Sensor placement strategy to inform decisions
Name
MaininiWillcox_Aviation2017.pdf
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1.02 MB
Format
Adobe PDF
Checksum (MD5)
712a008cca3e2dc92ac7a5ab606de534
Author(s)
Willcox, Karen E
Mainini, Laura
Date Issued
June 2017
Journal
18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
Publisher
American Institute of Aeronautics and Astronautics
Citation
Mainini, Laura, and Karen E. Willcox. "Sensor Placement Strategy to Inform Decisions." 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 5-9 June, 2017, Denver, Colorado, American Institute of Aeronautics and Astronautics, 2017.
Version
Author's final manuscript
Abstract
This paper introduces a computational strategy to determine optimal sets of sensor locations to support real-time operational decisions. We exploit unsupervised learning strategies (specifically self-organizing maps) to identify the most informative locations to place sensors. The sensor placement procedure is then combined with a Multi-Step-Reduced Order
Modeling approach that exploits the low-dimensional map between the sparse sensed data and the decisions at hand. The approach is demonstrated for the real-time assessment of an unmanned aircraft wing panel undergoing structural degradation. For this application, we compare the optimal sets of sensor locations with random placements for a variety of sensor availabilities. By adopting our placement strategy, we achieve improvements in accuracy and robustness of capability predictions, even when measured data are sparse and cover less than 10% of the reference data.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2017-3820
