通过自适应性歧视性转移词典将通用跨领域工业过程监测与属性嵌入配对学习
概括
本研究介绍了一种适应性歧视性转移词典对学习 (ADTDPL) 方法,用于跨领域的工业过程监测. 该方法有效地处理不同的数据分布和未标记的数据,提高监测准确性.
科学领域:
- 工业过程监控 工业过程监控
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 工业过程数据通常具有多种分布和独特的特征空间,限制了传统的监测.
- 未标记的数据进一步挑战了传统过程监控技术的有效性.
研究的目的:
- 开发一种先进的方法,用于普遍的跨领域工业过程监测.
- 为了应对数据分布的分歧,特征空间的不同以及未标记的数据所带来的挑战.
主要方法:
- 提出了一种具有属性嵌入的适应性歧视性转移字典对学习 (ADTDPL) 方法.
- 使用转移重建和通用字典对齐功能空间.
- 通过属性嵌入集成语义属性,以提高可转移性和可解释性.
- 采用自适应分布的一致性函数和歧视性结构规范化.
- 引入了对未标记目标域名的选择性伪标签策略.
主要成果:
- 在跨领域的过程监控中,ADTDPL方法表现出卓越的性能.
- 有效地减少域间的分布差异.
- 确保对字典对和编码系数进行区分.
- 通过伪标签成功处理未标记的数据.
结论:
- 拟议的ADTDPL方法为通用的跨领域工业过程监测提供了一个强大的解决方案.
- 属性嵌入和自适应分布的一致性增强知识传输和监控准确性.
- 在田纳西伊斯曼平台和电解工艺 (AEP) 上验证.
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