Data to decisions: Real-time structural assessment from sparse measurements affected by uncertainty
Name
Willcox_Data to decisions.pdf
Description
Accepted version
Size
1.45 MB
Format
Adobe PDF
Checksum (MD5)
afc239d6c674e5defd894f04724faa77
Author(s)
Mainini, Laura
Willcox, Karen E
Date Issued
April 2017
Journal
Computers & Structures
Publisher
Elsevier BV
Citation
Mainini, Laura and Willcox, Laura. "Data to decisions: Real-time structural assessment fromsparse measurements affected by uncertainty." Computers & Structures 182 (April 2017): 296-312 © 2016 Elsevier Ltd.
Version
Author's final manuscript
Abstract
This paper proposes a data-to-decisions framework—a methodology and a computational strategy—to assist real-time decisions associated with structural monitoring and informed by incomplete, noisy measurements. The data-to-decision structural assessment problem is described in terms of sensor data measurements (such as strain components) and system capabilities (such as failure indices). A MultiStep Reduced-Order Modeling (MultiStep-ROM) strategy tackles the time-critical problem of estimating capabilities from measured data. The methodology relies on an offline-online decomposition of tasks, and combines reduced-order modeling, surrogate modeling, and clustering techniques. The performance of the approach is studied for the case of uncertain measurements arising from spatially distributed sensors over a wing panel. Both sensor noise and sensor spatial sparsity affect the quality of the information available online. The discussion is supported by three investigations that explore the efficiency of the online procedure for multiple combinations of quantity and quality of sensed data. The method is demonstrated for an unmanned aerial vehicle composite wing panel undergoing local degradation of its structural properties. Keywords: data-driven reduced-order modeling; data-driven structural assessment; data-to-decisions; sparse and uncertain measurements; real-time capability assessment; self-aware vehicle.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
MIT-SUTD Collaboration Office
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.compstruc.2016.12.007
