基于GAN的滚珠丝杠传动故障分类图片库扩充
GAN based ball screw drive picture database enlargement for failure classification
摘要：
缺乏可靠的大型数据集是将现代机器学习方法应用于制造业故障检测领域的最大困难之一。为了开发滚珠丝杠副表面的故障分类功能，需要有足够的表面故障图像数据。当基于小数据集训练神经网络模型时，训练的模型可能缺乏泛化能力，并且在实践中可能表现不佳。本文的主要目标是基于生成对抗网络（GAN）生成合成图像，以扩大滚珠丝杠表面故障的图像数据集。滚珠丝杠副表面可能出现的失效类型有点蚀失效和锈蚀失效。之后，使用定性方法（包括专家观察、t-SNE可视化和FID评分的定量方法）对生成图像的质量和多样性进行评估。为了验证基于GAN的生成图像是否可以提高故障分类性能，对真实图像数据集进行了扩充，并将其替换为基于GAN的生成图像来执行分类任务。作者成功地创建了基于GAN的滚珠丝杠表面故障图像，对分类测试性能产生了积极影响。
英文摘要：
The lack of reliable large datasets is one of the biggest difficulties of using modern machine learning methods in the field of failure detection in the manufacturing industry. In order to develop the function of failure classification for ball screw surface, sufficient image data of surface failures is necessary. When training a neural network model based on a small dataset, the trained model may lack the generalization ability and may perform poorly in practice. The main goal of this paper is to generate synthetic images based on the generative adversarial network (GAN) to enlarge the image dataset of ball screw surface failures. Pitting failure and rust failure are two possible failure types on ball screw surface chosen in this paper to represent the surface failure classes. The quality and diversity of generated images are evaluated afterwards using qualitative methods including expert observation, t-SNE visualization and the quantitative method of FID score. To verify whether the GAN based generated images can increase failure classification performance, the real image dataset was augmented and replaced by GAN based generated images to do the classification task. The authors successfully created GAN based images of ball screw surface failures which showed positive effect on classification test performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
