一个多速率的高阶动态双潜变量概率模型及其过程监控应用程序
Ze Ying1, Yuqing Chang2, Yuchen He3
1College of Information Science and Engineering, Northeastern University, Shenyang 110819, People's Republic of China.
ISA transactions
|April 23, 2024
概括
本研究引入了一种用于工业过程监控的多速率高阶动态双潜变量概率学 (MHDTVP) 模型. 该MHDTVP模型有效地处理复杂的数据特征,提高动态工业过程的监控效率.
科学领域:
- 工业过程监控 工业过程监控
- 数据分析数据分析数据分析.
- 化学工程是化学工程的重要组成部分.
背景情况:
- 工业过程安全依赖于完整的,低级的自相关数据收集.
- 现实世界的工业流程通常具有多速率采样和高阶动态,使监控复杂化.
- 现有的方法在工业环境中难以应对多速率和高阶动态数据的复杂性.
研究的目的:
- 在复杂的工业条件下开发一种可靠的质量相关的工艺监控模型.
- 从质量相关和无关的角度从多速率测量中提取相关性.
- 揭示和建模多速率采样过程中固有的动态.
主要方法:
- 提出了一个多速率高阶动态双隐变量概率模型 (MHDTVP).
- 该模型使用双潜变量结构提取数据相关性.
- 使用自回归双潜变量结构来捕获高阶动态特征.
- 参数使用预期最大化 (EM) 代框架进行训练.
主要成果:
- MHDTVP模型成功地提取了多速率测量之间的数据相关性.
- 高级的动态特征,无论是与质量相关的还是无关的,都得到了有效的识别.
- 该模型在经过验证的案例研究中显示出更高的监控效率.
- 使用田纳西伊斯曼工艺 (TEP) 和热电厂 (TPP) 验证了性能.
结论:
- 拟议的MHDTVP模型为复杂的工业过程提供了增强的监控能力.
- 该模型有效地解决了多速率抽样和高阶动态所带来的挑战.
- 与现有的动态流程监控方法相比,MHDTVP提供了显著的改进.
相关概念视频
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