Active Approaches to Identification and Failure Detection

主动识别和故障检测方法

• 批准号: 0620986
• 负责人: Stephen Campbell
• 金额: $ 18万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620986&HistoricalAwards=false
• 关键词: Active; Approaches; Identification; Failure; Detection

0620986 CampbellModern systems often involve both continuous and discrete processes. The discrete nature arises because of events such as discrete time controllers. These systems are called hybrid systems. The desire for increased performance makes consideration of nonlinear behavior necessary. Many classes of problems are most naturally modeled as a system of differential and algebraic equations (DAE). Recently it has been shown that a number of fundamental problems in control including observer design, model identification, and failure detection can be naturally viewed as problems involving DAEs. Intellectual Merit: The proposed research is a major extension of an active failure detection approach to include sampled data, hybrid, implicit and explicit nonlinear systems. Both theory and practical, robust, numerical algorithms will be developed. New theoretical understanding of nonlinear hybrid DAEs and their application to failure detection will result. The results will be applied to applications from areas such as aerospace, mechanical engineering and power systems. Broad Impact: Failure detection is now part of essentially every complex device or process. Failure detection plays a fundamental role in managing costs, promoting efficiency, and protecting the environment. Because of the pervasiveness of both hybrid and nonlinear processes and the need for failure detection, the proposed project will result in substantial advancement in a number of application areas, the training of the next generation of researchers to work on these problems, and contributions to several areas of national need. Among the broad impacts will be improved safety and reliability of manufacturing and transportation systems.

0620986坎贝尔现代系统通常涉及连续和离散过程。离散的本质是由于诸如离散时间控制器之类的事件而产生的。 这些系统称为混合系统。 对提高性能的需求使得考虑非线性行为成为必要。 许多类的问题最自然地建模为微分和代数方程（DAE）的系统。最近已经表明，控制中的一些基本问题，包括观测器设计，模型识别和故障检测，可以很自然地被视为涉及DAE的问题。智力优势：所提出的研究是一个积极的故障检测方法，包括采样数据，混合，隐式和显式非线性系统的一个主要扩展。理论和实际的，强大的，数值算法将被开发。 新的理论理解的非线性混合DAE和他们的应用程序的故障检测将导致。 研究结果将应用于航空航天、机械工程和电力系统等领域。广泛的影响：故障检测现在基本上是每个复杂设备或过程的一部分。 故障检测在管理成本、提高效率和保护环境方面发挥着重要作用。 由于混合和非线性过程的普遍性以及故障检测的需要，拟议的项目将导致 在一些应用领域取得了实质性进展，培训下一代研究人员研究这些问题，并为国家需要的几个领域做出贡献。 广泛的影响将包括提高制造和运输系统的安全性和可靠性。