用合成数据提升飞机螺栓装配的故障预测，解决数据不平衡难题。
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets
- 针对时序数据设计增强与加权策略，缓解故障样本稀少问题。
- 在真实数据集上将关键故障识别率提升至92.3%。
- 专为航空螺母装配设计评估指标，更适合实际生产需求。
飞机制造自动化仍依赖大量人力，主要因装配流程复杂且需定制化。大型结构的精确定位误差可能导致高昂维护成本或零件报废。传统螺栓类紧固件（如螺钉、螺栓、螺母）多由固定基座机器人执行，难以适应复杂制造环境。本文聚焦于螺栓装配中的错误检测与分类，尤其关注航空螺母的可靠安装。由于故障案例罕见，深度学习模型训练面临数据不平衡挑战。为此，论文提出针对时序数据的类别加权与数据增强技术，有效提升分类性能。同时引入面向螺母装配的新型建模方法，强调实际应用相关指标而非仅关注准确率，显著增强了模型应对装配挑战的能力。
原文摘要 · Abstract (English)
Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements. One key challenge is achieving precise positioning, especially for large aircraft structures, where errors can lead to substantial maintenance costs or part rejection. Existing solutions often require costly hardware or lack flexibility. Used in aircraft by the thousands, threaded fasteners, e.g., screws, bolts, and collars, are traditionally executed by fixed-base robots and usually have problems in being deployed in the mentioned manufacturing sites. This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars. Safe assembly of threaded fasteners is paramount since acquiring sufficient data for training deep learning models poses challenges due to the rarity of failure cases and imbalanced datasets. The paper addresses this by proposing techniques like class weighting and data augmentation, specifically tailored for temporal series data, to improve classification performance. Furthermore, the paper introduces a novel problem-modeling approach, emphasizing metrics relevant to collar assembly rather than solely focusing on accuracy. This tailored approach enhances the models' capability to handle the challenges of threaded fastener assembly effectively.
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