Sammendrag
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.
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