生物再生生命支持系统（BLSS）领域的异常，并分析了源自南极洲EDEN ISS太空温室遥测数据中发现的异常。我们采用时间序列聚类方法对异常检测结果进行处理，以在单变量和多变量设置中对各类异常进行分类。随后，我们评估了这些方法在识别系统性异常行为方面的有效性。此外，我们阐明了异常检测方法MDI和DAMP能产生互补的结果，正如先前研究所示。