← Back to NASA Technology Projects

Development of Machine Learning Protocols Towards the Non-destructive Evaluation of Polymeric Cable Insulations Through Colorimetry

Completed TRL 3 (started at 2, targeting 3)

Description

This project will train a machine learning algorithm on colorimetric and dielectric data from aged polymeric electrical insulation materials to diagnose insulation health. The trained machine learning algorithm deliverable will reduce the labor of insulation health monitoring and diagnose the locations of expected insulation failure for more efficient preventative maintenance.

Benefits

A machine learning algorithm that leverages colorimetry to correlate polymer color change with dielectric property loss will provide predictive health diagnostics of electrical insulation and enable highly reliable power distribution systems in harsh, unserviceable environments.

Details

Technology areaMaterials, Structures, Mechanical Systems, and Manufacturing > Structures > Reliability and Sustainment
ProgramCenter Innovation Fund: GRC CIF (GRC CIF)
Lead organizationGlenn Research Center, Cleveland, OH
Start date2022-10-01
End date2023-09-30

Project contacts

Listed on TechPort itself — the most direct way to ask about this specific project.

How to get involved

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.