Zhang Han-Bing, Zhang Chun-Yan, Cheng De-Jun, Zhou Kai-Li, Sun Zhi-Ying
School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel). 2024 Mar 4;24(5):1663. doi: 10.3390/s24051663.
Casting defects in turbine blades can significantly reduce an aero-engine's service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft performance. Existing defect detection methods face challenges in effectively detecting multi-scale defects and handling imbalanced datasets, leading to unsatisfactory defect detection results. In this work, a novel blade defect detection method is proposed. This method is based on a detection transformer with a multi-scale fusion attention mechanism, considering comprehensive features. Firstly, a novel joint data augmentation (JDA) method is constructed to alleviate the imbalanced dataset issue by effectively increasing the number of sample data. Then, an attention-based channel-adaptive weighting (ACAW) feature enhancement module is established to fully apply complementary information among different feature channels, and further refine feature representations. Consequently, a multi-scale feature fusion (MFF) module is proposed to integrate high-dimensional semantic information and low-level representation features, enhancing multi-scale defect detection precision. Moreover, R-Focal loss is developed in an MFF attention-based DEtection TRansformer (DETR) to further solve the issue of imbalanced datasets and accelerate model convergence using the random hyper-parameters search strategy. An aero-engine turbine blade defect X-ray (ATBDX) image dataset is applied to validate the proposed method. The comparative results demonstrate that this proposed method can effectively integrate multi-scale image features and enhance multi-scale defect detection precision.
涡轮叶片中的铸造缺陷会显著缩短航空发动机的使用寿命，并在暴露于恶劣环境时对叶片造成二次损伤。因此，铸造缺陷检测在提高飞机性能方面起着至关重要的作用。现有的缺陷检测方法在有效检测多尺度缺陷和处理不平衡数据集方面面临挑战，导致缺陷检测结果不尽人意。在这项工作中，提出了一种新颖的叶片缺陷检测方法。该方法基于具有多尺度融合注意力机制的检测变压器，综合考虑各种特征。首先，构建了一种新颖的联合数据增强（JDA）方法，通过有效增加样本数据数量来缓解不平衡数据集问题。然后，建立了基于注意力的通道自适应加权（ACAW）特征增强模块，以充分利用不同特征通道之间的互补信息，并进一步细化特征表示。因此，提出了一种多尺度特征融合（MFF）模块，以整合高维语义信息和低级表示特征，提高多尺度缺陷检测精度。此外，在基于MFF注意力的检测变压器（DETR）中开发了R-Focal损失，以进一步解决不平衡数据集问题，并使用随机超参数搜索策略加速模型收敛。应用航空发动机涡轮叶片缺陷X射线（ATBDX）图像数据集来验证所提出的方法。比较结果表明，该方法能够有效整合多尺度图像特征，提高多尺度缺陷检测精度。
