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NASA NTRS · Other

Training Data Optimized and Conditioned to Learn Characteristic Patterns of Vibrating Blisks and Fan Blades

Published 2018-06-04 From Glenn Research Center 1 author

Attribution

This is the abstract and citation. Full text lives at NASA NTRS — we link out rather than host. All credit to the authors and Glenn Research Center.

Abstract

Verbatim from NASA NTRS. Not paraphrased, not summarized.

At the NASA Glenn Research Center, we have been training artificial neural networks to interpret the characteristic patterns (see the leftmost image) generated from electronic holograms of vibrating structures. These patterns not only visualize the vibration properties of structures, but small changes in the patterns can indicate structural changes, cracking, or damage. Neural networks detect these small changes well. Our objective has been to adapt the neural-network, electronic-holography combination for inspecting components in Glenn's Spin Rig.

Author

  • Decker, Arthur J. NASA Glenn Research Center

Citation: Decker, Arthur J. (2018). Training Data Optimized and Conditioned to Learn Characteristic Patterns of Vibrating Blisks and Fan Blades. Glenn Research Center. NASA NTRS ID 20050196612. https://ntrs.nasa.gov/citations/20050196612 ↗