Anomaly Detection and Precursor Identification in UAV flight data
Description
This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown anomalies and precursors to known issues in UAV (Unmanned Aerial Vehicle) test flights.
Detailed example
previously-unknown anomalies and precursors to known issues in UAV (Unmanned Aerial Vehicle) test flights.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown anomalies and precursors to known issues in UAV (Unmanned Aerial Vehicle) test flights.
Controls / human review
ATO: Not reported; PIA: Not published