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
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.
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