Nowadays, leveraging data augmentation-based methods to address the data-shortage problem in diagnosis field becomes fairly prevailing, while assessing the quality of the generated data receives little attention. Typically, the data quality is evaluated by straightforward employing some existing shallow functions or simple classification models, which have several disadvantages. In this paper, the limitations of existing techniques are comprehensively summarized and illustrated by pathological examples. Accordingly, a novel metric method called Normal Sinkhorn Distance (NSD) is developed to evaluate the quality of vibration data. Firstly, the raw vibration data are directly transformed into time–frequency images with a tensor format and stored to ensure the following computation efficiency. Then an off-the-shelf pretrained Inception model is employed to capture their high-order data structure and amplitude-wise dependence. Finally, the NSD metric is constructed and adopted to measure the similarity between the real data and generated data in a high-dimensional feature space. Extensive results indicate that the proposed NSD provides a more reliable indication for practitioners than the existing evaluation measures on the data quality.

如今，利用基于数据增强的方法来解决诊断领域的数据短缺问题变得相当普遍，而评估生成数据的质量却很少受到关注。通常，数据质量是通过直接使用一些现有的浅层函数或简单的分类模型来评估的，这有几个缺点。在本文中，对现有技术的局限性进行了全面总结，并通过病理实例进行了说明。因此，开发了一种称为法向沉角距离 (NSD) 的新型度量方法来评估振动数据的质量。首先，将原始振动数据直接转换为张量格式的时频图像并存储，以保证后续的计算效率。然后使用现成的预训练初始模型来捕获它们的高阶数据结构和振幅相关性。最后，构造并采用 NSD 度量来衡量高维特征空间中真实数据与生成数据之间的相似性。广泛的结果表明，与现有的数据质量评估措施相比，拟议的 NSD 为从业者提供了更可靠的指示。