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 Campbell 现代系统通常涉及连续过程和离散过程。离散性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。 对提高性能的渴望使得有必要考虑非线性行为。 许多类别的问题最自然地建模为微分方程和代数方程 (DAE) 系统。最近的研究表明，控制中的许多基本问题，包括观测器设计、模型识别和故障检测，都可以自然地视为涉及 DAE 的问题。智力优点：所提出的研究是主动故障检测方法的主要扩展，包括采样数据、混合、隐式和显式非线性系统。将开发理论和实用、稳健的数值算法。 将会产生对非线性混合 DAE 及其在故障检测中的应用的新理论理解。 研究结果将应用于航空航天、机械工程和电力系统等领域的应用。广泛影响：故障检测现在基本上已成为每个复杂设备或流程的一部分。 故障检测在管理成本、提高效率和保护环境方面发挥着基础作用。 由于混合和非线性过程的普遍性以及故障检测的需要，拟议的项目将在许多应用领域取得实质性进展，培训下一代研究人员解决这些问题，并为国家需要的几个领域做出贡献。 广泛的影响包括提高制造和运输系统的安全性和可靠性。