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Completed TRL 3 (started at 2, targeting 3)
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.
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.
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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.
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