RESEARCH ON WIND TURBINE BLADE FAULT DIAGNOSIS BASED ON SYMMETRIC OCTAV-BASED IMPROVED MFCC ALGORITHM

Zhang Jia&#x02019;an; Shi Runze; Ren Hongyi

doi:10.19912/j.0254-0096.tynxb.2024-1979

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 347-356. DOI: 10.19912/j.0254-0096.tynxb.2024-1979

RESEARCH ON WIND TURBINE BLADE FAULT DIAGNOSIS BASED ON SYMMETRIC OCTAV-BASED IMPROVED MFCC ALGORITHM

Zhang Jia’an^1,2, Shi Runze³, Ren Hongyi³

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Abstract

Aiming at the problem that wind turbine blades are prone to fault and difficult to detect in harsh environments, a new fault diagnosis method is proposed. This method combines the Frequency octave theory and the Mel Frequency Cepstrum Coefficient （MFCC） algorithm, and optimizes the traditional MFCC algorithm by introducing the symmetric variable frequency octave-based technology. In terms of frequency band division, according to the characteristics of blade sound signals and octave theory, the mapping relationship between physical frequency and Mel frequency is reconstructed to enhance the algorithm’s ability to extract fault features distributed in the middle frequency band and high frequency band, and effectively reduce noise interference. Then, the K-means clustering algorithm is used to cluster the acoustic features extracted by the optimized MFCC algorithm. The elbow rule is used to determine the optimal number of clusters under different states of blades, and the noise clusters are removed according to the short-term energy distribution, so as to effectively distinguish the sound signals of different states of blades. Finally, a classifier based on random forest algorithm was constructed to accurately diagnose blade faults. It verifies the ability of the improved MFCC algorithm to extract the acoustic features of the wind turbine blades and anti-interference.

Key words

wind turbine blades / fault detection / clustering algorithms / acoustic signal processing / MFCC / feature extraction

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Zhang Jia’an, Shi Runze, Ren Hongyi. RESEARCH ON WIND TURBINE BLADE FAULT DIAGNOSIS BASED ON SYMMETRIC OCTAV-BASED IMPROVED MFCC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 347-356 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1979

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