Consideration of environmental and operational variability for damage diagnosis [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2002.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- 12 pages : digital, PDF file
- Additional Creators:
- Los Alamos National Laboratory
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- Damage diagnosis is a problem that can be addressed at many levels. Stated in its most basic form, the objective is to ascertain simply if damage is present or not. In a statistical pattern recognition paradigm of this problem, the philosophy is to collect baseline signatures from a system to be monitored and to compare subsequent data to see if the new 'pattern' deviates significantly from the baseline data. Unfortunately, matters are seldom as simple as this. In reality, structures will be subjected to changing environmental and operational conditions that will affect measured signals. In this case, there may be a wide range of normal conditions, and it is clearly undesirable to signal damage simply because of a change in the environment. In this paper, a unique combination of time series analysis, neural networks, and statistical inference techniques is developed for damage classification explicitly taking into account these natural variations of the system in order to minimize false positive indication of true system changes.
- Published through SciTech Connect.
Submitted to; Smart Structures and Materials 2002: Smart Systems for Bridges, Structures, and Highways, June 2002, San Diego, CA; Final version published in: Proceedings of SPIE - The International Society for Optical Engineering ; 2002; v.4696, p.100-111.
Farrar, C. R.; Sohn, H.; Worden, K.
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