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Title: DATA NORMALIZATION: A KEY FOR STRUCTURAL HEALTH MONITORING

Abstract

No abstract prepared.

Authors:
; ;
Publication Date:
Research Org.:
Los Alamos National Lab., NM (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
783793
Report Number(s):
LA-UR-01-4212
TRN: AH200137%%119
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Jul 2001
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; MONITORING; LANL; RISK ASSESSMENT

Citation Formats

C. R. FARRAR, H. SOHN, and K. WORDEN. DATA NORMALIZATION: A KEY FOR STRUCTURAL HEALTH MONITORING. United States: N. p., 2001. Web.
C. R. FARRAR, H. SOHN, & K. WORDEN. DATA NORMALIZATION: A KEY FOR STRUCTURAL HEALTH MONITORING. United States.
C. R. FARRAR, H. SOHN, and K. WORDEN. Sun . "DATA NORMALIZATION: A KEY FOR STRUCTURAL HEALTH MONITORING". United States. doi:. https://www.osti.gov/servlets/purl/783793.
@article{osti_783793,
title = {DATA NORMALIZATION: A KEY FOR STRUCTURAL HEALTH MONITORING},
author = {C. R. FARRAR and H. SOHN and K. WORDEN},
abstractNote = {No abstract prepared.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jul 01 00:00:00 EDT 2001},
month = {Sun Jul 01 00:00:00 EDT 2001}
}

Conference:
Other availability
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  • 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. Whenmore » 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.« less
  • No abstract prepared.
  • Issues surrounding the use of ambient vibration modes for the location of structural damage via dynamically measured flexibility are examined. Several methods for obtaining the required mass- normalized dynamic mode shapes from ambient modal data are implemented and compared. The method are applied to data from a series of ambient modal tests on an actual highway bridge. Results indicate that for the damage case examined, the flexibility from the ambient mode shapes gave a better indication of damage than the flexibility from the forced-vibration mode shapes. This improved performance is attributed to the higher excitation load levels that occur duringmore » the ambient test.« less
  • The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors approach is to address the SIAM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discussmore » each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring (DIAMOND 11) with a modular wireless sensing and processing platform that is being jointly developed with Motorola Labs. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.« less
  • Abstract not provided.