Paper
9 March 2014 Experiments on a wind turbine blade testing an indication for damage using the causal and anti-causal Green's function reconstructed from a diffuse field
Jeffery D. Tippmann, Francesco Lanza di Scalea
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Abstract
The increasing demand for renewable and clean power generation has resulted in increasing sizes of rotor blades in wind turbine systems. The demanding and variable operational environments have introduced the need for structural health monitoring systems in the blades in order to prevent unexpected downtime events in the operation of the power plant. Many non-destructive evaluation methods used for structural health monitoring purposes need external excitation sources. However, several systems already accepted in the wind turbine industry are passive. Here we present a new approach to health monitoring of a wind turbine blade using only passive sensors and the existing noise created on the blade during operation. This is achieved using a known method to reconstruct the causal and anticausal time-domain Green’s function between any two points in an array of passive sensors placed in a diffuse field. Damage is indicated when the similarity between the causal and anticausal signals decrease due to nonlinearities introduced from structural damage. This method was studied experimentally using a CX-100 wind turbine test blade located at the UCSD’s Powell Structural Laboratories where a diffuse field was approximated by exciting the skin of the blade with a random signal at several locations.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffery D. Tippmann and Francesco Lanza di Scalea "Experiments on a wind turbine blade testing an indication for damage using the causal and anti-causal Green's function reconstructed from a diffuse field", Proc. SPIE 9064, Health Monitoring of Structural and Biological Systems 2014, 90641I (9 March 2014); https://doi.org/10.1117/12.2046417
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Wind turbine technology

Composites

Structural health monitoring

Correlation function

Damage detection

Interference (communication)

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