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

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.