NASA · NASA-905 · record R0501

Bump in the Wire (BITW)

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

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

Problem the AI is intended to solve

BITW is a software technology designed to detect anomalous commands and trends in telemetry beyond traditional detection methods such as command parsers and telemetry limit checkers. It provides an extensible architecture that enables a pipeline of detection plugins based on Machine Learning (ML) models and signature-based algorithms

Expected benefits

Intended benefit for this technology is to improve upon the detection of anomalous space system commands and telemetry beyond traditional command parsers and telemetry limit checkers, and to improve the time to detect anomalies by embedding the detection software on the edge computing platform such as a spacecraft.

System outputs

Expected output is identification of commands anomalies in command data and telemetry values

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.

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