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

NASA NTRS · Other · 20050196612 · Published 2018-06-04 · Glenn Research Center · 1 author

Abstract and citation only, verbatim from NASA NTRS; full text lives there. All credit to the authors and Glenn Research Center.

Abstract

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 ↗