Home // ICAS 2022, The Eighteenth International Conference on Autonomic and Autonomous Systems // View article

Enhancing Autonomous Systems’ Awareness: Conceptual Categorization of Anomalies by Temporal Change During Real-Time Operations

Authors:
Rialda Spahic
Vidar Hepsø
Mary Ann Lundteigen

Keywords: anomalies, anomalous change detection, anomaly detection, time-series analysis, autonomous systems

Abstract:
The Unmanned Autonomous Systems (UAS) are anticipated to have a permanent role in offshore operations, enhancing personnel, environmental, and asset safety. These systems can alert onshore operators of hazardous occurrences in the environment, in the form of anomalies in data, during real-time inspections, enabling early prevention of hazardous events. Time series data, collected by sensors that detect environmental phenomena, enables the observation of anomalous data as dynamic instances of the dataset. Recent research characterizes anomalies in terms of their patterns of occurrence in data. However, there is insufficient research on anomalous temporal change patterns. In this paper, we examine anomalies in relation to one another and propose a conceptual categorization system for anomalies based on their temporal changes. We demonstrate the categorization through a case study of potentially hazardous occurrences observed by UAS during underwater pipeline inspection. Analyzing anomalies based on their behavior can provide further information about current environmental changes and enable the early discovery of unwanted events, simultaneously minimizing false alarms that overwhelm the systems with low-significance information in real-time.

Pages: 25 to 30

Copyright: Copyright (c) IARIA, 2022

Publication date: May 22, 2022

Published in: conference

ISSN: 2308-3913

ISBN: 978-1-61208-966-9

Location: Venice, Italy

Dates: from May 22, 2022 to May 26, 2022