Enhancing Time Series Analysis in Flight Testing With Real-Time Embedded AI

The field of flight tests has traditionally relied on deterministic data, making the presence of on-board AI uncommon. However, as the number of measurement points in flight tests increases, Safran Data Systems (SDS) recognizes the need to address the growing data flow without scaling the entire acquisition chain. SDS has introduced embedded AI algorithms to reduce the load on the acquisition chain by filtering out nominal data, only keeping outliers. While initially used for monitoring electrical networks, this real-time time series analysis has vast potential. It could revolutionize pre-flight checks by enabling thousands of signals to be checked in real-time, accelerating the go/no-go decision process. SDS envisions using AI to unlock new levels of efficiency and data analysis in flight testing. The future of flight tests could involve intelligent systems that swiftly and accurately assess data, providing invaluable insights and expediting the flight testing process.

Institutions

Publication Details

Journal
UA Campus Repository (The University of Arizona)
Published
2024-10-01
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Time Series Analysis in Flight Testing With Real-Time Embedded AI

UA Campus Repository (The University of Arizona)
Magnetic confinement fusion research
article

Enhancing Time Series Analysis in Flight Testing With Real-Time Embedded AI

article en

Abstract

The field of flight tests has traditionally relied on deterministic data, making the presence of on-board AI uncommon. However, as the number of measurement points in flight tests increases, Safran Data Systems (SDS) recognizes the need to address the growing data flow without scaling the entire acquisition chain. SDS has introduced embedded AI algorithms to reduce the load on the acquisition chain by filtering out nominal data, only keeping outliers. While initially used for monitoring electrical networks, this real-time time series analysis has vast potential. It could revolutionize pre-flight checks by enabling thousands of signals to be checked in real-time, accelerating the go/no-go decision process. SDS envisions using AI to unlock new levels of efficiency and data analysis in flight testing. The future of flight tests could involve intelligent systems that swiftly and accurately assess data, providing invaluable insights and expediting the flight testing process.

UA Campus Repository (The University of Arizona)
Safran (France) (FR)
Openalex Percentile: Top 77%
Magnetic confinement fusion research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.