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.

This paper discusses a proof test procedure for estimating and extending the fatigue life of composite coupons. The estimates were based on the acoustic emission data collected during the described proof test procedure. A group of coupon specimens that included both undamaged as well as damaged ones were tested to verify the ability to estimate the fatigue durability. For majority of the specimens tested the fatigue life of the coupons is inversely proportional to the cumulative AE energy collected during the proof test procedure. Based on the trend that was established, a new group of specimens AE based proof test was performed and using the acoustic emission response, the life was estimated. If one could estimate the fatigue life, it would be possible to identify those specimens, which are likely to fail prematurely. For such specimens it may be possible to extend the fatigue life by appropriate reduction in the cyclic load amplitude. This hypothesis was tested on the last group of specimens. The results obtained during the life extension phase actually show that it is possible to identify the specimens, which are likely to have short life and extend the fatigue life by subjecting them to less demanding load history.

Structural health monitoring of wind turbines is necessitated by the difficulty in manually inspecting a wind turbine to assess safety and consequences of damage. Damage to a blade could dynamically unbalance the turbine, which could destroy the entire machine. Besides the huge financial loss if a turbine fails, safety is a concern for anything near the turbine. Loads on turbines are stochastic and include wind, ice, hail, gravity, tower shadowing, and gyroscopic forces. The complex loading makes it difficult to predict the exact life of the turbine. Also, since the turbines are so large, they are difficult to inspect for imperfections that occur during remote manufacturing and for damage that occurs during operation. Sensor systems for health monitoring of wind turbines must monitor large components including composite blades that can be 150‐ft long, the nacelle, which contains shafts and bearings that are on the scale of ‘shafts and bearings whose diameter is a few feet’ in diameter, a brake, generator, and the tower. The composite blades are particularly challenging to monitor for damage owing to their complex geometry and heterogeneous construction made of different materials such as fiberglass, graphite, and balsa wood. Blades may fail by overstress and fatigue and also by buckling of the cross section near the root of the blade. The sensor system on the blade must operate in a rotating frame and be extremely reliable, ideally lasting for 30 plus years. All these requirements together paint a picture of the unique challenges of health monitoring of wind turbines. This article discusses existing and potential new techniques for health monitoring of wind turbines, and especially blades.

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