System Control and Diagnosis in Data-Rich Environment

数据丰富环境中的系统控制与诊断

• 批准号: RGPIN-2016-06375
• 负责人: Zhao, Qing
• 金额: $ 2.99万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710181
• 关键词: System; Control; Diagnosis; Data; Rich

Owing to the fast development of computer and network technology, nowadays processes and control systems are enhanced by rich measurement data from numerous sensors of multiple sources and locations. In large-scale manufacturing and chemical processes, application of distributed control systems (DCS) has created such a data-rich environment. Real-time measurements as well as historical data from different locations and sources are available for processing. Meanwhile, due to the lack of effective tools digging in the massive amount of data for relevant and useful information, industries are facing the so-called “data rich but information poor” syndrome. There are several situations where data becomes essential and critical in decision-making. First of all, systems and processes are mostly operated under well-controlled constraints that are well understood in the initial design stage. For many systems and processes, first-principle models and process knowledge are available and can be used for control and performance analysis/evaluation under the normal operation conditions. However, when unexpected conditions emerge, e.g. fault related or other time-varying operations, system operations deviate from its initially designed conditions, and fidelity of those models suffers. In this case, data becomes important for system performance evaluation, fault diagnosis, updating of the control law and even condition monitoring based maintenance. How to extract useful up-to-date information from multiple sources of data and fuse' it with knowledge from the physical model has raised attention from industries and research communities. Furthermore, in many practical systems, traditional time-series measurement, as well as measurement of alternate forms, e.g. distribution functions, and spectroscopic data, are available. They all carry vital information about the system. How to extract useful information from all available data of different form for control and system fault diagnosis is a new challenge. Another situation where data is essential arises when the system and process is highly complex and the physical model is not even valid. In this case, data-driven modeling and important process analytics become almost the only means. In this research program, we will focus on some of the new challenges facing conventional system control and fault diagnosis methodologies in the new data-rich paradigm. The long-term objective is aimed at developing effective design and analysis tools for control and diagnosis of systems/processes that are capable of processing data of different forms and/or from multiple sources (timely and spatially). These tools are essential for the development of the new generation information system and control/instrumentation devices for the new industry standard, such as Industrial Internet of Things (IIoT).

由于计算机和网络技术的快速开发，如今的流程和控制系统通过来自多个来源和位置的众多传感器的丰富测量数据来增强。在大型制造和化学过程中，分布式控制系统（DC）的应用创造了如此丰富的数据。实时测量以及来自不同位置和来源的历史数据可供处理。同时，由于缺乏有效的工具来挖掘有关相关和有用信息的大量数据，行业正面临着所谓的“数据丰富但信息差”综合征。 在几种情况下，数据在决策中变得至关重要且至关重要。首先，系统和过程主要是在控制良好的约束下进行的，这些约束在初始设计阶段得到了很好的理解。对于许多系统和过程，可以使用第一原则模型和过程知识，可用于在正常操作条件下进行控制和绩效分析/评估。但是，当出现意外情况时，例如与故障相关或其他时变操作，系统操作偏离了其最初设计的条件，并且这些模型的保真度受到了影响。在这种情况下，数据对于系统性能评估，故障诊断，控制法的更新以及基于条件监控的维护变得重要。如何从多个数据源中提取有用的最新信息并与物理模型的知识融合，从而引起了行业和研究社区的关注。此外，在许多实用系统中，传统的时间序列测量以及替代形式的测量，例如分布功能和光谱数据可用。他们都提供有关系统的重要信息。如何从不同形式的所有可用数据中提取有用的信息进行控制和系统故障诊断是一个新的挑战。当系统和过程高度复杂并且物理模型甚至无效时，就会出现数据至关重要的另一种情况。在这种情况下，数据驱动的建模和重要过程分析几乎成为唯一的手段。 在该研究计划中，我们将重点关注新数据范围范式中常规系统控制和故障诊断方法所面临的一些新挑战。长期目标旨在开发有效的设计和分析工具，以控制和诊断能够处理不同形式和/或来自多个来源的数据的系统/过程（及时和空间）。这些工具对于新一代信息系统的开发以及新的行业标准（例如工业互联网（IIOT））的开发至关重要。