DATA NORMALIZATION : A KEY FOR STRUCTURAL HEALTH MONITORING
- Charles R.
- Hoon
Structural health monitoring (SHM) is the implementation of a damage detection strategy for aerospace, civil and mechanical engineering infrastructure. Typical damage experienced by this infrastructure might be the development of fatigue cracks, degradation of structural connections, or bearing wear in rotating machinery. For SHM strategies that rely on vibration response measurements, the ability to normalize the measured data with respect to varying operational and environmental conditions is essential if one is to avoid false-positive indications of damage. Examples of common normalization procedure include normalizing the response measurements by the measured inputs as is commonly done when extracting modal parameters. When environmental cycles influence the measured data, a temporal normalization scheme may be employed. This paper will summarize various strategies for performing this data normalization task. These strategies fall into two general classes: (1) Those employed when measures of the varying environmental and operational parameters are available; (2) Those employed when such measures are not available. Whenever data normalization is performed, one runs the risk that the damage sensitive features to be extracted from the data will be obscured by the data normalization procedure. This paper will summarize several normalization procedures that have been employed by the authors and issues that have arose when trying to implement them on experimental and numerical data.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 975664
- Report Number(s):
- LA-UR-01-4212; TRN: US201008%%251
- Resource Relation:
- Conference: "Submitted to: 3rd Int'l workshop/structural health monitoring, Stanford University, Sanford, CA, September 17-19, 2001."
- Country of Publication:
- United States
- Language:
- English
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