WIND TURBINE BLADE FAULT DIAGNOSIS METHOD BASED ON IMPROVED MFCC ALGORITHM

Deng Qiang; Liang Tao; Zhang Jiaan; Tian Jiahui; Wang Tiecheng

doi:10.19912/j.0254-0096.tynxb.2022-1523

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 285-290. DOI: 10.19912/j.0254-0096.tynxb.2022-1523

WIND TURBINE BLADE FAULT DIAGNOSIS METHOD BASED ON IMPROVED MFCC ALGORITHM

Zhang Jiaan¹, Tian Jiahui², Wang Tiecheng³, Deng Qiang², Liang Tao²

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Abstract

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.

Key words

wind turbine blades / acoustic signal processing / fault diagnosis / feature extraction / Mel frequency cepstrum coefficient

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Zhang Jiaan, Tian Jiahui, Wang Tiecheng, Deng Qiang, Liang Tao. WIND TURBINE BLADE FAULT DIAGNOSIS METHOD BASED ON IMPROVED MFCC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 285-290 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1523

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