Neural network based system for damage identification and location in structural and mechanical systems

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Description

This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Recent advances in wireless, remotely monitored data acquisition systems coupled with the development of vibration-based damage detection algorithms make the possibility of self- or remotely-monitored structures and mechanical systems appear to be within the capabilities of current technology. However, before such a system can be relied upon to perform this monitoring, the variability of the vibration properties that are the basis for the damage detection algorithm must be understood and quantified. This understanding is necessary so that the ... continued below

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9 pages

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Farrar, C.R.; Doebling, S.W.; Prime, M.B.; Cornwell, P.; Kam, M.; Straser, E.G. et al. November 1, 1998.

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Description

This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Recent advances in wireless, remotely monitored data acquisition systems coupled with the development of vibration-based damage detection algorithms make the possibility of self- or remotely-monitored structures and mechanical systems appear to be within the capabilities of current technology. However, before such a system can be relied upon to perform this monitoring, the variability of the vibration properties that are the basis for the damage detection algorithm must be understood and quantified. This understanding is necessary so that the artificial intelligence/expert system that is employed to discriminate when changes in modal properties are indicative of damage will not yield false indications of damage. To this end, this project has focused on developing statistical methods for quantifying variability in identified vibration proper ties of structural and mechanical systems.

Physical Description

9 pages

Notes

OSTI as DE00674673

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  • Other Information: Supercedes report DE99000829; PBD: [1998]

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  • Other: DE99000829
  • Report No.: LA-UR--98-2232
  • Grant Number: W-7405-ENG-36
  • DOI: 10.2172/674673 | External Link
  • Office of Scientific & Technical Information Report Number: 674673
  • Archival Resource Key: ark:/67531/metadc708901

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • November 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • May 5, 2016, 7:38 p.m.

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Farrar, C.R.; Doebling, S.W.; Prime, M.B.; Cornwell, P.; Kam, M.; Straser, E.G. et al. Neural network based system for damage identification and location in structural and mechanical systems, report, November 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc708901/: accessed November 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.