single-rb.php

JRM Vol.27 No.3 pp. 244-250
doi: 10.20965/jrm.2015.p0244
(2015)

Paper:

Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization

Guimei Gu, Rang Hu, and Yuanyuan Li

School of Automation and Electrical Engineering, Lanzhou Jiaotong University
Editorial Department of Journal of Lanzhou Jiaotong University, Lanzhou 730070, China

Received:
December 4, 2014
Accepted:
March 3, 2015
Published:
June 20, 2015
Keywords:
wind turbine blade, acoustic emission, support vector machine, particle swarm optimization
Abstract

Classification results of SVM-PSO

In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.

Cite this article as:
G. Gu, R. Hu, and Y. Li, “Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization,” J. Robot. Mechatron., Vol.27, No.3, pp. 244-250, 2015.
Data files:
References
  1. [1] Y. Hu, J. Dai, and D. Liu, “Research Status and Development Trend on Large Scale Wind Turbine Blades,” Chinese J. of Mechanical Engineering, Vol.49, No.20, pp. 140-151, 2013 (in Chinese).
  2. [2] X. Chen, J. Li, H. Cheng, B. Li, and Z. He, “Research and Application of Condition Monitoring and Fault Diagnosis Technology in Wind Turbines,” Chinese J. of Mechanical Engineering, Vol.47, No.9, pp. 45-52, 2011 (in Chinese).
  3. [3] C. Xu, J. L. Rose, and X. Zhao, “Detection Principle of Shape and Orientation of Corrosive Defects Using Lamb Waves,” J. of Robotics and Mechatronics, Vol.21, No.5, pp. 568-573, 2009.
  4. [4] B. Zhou, N. Wang, C. Fei, and C. Chen, “Study on the reliability for the blade structure of wind turbine,” Proc. of the eighth Int. Conf., No.2, pp. 1005-1008, 2005.
  5. [5] B. Ruanjia, M. Hosiucun, D. Patil, and G. Song, “Structural health monitoring of wind turbine blade using piezoceramic based active sensing and impedance sensing,” Proc. of the 11th IEEE Int. Conf. on Networking, Sensing and Control, pp. 661-666, 2014.
  6. [6] Y. Qu, C. Chen, H. Wu, and B. Zhou, “Research on crack recognition of blade based on acoustic emission and neural network,” Machinery Design & Manufacture, No.3, pp. 152-154, 2012 (in Chinese).
  7. [7] Y. Chen, S. Dai, and J. Bi, “Study on Key Technologies of Wind Turbine Blades,” Machinery, Vol.49, No.566, pp. 69-72, 2011 (in Chinese).
  8. [8] H. Yuan, L. Zhou, X. Ke, and H. Wang, “Analysis of Orientation for Crackle of Wind Turbines Blade Based on Acoustic Emission Signal,” Computer Engineering and Design, Vol.32, No.1, pp. 320-323, 2011 (in Chinese).
  9. [9] G. Caruana, Z. Limao, and M. Qi, “A MapReduce based parallel SVM for large scale spam filtering,” 2011 8th Int. Conf. on Fuzzy and Knowledge Discovery, Vol.4, pp. 2659-2662, 2011.
  10. [10] N. Shahid, H. Naqviljaz, and B. Qaisarsaad, “Quarter-Sphere SVM: Attribute and Spatio-Temporal correlation based Outlier & Event Detection in wireless sensor networks,” IEEE Wireless Communications and Networking Conf., pp. 2048-2053, 2012.
  11. [11] B. Soudan and M. Saad, “An Evolutionary Dynamic Population Size PSO Implementation,” 3rd Int. Conf. on Information and Communication Technologies: From Theory to Applications, pp. 1-5, 2008.
  12. [12] T. Tsujimoto, T. Shindo, T. Kimura, and K. Jin’no, “A relationship between network topology and search performance of PSO,” IEEE Congress on Evolutionary Computation, pp. 1-6, 2012.
  13. [13] A. Afjehabdollah, B. Andersen, W. Leejin, M. Norouzi, and E. Nikolaidis, “Advanced Concept Offshore Wind Turbine Development,” J. of Robotics and Mechatronics, Vol.18, No.5, pp. 728-735, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 16, 2018