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Title: DEVELOPMENT OF AN INTEGRATED SOFTWARE SOLUTION FOR PIEZOELECTRIC ACTIVE-SENSING IN STRUCTURAL HEALTH MONITORING

Authors:
 [1];  [1];  [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1237264
Report Number(s):
LA-UR-07-1597
DOE Contract Number:
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: SPIE INTERNATIONAL SYMPOSIUM ON SMART STRUCTURES & NONDESTRUCTIVE EVALUATION ; 200703 ; SAN DIEGO
Country of Publication:
United States
Language:
English

Citation Formats

JACOBS, LAURA D., PARK, GYUHAE, and FARRAR, CHARLES R. DEVELOPMENT OF AN INTEGRATED SOFTWARE SOLUTION FOR PIEZOELECTRIC ACTIVE-SENSING IN STRUCTURAL HEALTH MONITORING. United States: N. p., 2007. Web.
JACOBS, LAURA D., PARK, GYUHAE, & FARRAR, CHARLES R. DEVELOPMENT OF AN INTEGRATED SOFTWARE SOLUTION FOR PIEZOELECTRIC ACTIVE-SENSING IN STRUCTURAL HEALTH MONITORING. United States.
JACOBS, LAURA D., PARK, GYUHAE, and FARRAR, CHARLES R. Fri . "DEVELOPMENT OF AN INTEGRATED SOFTWARE SOLUTION FOR PIEZOELECTRIC ACTIVE-SENSING IN STRUCTURAL HEALTH MONITORING". United States. doi:. https://www.osti.gov/servlets/purl/1237264.
@article{osti_1237264,
title = {DEVELOPMENT OF AN INTEGRATED SOFTWARE SOLUTION FOR PIEZOELECTRIC ACTIVE-SENSING IN STRUCTURAL HEALTH MONITORING},
author = {JACOBS, LAURA D. and PARK, GYUHAE and FARRAR, CHARLES R.},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 09 00:00:00 EST 2007},
month = {Fri Mar 09 00:00:00 EST 2007}
}

Conference:
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  • 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
  • This paper is a report of an initial investigation into tracking and monitoring the integrity of bolted joints using piezoelectric active-sensors. The target application of this study is a fitting lug assembly of unmanned aerial vehicles (UAVs), where a composite wing is mounted to a UAV fuselage. The SHM methods deployed in this study are impedance-based SHM techniques, time-series analysis, and high-frequency response functions measured by piezoelectric active-sensors. Different types of simulated damage are introduced into the structure, and the capability of each technique is examined and compared. Additional considerations encountered in this initial investigation are made to guide furthermore » thorough research required for the successful field deployment of this technology.« less
  • This paper gives a brief overview of a new project at LANL in structural damage identification for wind turbines. This project makes use of modeling capabilities and sensing technology to understand realistic blade loading on large turbine blades, with the goal of developing the technology needed to automatically detect early damage. Several structural health monitoring (SHM) techniques using piezoelectric active materials are being investigated for the development of wireless, low power sensors that interrogate sections of the wind turbine blade using Lamb wave propagation data, frequency response functions (FRFs), and time-series analysis methods. The modeling and sensor research will bemore » compared with extensive experimental testing, including wind tunnel experiments, load and fatigue tests, and ultrasonic scans - on small- to mid-scale turbine blades. Furthermore, this study will investigate the effect of local damage on the global response of the blade by monitoring low-frequency response changes.« less