基于SMOTE-XGBOOST的不平衡数据质量监测,支持边缘计算
Yan Han1, Zhe Wei2, Guotian Huang3
1School of Mechanical Engineering, Shenyang University of Technology, Shenyang, 110870, China.
Scientific reports
|May 2, 2024
概括
本研究介绍了用于工业产品组装质量监测的边缘计算框架. 它通过采样方法解决数据不平衡问题,并提出SMOTE-XGBoost模型,以改进缺陷检测.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 产品组装产生高维度,不平衡的数据,具有挑战性的质量监控.
- 传统的基于云计算的处理难以应对工业数据的数量.
研究的目的:
- 为实时产品组装质量监测提出一个边缘计算框架.
- 调查和减轻工业数据集中的数据不平衡问题.
- 开发和验证一个有效的质量监测模型.
主要方法:
- 开发了一个利用边缘计算进行数据处理的框架.
- 他们比较了五种数据采样技术 (边界SMOTE,随机下采样,随机上采样,SMOTE,ADASYN).
- 实现了一个带有网格搜索超参数优化的SMOTE-XGBoost模型.
主要成果:
- 在质量监测方面,SMOTE-XGBoost模型证明了其有效性.
- 边缘计算减少了云数据聚合的负担.
- 在IGBT模块组装线上的实验验证证证了模型的有效性.
结论:
- 拟议的边缘计算框架加强了工业质量监测.
- 对不平衡数据的有效处理对于准确的组装质量评估至关重要.
- SMOTE-XGBoost模型为检测组装缺陷提供了一个强大的解决方案.
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