The objective of the proposed project is to develop decision and control theory, state estimation algorithms, and mathematical tools for networked sensors in a system-level application area of structural health monitoring (SHM). We will develop distributed computing algorithms for diagnostic and prognostic interpretation of the spatio-temporal data. The initial focus is on aerospace applications but the research results will be suffciently general to be used in many other areas. The algorithms will use embedded optimization for accurate estimation of the evolving spatio-temporal pattern of the damage. The computing will be scalable to very large sensor arrays. The proposed approach is to develop an innovative engineering application in aerospace SHM area through industrial collaboration and then generalizing the results. Existing SHM work is aimed at developing sensors, data interrogation methods, and low-level signal processing to obtain a single damage estimate array. This project will develop system-level statistical algorithms for estimation of structural health state, monitoring, and decision support based on series of spatial data arrays obtained from distributed networked sensors. The algorithms will allow for scalable and reconfigurable distributed parallel computing implementation. Intellectual merit. We propose to develop algorithms for spatio-temporal estimation of structural damage 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 the following 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 the data, while the existing SHM literature is focused on obtaining a single spatial pattern of the damage. Modeling and trending the temporal evolution of the damage will enable prognostics; (iii) We pursue system scalability through constrained optimization-based estimation algorithms implemented through distributed computing. Existing distributed algorithms are for optimizing a small number of parameters. We will estimate large spatio-temporal arrays; (iv) The decision system design will include uncertainty, missing data, self-healing capability etc, as opposed to considering an idealized and simplified mathematical problem. The main steps and challenges in the proposed work include: 1. Formulating models of spatiotemporal evolution of the damage and criteria of estimation optimality. 2. Developing solutions of optimal statistical estimation and detection problems through embedded convex optimization of a log-likelihood index. 3. Developing scalable algorithms through networked distributed computing. Broad impact. Algorithmic and system engineering approaches in this proposal would accelerate industrial 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 improving military aircraft readiness. Other aerospace applications include next generation space vehicles and space habitats. The transition into aerospace engineering practice will be facilitated by the collaboration with Honeywell. The research would help in developing large networked sensing systems in other areas. These include SHM of civil structures (buildings, bridges, etc), marine and ground vehicles, and industrial plants. The results could be also extended to sensing and health assessment in medicine, geophysics (e.g., earthquake related damage), and bio or ecosystem damage. We will take steps towards developing graduate training in the area of decision technology for the networked sensors by including the research results in the curriculum of existing courses. During the planned university visit, the industrial co-PI will teach an industrial-oriented graduate-level course. The students involved with the project or attending the courses will have an opportunity to work as interns at Honeywell, which will provide them with an additional practical training. 1
