Integrated structural health monitoring.

Conference · Mon Jan 01 04:00:00 EST 2001

DOI:https://doi.org/10.1117/12.434158· OSTI ID:975306

Farrar, C R ^[1]

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1. Charles R.

Structural health monitoring 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. The goal of the research effort reported herein is to develop a robust and cost-effective structural health monitoring solution by integrating and extending technologies from various engineering and information technology disciplines. It is the authors opinion that all structural health monitoring systems must be application specific. Therefore, a specific application, monitoring welded moment resisting steel frame connections in structures subjected to seismic excitation, is described along with the motivation for choosing this application. The structural health monitoring solution for this application will integrate structural dynamics, wireless data acquisition, local actuation, micro-electromechanical systems (MEMS) technology, and statistical pattern recognition algorithms. The proposed system is based on an assessment of the deficiencies associated with many current structural health monitoring technologies including past efforts by the authors. This paper provides an example of the integrated approach to structural health monitoring being undertaken at Los Alamos National Laboratory and summarizes progress to date on various aspects of the technology development.

• Research Organization: Los Alamos National Laboratory
• Sponsoring Organization: DOE
• OSTI ID: 975306
• Report Number(s): LA-UR-01-2283
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
ALGORITHMS
BEARINGS
DATA ACQUISITION
DETECTION
EXCITATION
IMPLEMENTATION
LANL
MACHINERY
MECHANICAL ENGINEERING
MONITORING
PATTERN RECOGNITION
STEELS
TIME-SERIES ANALYSIS