Han Yan, Wei Zhe, Huang Guotian
School of Mechanical Engineering, Shenyang University of Technology, Shenyang, 110870, China.
Genertec Shenyang Machine Tool Co., Ltd, Shenyang, 110041, China.
Sci Rep. 2024 May 2;14(1):10151. doi: 10.1038/s41598-024-60600-x.
Product assembly involves extensive production data that is characterized by high dimensionality, multiple samples, and data imbalance. The article proposes an edge computing-based framework for monitoring product assembly quality in industrial Internet of Things. Edge computing technology relieves the pressure of aggregating enormous amounts of data to cloud center for processing. To address the problem of data imbalance, we compared five sampling methods: Borderline SMOTE, Random Downsampling, Random Upsampling, SMOTE, and ADASYN. Finally, the quality monitoring model SMOTE-XGBoost is proposed, and the hyperparameters of the model are optimized by using the Grid Search method. The proposed framework and quality control methodology were applied to an assembly line of IGBT modules for the traction system, and the validity of the model was experimentally verified.
产品装配涉及大量生产数据，这些数据具有高维度、多样本和数据不平衡的特点。本文提出了一种基于边缘计算的工业物联网产品装配质量监测框架。边缘计算技术减轻了将大量数据聚合到云中心进行处理的压力。为了解决数据不平衡问题，我们比较了五种采样方法：边界合成少数类过采样技术（Borderline SMOTE）、随机下采样、随机上采样、合成少数类过采样技术（SMOTE）和自适应合成采样方法（ADASYN）。最后，提出了质量监测模型SMOTE-XGBoost，并使用网格搜索方法对模型的超参数进行了优化。将所提出的框架和质量控制方法应用于牵引系统IGBT模块的装配线，并通过实验验证了模型的有效性。
