基于变压器的专家的多任务学习与多门混合,用于制造业的预测过程监控
Jiaojiao Wang1,2, Yao Sui3, Chang Liu1,2
1Institute of Intelligent Media Technology, Communication University of Zhejiang, Hangzhou, Zhejiang, China.
Science progress
|December 10, 2024
概括
这项研究引入了基于变压器的专家的新多门组合,用于预测过程监控. 这种先进的方法改善了制造业的实时预测,优于现有技术.
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
- 人工智能的人工智能
背景情况:
- 预测性流程监控对于管理制造和业务流程,防止延误至关重要.
- 目前的单任务学习方法在捕捉过程变化方面有限,并且很复杂.
- 制造过程监控通常只关注预测产品循环时间.
研究的目的:
- 开发一个先进的预测过程监测框架.
- 解决单任务学习在捕捉过程变化的局限性.
- 提高制造业实时预测的准确性和效率.
主要方法:
- 提出了基于变压器的专家框架的多门混合.
- 在多任务学习架构中利用了变压器网络.
- 雇佣专家网,以模拟任务的共同点和差异.
主要成果:
- 拟议的框架在五个现实事件日志中表现出卓越的性能.
- 在一般化,有效性和效率方面表现优于三个替代架构.
- 成功捕获了历史过程执行中的变化.
结论:
- 基于变压器的专家的多门组合对于预测过程监控非常有效.
- 这种方法比传统的单任务学习方法提供了显著的改进.
- 该框架为复杂的制造环境中的实时预测提供了强大的解决方案.
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