Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry

TL;DR

This paper presents an unsupervised approach to discover and categorize anomalies in telemetry data from space greenhouse systems, enhancing understanding of system failures in critical environments.

Contribution

It introduces a method combining anomaly detection with time series clustering to analyze anomalies in bio-regenerative life support systems, highlighting the complementary nature of MDI and DAMP detection methods.

Findings

Time series clustering effectively categorizes anomalies.

MDI and DAMP methods produce complementary results.

Analysis improves understanding of anomalies in space life support systems.

Abstract

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.