Sensors: GOALI: Networked Estimation and Decision Computing for Structural Health Monitoring

传感器：GOALI：用于结构健康监测的网络估计和决策计算

• 批准号: 0529426
• 负责人: Stephen Boyd
• 金额: $ 28.01万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0529426&HistoricalAwards=false
• 关键词: Sensors; GOALI; Networked; Estimation; Decision

The objective of the proposed project is to develop decision and control theory, state estimationalgorithms, and mathematical tools for networked sensors in a system-level application area ofstructural health monitoring (SHM). We will develop distributed computing algorithms for diagnosticand prognostic interpretation of the spatio-temporal data. The initial focus is on aerospaceapplications but the research results will be suffciently general to be used in many other areas. Thealgorithms will use embedded optimization for accurate estimation of the evolving spatio-temporalpattern of the damage. The computing will be scalable to very large sensor arrays.The proposed approach is to develop an innovative engineering application in aerospaceSHM area through industrial collaboration and then generalizing the results. Existing SHM workis aimed at developing sensors, data interrogation methods, and low-level signal processing toobtain a single damage estimate array. This project will develop system-level statistical algorithmsfor estimation of structural health state, monitoring, and decision support based on series of spatialdata arrays obtained from distributed networked sensors. The algorithms will allow for scalableand reconfigurable distributed parallel computing implementation.Intellectual merit. We propose to develop algorithms for spatio-temporal estimation of structuraldamage that can be scaled to very large spatial data arrays through networked computing.The engineering need and potential for practical impact come from collaboration with Honeywell.The place of the problem formulation with respect to existing work is characterized by thefollowing highlights: (i) We concentrate on mathematical processing of the existing sensor data,rather than developing new sensing systems; (ii) We focus on spatio-temporal processing of thedata, while the existing SHM literature is focused on obtaining a single spatial pattern of thedamage. Modeling and trending the temporal evolution of the damage will enable prognostics;(iii) We pursue system scalability through constrained optimization-based estimation algorithmsimplemented through distributed computing. Existing distributed algorithms are for optimizinga small number of parameters. We will estimate large spatio-temporal arrays; (iv) The decisionsystem design will include uncertainty, missing data, self-healing capability etc, as opposed toconsidering an idealized and simplified mathematical problem.The main steps and challenges in the proposed work include: 1. Formulating models of spatiotemporalevolution of the damage and criteria of estimation optimality. 2. Developing solutions ofoptimal statistical estimation and detection problems through embedded convex optimization of alog-likelihood index. 3. Developing scalable algorithms through networked distributed computing.Broad impact. Algorithmic and system engineering approaches in this proposal would accelerateindustrial adoption of the SHM technology by reducing false alarms and improving scalability.This would enable condition-based maintenance of structures reducing airline flight delays and improvingmilitary aircraft readiness. Other aerospace applications include next generation spacevehicles and space habitats. The transition into aerospace engineering practice will be facilitatedby the collaboration with Honeywell. The research would help in developing large networked sensingsystems in other areas. These include SHM of civil structures (buildings, bridges, etc), marineand ground vehicles, and industrial plants. The results could be also extended to sensing andhealth assessment in medicine, geophysics (e.g., earthquake related damage), and bio or ecosystemdamage.We will take steps towards developing graduate training in the area of decision technology for thenetworked sensors by including the research results in the curriculum of existing courses. Duringthe planned university visit, the industrial co-PI will teach an industrial-oriented graduate-levelcourse. The students involved with the project or attending the courses will have an opportunityto work as interns at Honeywell, which will provide them with an additional practical training.1

拟议项目的目的是开发决策和控制理论，国家估计值以及结构性健康监测（SHM）系统级应用领域的网络传感器的数学工具。我们将开发分布式计算算法，用于诊断时空数据的诊断和预后解释。最初的重点是航空涂抹术，但研究结果将是足够的一般性，可以在许多其他领域使用。 Thealgorithms将使用嵌入式优化来准确估计损伤的时空时空。该计算将可扩展到非常大的传感器阵列。拟议的方法是通过工业协作在Aerospaceshm地区开发创新的工程应用，然后概括结果。现有的SHM Workis旨在开发传感器，数据询问方法和低级信号处理，以实现单个损伤估算阵列。该项目将基于从分布式网络传感器获得的一系列SpatialData阵列来开发系统级统计算法，以估计结构健康状态，监测和决策支持。该算法将允许可缩放的可重新配置分布式并行计算实现。我们建议开发用于结构性污染时空估计的算法，可以通过网络计算将其缩放到非常大的空间数据阵列。工程的需求和实践影响来自与霍尼韦尔的合作。工作的特征是亮点：（i）我们专注于现有传感器数据的数学处理，而不是开发新的传感系统； （ii）我们专注于Thedata的时空处理，而现有的SHM文献则侧重于获得单一的空间模式。建模和趋势损害的时间演变将使预后能够实现预后；（iii）我们通过通过分布式计算的基于优化的估计算法来实现系统可扩展性。现有的分布式算法用于优化少数参数。我们将估计大型时空阵列； （iv）决策系统设计将包括不确定性，缺少数据，自我修复能力等，而不是对理想化和简化的数学问题进行反对。拟议的工作中的主要步骤和挑战包括：1。对损害和损害和损害和损害的时空模型制定模型估计最佳性的标准。 2。通过嵌入的Alog-Fikelihood指数的凸凸优化，开发出最佳的统计估计和检测问题的解决方案。 3.通过网络分布式计算开发可扩展算法。该提案中的算法和系统工程方法将通过降低错误警报和提高可扩展性来加速工业工业的采用。这将促进基于条件的结构维护，以减少航空公司的飞行延误和改善军事飞机就绪。其他航空航天的应用包括下一代太空保险和太空栖息地。与霍尼韦尔的合作将促进向航空工程实践的过渡。该研究将有助于开发其他领域的大型网络轰动系统。其中包括民用结构（建筑物，桥梁等），Marineand地面车辆和工业工厂的SHM。结果也可以扩展到感应医学，地球物理学（例如地震相关损害）和生物或生态系统damage的感知和健康评估。我们将采取步骤在决策技术领域开发研究生培训，以通过在决策技术领域中，通过在内，通过在内的研究结果包括研究结果。现有课程的课程。在计划的大学访问期间，工业co-Pi将教授面向工业的研究生级别。参与该项目或参加课程的学生将有机会在霍尼韦尔（Honeywell）实习生，这将为他们提供额外的实践培训。1