Multiple damage identification on a wind turbine blade using a structural neural system

Goutham R. Kirikera; Mark J. Schulz; Mannur J. Sundaresan

doi:10.1117/12.716071

10 April 2007 Multiple damage identification on a wind turbine blade using a structural neural system

Goutham R. Kirikera, Mark J. Schulz, Mannur J. Sundaresan

Proceedings Volume 6530, Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring 2007; 65300T (2007) https://doi.org/10.1117/12.716071
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2007, San Diego, California, United States

Abstract

A large number of sensors are required to perform real-time structural health monitoring (SHM) to detect acoustic emissions (AE) produced by damage growth on large complicated structures. This requires a large number of high sampling rate data acquisition channels to analyze high frequency signals. To overcome the cost and complexity of having such a large data acquisition system, a structural neural system (SNS) was developed. The SNS reduces the required number of data acquisition channels and predicts the location of damage within a sensor grid. The sensor grid uses interconnected sensor nodes to form continuous sensors. The combination of continuous sensors and the biomimetic parallel processing of the SNS tremendously reduce the complexity of SHM. A wave simulation algorithm (WSA) was developed to understand the flexural wave propagation in composite structures and to utilize the code for developing the SNS. Simulation of AE responses in a plate and comparison with experimental results are shown in the paper. The SNS was recently tested by a team of researchers from University of Cincinnati and North Carolina A&T State University during a quasi-static proof test of a 9 meter long wind turbine blade at the National Renewable Energy Laboratory (NREL) test facility in Golden, Colorado. Twelve piezoelectric sensor nodes were used to form four continuous sensors to monitor the condition of the blade during the test. The four continuous sensors are used as inputs to the SNS. There are only two analog output channels of the SNS, and these signals are digitized and analyzed in a computer to detect damage. In the test of the wind turbine blade, multiple damages were identified and later verified by sectioning of the blade. The results of damage identification using the SNS during this proof test will be shown in this paper. Overall, the SNS is very sensitive and can detect damage on complex structures with ribs, joints, and different materials, and the system relatively inexpensive and simple to implement on large structures.

Citation Download Citation

Goutham R. Kirikera, Mark J. Schulz, and Mannur J. Sundaresan "Multiple damage identification on a wind turbine blade using a structural neural system", Proc. SPIE 6530, Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring 2007, 65300T (10 April 2007); https://doi.org/10.1117/12.716071

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