Abstract
The fault problem of windturbine blades cannot be ignored. This paper proposes a fault diagnosis method for windturbine blades based on feature optimization strategy. First, the Mel-frequency cepstrum coefficient(MFCC) method is used to extract the feature samples of the windturbine sound signal, and then the entropy weight method is used to optimize the characteristics of the feature samples, and finally the K-means clustering is combined to realize the fault diagnosis of the windturbine blade. The effectiveness of the proposed method is verified by using two faulty windturbine and one faultless windturbine in a wind farm.
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