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

Abstract

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.

The capacity of switchgrass (Panicum virgatum) to degrade atrazine in a phytoremediation setting

“…These measurements were correlated with the onset of visible damage to the blade and grew in intensity as the damage evolved. A similar approach for monitoring damage during static blade tests using a network of acoustic emission sensors was described by Krikera et al 22. A relatively comprehensive review of SHM and non‐destructive testing methods applied to wind turbine blades was also provided by Lading et al .…”

Delamination detection with carbon nanotube thread in self-sensing composite materials

“…CNT materials can be used to design miniature sensors that can sense the same physical parameters as larger conventional sensors, including strain, acoustic waves, and acceleration [33][34][35][36][37]. It is shown for the first time here that distributed sensors based on CNT thread are able to detect initiating damage in composite materials [38].…”

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

“…As early as 1994, Sutherland used the two nondestructive testing techniques of acoustic emission (AE) and coherent optical (CO) to monitor the behavior of a typical wind turbine blade and detect the damage during static experiments [39]. Afterwards, a new structural neural system was invented and developed for the structural health monitoring (SHM) of large blades at the National Renewable Energy Laboratory [40]. This system can also be implemented in a quasi-static proof test to track failures based on the AE monitoring technology.…”