SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

Z. Sharif-Khodaei; M. Ghajari; Ferri M.H.Aliabadi; A. Apicella

doi:10.4028/www.scientific.net/KEM.525-526.581

Paper Titles

Impact Identification in Composite Stiffened Panels
p.565

Electro-Mechanical Impedance Technique for Structural Health Monitoring of Composite Panels
p.569

Dynamic Response Analysis of Lining Structure with Primary Defects for Tunnel under Moving Train Loading
p.573

Monitoring Analysis of Tunnel Construction in a New Subway Line Down-Through an Existed Line
p.577

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels
p.581

Micromechanical Characterisation of Microstructure in Weld Heat Affected Zone of Structural Steel
p.585

Two Scale-Based Continuum Damage Model for Brittle Materials under Thermomechanical Loading
p.589

The Applicability Study on the FRP-Concrete Composite Bridge Deck for Cable-Stayed Bridges
p.593

Improvement of Fatigue Crack Growth Behavior in the Case of the Cracked Specimen with Relatively Narrow Width
p.597

HomeKey Engineering MaterialsKey Engineering Materials Vols. 525-526SMART Platform for Structural Health Monitoring of...

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

Abstract:

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.

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Periodical:

Key Engineering Materials (Volumes 525-526)

Pages:

581-584

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.525-526.581

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Online since:

November 2012

Authors:

Z. Sharif-Khodaei, M. Ghajari, Ferri M.H.Aliabadi, A. Apicella

Keywords:

Artificial Neural Network (ANN), Damage Detection, Electromechanical Impedance, Impact Detection, Structural Health Monitoring (SHM), Ultrasonic Guided Waves

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References

[1] Acellent Technologies Inc., SMART LAYER, (2011).

Google Scholar

[2] AERnnova Engineering, PAMELA SHM system, (2010).

Google Scholar

[3] The Boeing Company, Composites with integrated multi-functional circuits, US Patent, (2011).

Google Scholar

[4] M. Ghajari, Z. Sharif Khodaei, and M. Aliabadi, Impact Detection Using Artificial Neural Networks, Key Engineering Materials, vol. 488, pp.767-770, (2012).

DOI: 10.4028/www.scientific.net/kem.488-489.767

Google Scholar

[5] Z. Sharif Khodaei and M. H. Aliabadi, Damage Identification Using Lamb Waves, Key Engineering Materials, vol. 452, pp.29-32, (2011).

DOI: 10.4028/www.scientific.net/kem.452-453.29

Google Scholar

[6] Z. Sharif Khodaei, R. Rojas-Diaz, and M. Aliabadi, Lamb-Wave Based Technique for Impact Damage Detection in Composite Stiffened Panels, Key Engineering Materials, vol. 488, pp.5-8, (2012).

DOI: 10.4028/www.scientific.net/kem.488-489.5

Google Scholar

[7] M. H. Aliabadi, Final Project Report - SMASH, (2012).

Google Scholar