OMB Individually Reported
Unsupervised anomaly detection in flight data with deep variational autoencoders
Low riskExact public inventory row
Description
This model is an unsupervised deep learning based anomaly detection for aircraft flight data based on variational autoencoders with convolutional architecture. The model is designed to find anomalies in multivaraite time-series and can work with heterogeneous data.
Detailed example
anomaly detection
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
It is currently tested and validated in finding anomaly detection in flight's operational quality assurance data from commerical aircraft.
Controls / human review
ATO: Not reported; PIA: Not published