Paper Titles
Optimized Simulation for GaN Growth in Vertical HVPE Reactor
p.116
A Novel High Efficiency Solution of High Power LED Lighting
p.121
Path Planning of the Robot for Aero-Engine Turbine Blades Maintenance
p.127
Study and Implementation of Expendable Current Profiler Measurement
p.133
State Characteristic Parameters Extraction Method of Wind Turbine Blades Based on the Length Fractal Dimension
p.139
The Design of the Detection and Localization of the Color Image for the Location of Road Traffic Signs
p.143
The Finite Element Analysis in Measurement System of Airplane Fuel Mass
p.147
The Motion Control of Clothes Model Robot and Measurement of Clothes Energy Consumption
p.153
Quality Detection and Specie Identification of Apples Based on Multi-Spectral Imaging
p.158

State Characteristic Parameters Extraction Method of Wind Turbine Blades Based on the Length Fractal Dimension

Article Preview

Abstract:

According to characteristic of wind turbine vibration signal, base on the length fractal dimension principle, carry out quantitative description of length fractal characteristics to nonlinear signals on the fault generated by the wind turbine blades. The test and calculation results show that when the different wind turbine blades failures occur, the length fractal dimension value appear clear rules. Therefore, we can use the length fractal dimension effectively extract the fault character parameters of wind turbine blade.

You might also be interested in these eBooks
Advanced Measurement and Test View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 301-303)

Pages:

139-142

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.301-303.139

Citation:

Cite this paper

Online since:

July 2011

Authors:

Ya Feng Li, Yu Xiu Xu, Xin Hua Ma

Keywords:

Characteristic Parameters, Fault Diagnosis, Length Fractal Dimension, Wind Turbine Blade

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] Sheng zhaoshun, Yin qiling. Equipment condition monitoring and fault diagnosis technology and application [M]. Beijing: Chemical Industry Press . (2003).

Google Scholar

[2] Su shaoyu. Design and operation maintenance of wind turbine[M]. Beijing: China Electric Power Press, (2003).

Google Scholar

[3] Jonathan R. White, Douglas E. Adams, Mark A. Rumsey, Jeroen van Dam, Scott Hughes. Impact Loading and Damage Detection in a Carbon Composite TX-100 Wind Turbine Rotor Blade[C]. 46th AIAA Aerospace Sciences Meeting, 2008, 1(23): 45-50.

DOI: 10.2514/6.2008-1349

Google Scholar

[4] Shao Jinhua. Dynamic characteristics simulation study of Wind turbine system[D]. Master degree thesis. Chongqing University, 2007. 4.

Google Scholar

[5] Rui Chuang, Lin Fuhong. Wind turbine online monitoring and diagnosis system study[J]. East China Electric Power，2009. 1， 37（1）: 190-193.

Google Scholar

[6] J. Milimonfared, H.M. Kelk, S. Nadi. A Novel Approach for Broken-rotor-bar Detection in Cage Induction Motors[C]. IEEE Tranactions on Industry Applications. 1999, 35(5): 1000~1006.

DOI: 10.1109/28.793359

Google Scholar

[7] Xu Yuxiu, Zhang Jian, Hou Rongtao. Mechanical system dynamics fractal characteristics and fault diagnosis methods [M]. Beijing: National Defence Industry Press，2006. 1.

Google Scholar

[8] Zhang Ping, Ouyang Guangyao. Signal characteristics extraction technology and its application based on Fractal geometry the [J]. Naval University of Engineering. 2001. 2: 1-5.

Google Scholar

[9] Hu Xiaotang. An improved selection method for phase space reconstruction parameters [J]. Vibration Engineering. 2001. 14(2): 242-244.

Google Scholar

[10] MA Junhai. Reconstruction of Complex Nonlinear Systems [M]. Tianjin: Tianjin University Press, (2005).

Google Scholar