Among the renewable energy sources, wind power generation presents competitive costs and high installation potential in many countries. Ensuring the integrity of the generation equipment plays an important role for reliable energy production. Therefore, nondestructive test procedures are required, especially for turbine blades, which are subject to severe operational conditions due to phenomena such as lightning strikes, mechanical stress, humidity and corrosion. Nondestructive ultrasonic test techniques are commonly applied in their predictive maintenance. This work proposes the use of novelty detection methods combined with nondestructive ultrasound testing to identify structural problems in wind turbine blades. Ultrasound signals are preprocessed using both, wavelet denoising and principal component analysis. Novelty detection deals with the one-class classification problem, when only the normal condition signatures are required for the classification system design. For the nondestructive test of turbine blades, this is an interesting paradigm because it is not always possible to obtain test samples from all of the existing flaw conditions. Our experimental results indicate the efficiency of the proposed method.

中文翻译：

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使用新颖性检测的基于超声波的风力涡轮机叶片损坏识别

在可再生能源中，风力发电在许多国家具有竞争力的成本和高安装潜力。确保发电设备的完整性对于可靠的能源生产起着重要作用。因此，需要无损检测程序，特别是对于涡轮叶片，由于雷击、机械应力、湿度和腐蚀等现象，涡轮叶片会受到严酷的操作条件的影响。无损超声波检测技术通常应用于其预测性维护。这项工作建议使用新颖的检测方法结合无损超声检测来识别风力涡轮机叶片的结构问题。使用小波去噪和主成分分析对超声信号进行预处理。当分类系统设计只需要正常条件特征时，新颖性检测处理一类分类问题。对于涡轮叶片的无损测试，这是一个有趣的范例，因为并非总是可以从所有现有缺陷条件中获取测试样本。我们的实验结果表明了所提出方法的有效性。