The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs and lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view of these complex structures during curing. In this paper, we enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterizes an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.

翻译：风力涡轮机叶片（WTB）生产中的制造缺陷会导致运维成本显著增加，并可能引发严重灾难性后果。因此，制造过程中的检测对确保复合材料构件的一致性生产至关重要。非接触式传感技术（如调频连续波雷达）因能在固化期间提供这类复杂结构的全景视图而日益普及。本文利用调频连续波雷达作为非破坏性传感模态，提升制造过程的质量保障水平。此外，我们开发了一种新型异常检测流程，具备以下优势：（1）采用调频连续波雷达中频信号的解析表示作为特征，从接收波中分离材料特性与往返延迟信息；（2）提出一种名为聚焦支持向量数据描述（focus-SVDD）的新型异常检测方法，该方法通过移除健康数据特征后定义数据集边界，从而聚焦于异常属性；（3）所提方法采用复值自编码器移除健康特征，并引入新型激活函数——指数幅度衰减（EAD）。该函数利用表征瞬时幅度信号的瑞利分布特性。通过将所提方法应用于采集数据，其性能优于其他先进的无监督异常检测方法，验证了有效性。该方法不仅有望为结构健康监测做出重要贡献，还将推动深度复值数据处理与SVDD应用领域的发展。