在物联网驱动的工厂中使用物流提升,随机森林和SVM增强异常检测:比较机器学习方法
Mohammed Aly1, Mohamed H Behiry2,3
1Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr, 11829, Egypt. mohammed-alysalem@eru.edu.eg.
Scientific reports
|July 3, 2025
概括
后勤提升有效地检测工业物联网设置中的异常,优于随机森林和SVM. 这种机器学习方法为实时工业异常检测系统提供了高精度.
科学领域:
- 工业物联网工业物联网工业物联网
- 机器学习 机器学习
- 异常检测检测异常检测
背景情况:
- 工业环境产生大量的传感器数据.
- 有效的异常检测对于运营效率和安全至关重要.
- 现有的机器学习模型需要对工业物联网异常检测进行评估.
研究的目的:
- 评估和比较物流提升,随机森林和支持矢量机器 (SVM) 用于工业物联网中的异常检测.
- 确定最有效的机器学习算法来分类工厂传感器数据中的异常.
- 为开发强大的实时异常检测系统提供见解.
主要方法:
- 对现实世界数据集的分析,包括来自工厂传感器的15,000个实例.
- 使用的接收器操作特征 (ROC) 曲线,混矩阵和标准性能指标.
- 对物流提升,随机森林和支持矢量机 (SVM) 算法的比较评估.
主要成果:
- 后勤增强实现了最高的性能,曲线下的面积 (AUC) 为0.992,准确率为96.6%,精度为93.5%,回忆率为94.8%,F1得分为0.941.
- 后勤增强证明了对不平衡数据的优越处理,有134个假阳性和117个假阴性.
- 随机森林表现出强的结果 (AUC = 0.9982),SVM表现出高回忆率,但物流增强的合奏方法最有效.
结论:
- 后勤增强是工业物联网环境中异常检测最有效的机器学习算法.
- 这些发现支持对工厂实时异常检测系统的物流提升的实施.
- 未来的研究应该探索混合架构和边缘优化,以加强工业物联网异常检测.
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