Unsupervised anomaly detection in flight data with deep variational autoencoders

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Transportation

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

It is currently tested and validated in finding anomaly detection in flight's operational quality assurance data from commerical aircraft.

System outputs

anomaly detection

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported