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

• URL: http://arxiv.org/abs/2406.09825v2
• Date: Thu, 26 Sep 2024 15:21:31 GMT
• Title: Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
• Authors: Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling, Joachim Denzler,
• Abstract summary: We 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. We illustrate that the anomaly detection methods MDI and DAMP produce complementary results.
• Score: 43.90503061403169
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Precursor-of-Anomaly Detection for Irregular Time Series [31.73234935455713]
  We present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm.
  arXiv  Detail & Related papers  (2023-06-27T14:10:09Z)
• Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series [0.0]
  Among the anomalies detected may be events that are rare, e.g., a planned shutdown of a machine, but are not the actual event of interest. We propose to support this anomaly investigation by providing explanations of anomaly detection. We conduct a behavioral experiment using records of taxi rides in New York City as a testbed.
  arXiv  Detail & Related papers  (2023-02-07T07:27:26Z)
• Prototypical Residual Networks for Anomaly Detection and Localization [80.5730594002466]
  We propose a framework called Prototypical Residual Network (PRN) PRN learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. We present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies.
  arXiv  Detail & Related papers  (2022-12-05T05:03:46Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Deep Learning for Time Series Anomaly Detection: A Survey [53.83593870825628]
  Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning.
  arXiv  Detail & Related papers  (2022-11-09T22:40:22Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Intrinsic Anomaly Detection for Multi-Variate Time Series [33.199682596741276]
  Intrinsic anomalies are changes in the functional dependency structure between time series that represent an environment and time series that represent the internal state of a system that is placed in said environment. These address the short-comings of existing anomaly detection methods that cannot differentiate between expected changes in the system's state and unexpected ones, i.e., changes in the system that deviate from the environment's influence. Our most promising approach is fully unsupervised and combines adversarial learning and time series representation learning, thereby addressing problems such as label sparsity and subjectivity.
  arXiv  Detail & Related papers  (2022-06-29T00:51:44Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment [0.0]
  We propose a method that isolates the root cause of an anomaly by analyzing the patterns in time series fluctuations. The idea is to track the propagation of the effect when a problem causes unaligned but homogeneous shifts of the underlying states. We evaluate our approach by finding the root cause of anomalies in Zillows clickstream data by identifying causal patterns among a set of observed fluctuations.
  arXiv  Detail & Related papers  (2020-01-04T08:31:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.