关键词: 不平衡样本, 风机叶片, 样本增强, 生成式对抗网络, 开裂识别

Abstract: To solve the problem of few samples and low recognition rate of the cracked state of wind turbine blades,a method for enhancing samples based on Generative Adversarial Networks (GAN) was proposed to improve the recognition rate.Taking the empirical risk classification model as an example,the imbalanced sample problem was deeply analyzed from the theoretical point of view.The GAN network model satisfying the generation and discrimination of cracking samples was designed,and the batch normalization was introduced to obtain samples with standard normal distribution,so that convergence of the network train process was accelerated.The proposed method was tested on 36 UCI benchmark data sets with neural network as the classifier and F1 value,Recall,Precision as the metrics.Results showed that the enhanced results were better.Furthermore,real wind turbine blade cracking data were used for experiments with logistic regression and neural network as classifiers,and the results showed that the enhanced results are improved by 13.88% and 8.20% respectively by comparing with original imbalanced samples.Compared with SMOTE and ADASYN algorithm,the two classifiers were improved by 74%,11% and 19%,23% respectively.

Key words: imbalanced sample, wind turbine blade, sample enhancement, generative adversarial networks, cracking identification