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Monitoring Multi-Site Damage Growth During Quasi-Static Testing of a Wind Turbine Blade using a Structural Neural System

Center for Quality Engineering and Failure Prevention, Department of Mechanical Engineering, CAT Building, RM 327, 2137 N. Sheridan Road, Evanston, IL 60208, Goutham.Kirikera{at}northwestern.edu

Vishal Shinde

195 Clarksville Rd, Physical Acoustics Corporation, Princeton Jn, New Jersey, 08550

Mark J. Schulz

Smart Structures Bio-Nanotechnology Laboratory (SSBNL), 408B Rhodes Hall, Department of Mechanical Engineering, University of Cincinnati, Cincinnati OH, USA, 45221

Mannur J. Sundaresan

Intelligent Structures and Mechanisms Laboratory, Department of Mechanical Engineering, North Carolina A&T state University, Greensboro, NC 27411, USA

Scott Hughes

National Renewable Energy Laboratory/National Wind Technology Center, 1617 Cole Blvd, Golden, CO 80401, USA

Jeroen van Dam

National Renewable Energy Laboratory/National Wind Technology Center, 1617 Cole Blvd, Golden, CO 80401, USA

Francis Nkrumah

Intelligent Structures and Mechanisms Laboratory, Department of Mechanical Engineering, North Carolina A&T state University, Greensboro, NC 27411, USA

Gangadhar Grandhi

Intelligent Structures and Mechanisms Laboratory, Department of Mechanical Engineering, North Carolina A&T state University, Greensboro, NC 27411, USA

Anindya Ghoshal

United Technology Research Center, 411, Silver Lane, MS 129-73, East Hartford, CT 06108, USA

Structural Health Monitoring (SHM) of a wind turbine blade using a Structural Neural System (SNS) is described in this paper. Wind turbine blades are composite structures with complex geometry and sections that are built of different materials. The 3D structure, large size, anisotropic material properties, and the potential for damage to occur anywhere on the blade makes damage detection a significant challenge. A SNS based on acoustic emission (AE) monitoring (passive listening) was developed for practical low cost SHM of large composite structures such as wind turbine blades. The SNS was tested to detect damage initiation and propagation on a 9 m long wind turbine blade during a quasi-static proof test to failure at the National Renewable Energy Laboratory test facility in Golden, Colorado. Twelve piezoelectric sensors were bonded on the surface of the wind turbine blade and connected to form four continuous sensors which were used in the SNS to determine damage locations. Although 12 sensors monitored the wind turbine blade, the SNS produces only two analog output signals; one time signal to determine and locate damage, and a second time signal containing combined AE waveforms. Testing of the wind turbine blade produced some interesting results. After initial emissions due to settling of the blade diminished, damage initiated at one location on the blade. As the load was increased, damage occurred in a sequence at three other locations until there was a catastrophic buckling failure of the blade. The buckling occurred above the design load for the blade, and was due to the carbon spar cap disbonding from the fiberglass shear web under compressive bending stress. The SNS indicated the general area where the damage started and how the damage progressed, which is valuable information for verifying and improving the blade design and the manufacturing procedure. Strain gages on the blade did not provide a clear indication of damage until buckling occurred. A major outcome of this testing was to provide confidence that SHM of large composite structures that have complex geometry and multiple materials is practical using a simple, low cost SNS.

Key Words: structural neural system (SNS) • continuous sensors • acoustic emission (AE) • wind turbine blade • structural health monitoring (SHM) • passive health monitoring

This version was published on June 1, 2008

Structural Health Monitoring, Vol. 7, No. 2, 157-173 (2008)
DOI: 10.1177/1475921708089746


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