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Intelligent Control of Mechanical Structures for Damage Mitigation and Failure Prognosis

用于减轻损伤和故障预测的机械结构智能控制

基本信息

批准号：
9216386
负责人：
Asok Ray
金额：
$ 20万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1992
资助国家：
美国
起止时间：
1992-09-15 至 1996-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216386&HistoricalAwards=false
关键词：
Intelligent Control Mechanical Structures Damage

项目摘要

Interdisciplinary research in the fields of control systems, structural dynamics, and materials science is proposed for damage mitigation and failure prognosis of mechanical structures as an augmentation to intelligent control and diagnostics in complex dynamic processes. The key ideas of the proposed research are (i) a significant improvement in service life could be achieved by a small reduction in the system dynamic performance; and (ii) impending failures of critical plant components can be predicted in real time. Specifically, prognoses of impending component failures are critical for enhancement of plant and personnel safety, improvement of availability and operability, and reduction of operational cost. As the science and technology of materials continue to evolve, the design methodologies for damage- mitigating control and failure prognostics must have the capability of easily incorporating an appropriate representation of material properties. This requires augmentation of the current system-theoretic and AI-based techniques for synthesis of decision and control laws with governing equations and inequality constraints that would model the properties of the materials for the purpose of damage representation and failure prognosis. The major challenge in this research is to extract the information from the material properties and then utilize this information in a mathematical form for synthesizing intelligent control and diagnostic systems.

控制系统、结构动力学和材料科学领域的跨学科研究旨在减轻机械结构的损伤和进行故障预测，作为复杂动态过程中智能控制和诊断的增强。该研究的关键思想是：（i）通过稍微降低系统动态性能可以显着提高使用寿命； (ii) 可以实时预测关键工厂部件即将发生的故障。具体而言，对即将发生的部件故障的预测对于增强工厂和人员安全、提高可用性和可操作性以及降低运营成本至关重要。随着材料科学和技术的不断发展，损伤减轻控制和故障预测的设计方法必须能够轻松地结合材料特性的适当表示。这需要增强当前的系统理论和基于人工智能的技术，以综合决策和控制律以及控制方程和不等式约束，对材料的特性进行建模，以进行损伤表示和故障预测。这项研究的主要挑战是从材料特性中提取信息，然后以数学形式利用这些信息来综合智能控制和诊断系统。