监督的概率动态控制潜变模型用于质量模式预测和优化
Niannian Zheng1, Yuri A W Shardt2, Xiaoli Luan3
1Department of Automaton Engineering, Technical University of Ilmenau, 98693 Ilmenau, Germany; Institute of Automation, Jiangnan University, 214122 Wuxi, China.
ISA transactions
|August 14, 2024
概括
一个新的监督概率动态控制潜变量 (SPDCLV) 模型增强了在线预测和流程质量的实时优化. 它明确模拟了动态因果关系,以改善工业过程的监控和控制.
科学领域:
- 工艺工程是过程工程.
- 数据科学数据科学数据科学
- 控制系统 控制系统
背景情况:
- 现有的概率潜变模型缺乏明确的动态因果关系建模.
- 在线预测和实时优化工艺质量指标对于工业效率至关重要.
研究的目的:
- 为在线预测和实时质量优化提出一个监督的概率动态控制潜变量 (SPDCLV) 模型.
- 从被操纵的输入到质量模式,明确地建模动态因果关系.
- 在工程应用中开发基于模式的质量预测和优化框架.
主要方法:
- 开发一个动态控制的贝叶斯网络来建模因果关系.
- 实现期望最大化,前向过和后向平滑算法用于模型学习.
- 探索模式过和基于模式的软传感器,用于在线质量预测.
主要成果:
- 通过建模过程动态,SPDCLV模型有效地预测和优化质量指标.
- 案例研究证明了在工业削电路中的成功应用以及一个数值示例.
- 该方法可以直接控制对所需质量条件的模式.
结论:
- 拟议的SPDCLV方法为在线质量预测和实时优化提供了一个强大的方法.
- 它通过结合动态因果关系来显著改善现有模型.
- 该框架有助于在工业环境中加强过程监测和控制.
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