WIND TURBINE BLADE FAULT DIAGNOSIS METHOD BASED ON IMPROVED MFCC ALGORITHM
The traditional acoustic signal processing methods cannot effectively extract the acoustic features of wind turbine blades and the fault diagnosis accuracy is insufficient.Therefore,a fault feature extraction method based on improved Mel frequency cepstrum coefficient(MFCC)algorithm is proposed.Fast Fourier transform is used to analyze the frequency characteristics of the acoustic signal from wind turbine blade and wind noise at different wind speeds,and the corresponding frequency distribution regions are obtained.Meanwhile,we divide the whole frequency band into three parts,and use particle swarm optimization(PSO)to optimize the sensitivity of Mel function in different frequency bands.In the iterative optimization process,the sound characteristics of turbine blades extracted by MFCC algorithm are clustered,and the Silhouette coefficient is taken as the fitness function.Taking the blade sound acquisition data of a wind farm in North China as an example,the adaptability of the algorithm under different wind speeds is investigated,and a classifier based on support vector machine(SVM)is constructed to achieve accurate identification of wind turbine blade faults,which verifies the effectiveness of the method.
wind turbine bladesacoustic signal processingfault diagnosisfeature extractionMel frequency cepstrum coefficient
NETL
NSTL
万方数据
维普
