NASA · NASA-854 · record R0458

Application of ML to Detection of Anomalies in Spacecraft Health and Status Data

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2025-10-01 (day precision)Source: 10/01/2025 00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

We are collaborating with the Magnetospheric Multiscale (MMS) mission to research Machine Learning (ML) techniques capable of predicting and detecting anomalies in spacecraft health and status data. We are combining historical MMS telemetry data, with known mission events and anomalies, to perform unsupervised ML techniques. We have primarily used Temporal Convolutional Networks (TCN) as they preserve the temporal nature of our data while detecting long term and short term trends in the data.

Expected benefits

This research provides new insights in telemetry data patterns and may reduce the time it takes to identify anomalies, allowing operators to focus on finding resolutions to ensure spacecraft health and safety.

System outputs

Detection of anomalies

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): NA - local machine
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
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: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: MMS and LRO telemetry data from GSFC's Telemetry as a Service (TaaS) tool
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported