Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (7): 1895-1904.doi: 10.23940/ijpe.19.07.p16.18951904

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Fault Diagnosis of Wind Turbine Blades based on Wavelet Theory and Neural Network

Junxi Bia,b, Chenglong Zhengb,*, Hongzhong Huangc, Xiaojuan Songb, and Jinfeng Lid   

  1. a Aviation College, Inner Mongolia University of Technology, Hohhot, 010051, China
    b College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
    c Institute of Reliability Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
    d Inner Mongolia Institute of Metrology Testing and Research, Hohhot, 010020, China
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    This paper is supported by the Inner Mongolia Science & Technology Plan Project (No. 102-510001), the Inner Mongolia Science and Technology Achievement Conversion Project (No. CHZH2018130), and the Inner Mongolia Autonomous Region Science & Technology Innovation to Guide the Reward Project (No. 102-413128).

Abstract: With the development of the wind turbine industry, the reliability requirements of wind turbine blades are continuously increasing. In this paper, static load fatigue experiments are carried out on wind turbine blades, and the collected fault data of blades are extracted using the wavelet transform method. Wavelet theory is applied to remove the noise of the data and eliminate the interference of noise on the fault diagnosis of wind turbine blades. Then, the wavelet decomposition method is used to decompose high frequency signals and low frequency signals. The faulty low frequency signals are extracted and analyzed in the time domain, and a fault diagnosis method of wind turbine blade is established. The data of different vibration frequencies of wind turbine blades are collected by the acquisition system, and the data are imported into the neural network. The neural network is used to process the data and identify the states of wind turbine blades. The neural network proves that the wavelet transform method has reliable fault diagnosis ability in time domain analysis.

Key words: fatigue experiment, fault data, wavelet transform method, time domain analysis, fault diagnosis, neural network