性能驱动的蒸和自信的伪标签用于半监督的工业软传感器应用.
IEEE transactions on cybernetics
|July 3, 2025
概括
这项研究引入了一种新的半监督软传感器框架,以改善在标记数据稀缺时的工业质量预测. 该方法增强了特征表示和伪标签选择,大大减少了预测错误.
科学领域:
- 工业过程监控 工业过程监控
- 机器学习应用 机器学习应用
- 数据科学数据科学数据科学
背景情况:
- 软传感器开发的标记工业数据往往有限,阻碍了准确的过程监控.
- 现有的半监督方法面临特征有效性和伪标签信任评估方面的挑战.
研究的目的:
- 提出一个新的半监督软传感器框架,用于工业质量变量预测.
- 解决数据稀缺问题,改善软传感器模型的通用化.
主要方法:
- 开发了一种以绩效为导向的蒸策略,用于教师模型的SIameseLSTM结构.
- 引入了伪标签信任评估策略,以选择高质量的培训样本.
- 将这些策略集成到一个统一的半监督软传感器框架中.
主要成果:
- 拟议的框架证明了功能表示学习和模型概括的改进.
- 在生产数据集上验证,显示RMSE和MAE的显著减少.
- 实现了平均RMSE减少10.76%和MAE减少11.18%.
结论:
- 这种新型的半监督框架有效地提高了软传感器在具有有限标记数据的工业应用中的性能.
- 联合蒸和伪标签策略为准确的工业质量预测提供了强大的解决方案.
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