Robust Process Identification with Dynamic Feature Analysis

通过动态特征分析进行鲁棒过程识别

• 批准号: RGPIN-2017-03833
• 负责人: Huang, Biao
• 金额: $ 4.23万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637802
• 关键词: Robust; Process; Identification; Dynamic; Feature

Everyone in a process plant, from plant managers to engineers to technicians, relies on a massive amount of data, which plays a significant role in daily analysis and decision making. Common process control practice is to develop models based on data with the aid of process knowledge. But as modern process data has increased in dimensionality, diversity and complexity, traditional analytical tools have been unable to keep up with this onslaught of complex data. High dimensionality of data and irregularities during data collection pose many challenges in data-based modeling, thereby casting serious doubt on the validity of traditional modeling techniques. As a result, the process control research community is under ever increasing pressure to deliver analytic tools to cope with the challenges of the modern day practices of the process industries.

从工厂经理到工程师再到技术人员，流程工厂中的每个人都依赖于大量的数据，这些数据在日常分析和决策中发挥着重要作用。通常的过程控制实践是在过程知识的帮助下开发基于数据的模型。但是，随着现代过程数据在维度、多样性和复杂性方面的增加，传统的分析工具已经无法跟上这种复杂数据的冲击。数据的高维性和数据收集过程中的不规则性给基于数据的建模带来了许多挑战，从而使传统建模技术的有效性受到严重质疑。因此，过程控制研究界面临着越来越大的压力，需要提供分析工具来科普过程工业现代实践的挑战。