Robust Process Identification with Dynamic Feature Analysis

过程工厂中的每个人，从工厂经理到工程师到技术人员，都依赖于大量的数据，这些数据在日常分析和决策中起着重要作用。常见的过程控制实践是借助过程知识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采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，过程控制研究团体在不断增加的压力下交付分析工具，以应对过程工业现代实践的挑战。

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.