Current control and performance requirements for large-aperture deployable structures call for precise displacement control, with some tolerances approaching micron levels. Given that strain gages are one of the most economically-deployed sensor architectures, we explored two methods for reconstructing displacement from a distributed strain sensing array. One method linearly maps displacement fields to local strains in a supervised learning mode. After loading the system with sufficient cases, a matrix can be established to approach the approximate displacement-strain relationship. The other method is based on linear regression of generalized basis function projections, typically mode shapes. Results of these two approaches are compared for accuracy, robustness, training time, and real-time feasibility. The second method has higher accuracy due to natural modal behaviors, while the first method is feasible if large amount of training cases and measuring points are available.

Date Published: 8 April 2008
PDF: 12 pages
Proc. SPIE 6932, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008, 69320H (8 April 2008); doi: 10.1117/12.775931

Published in SPIE Proceedings Vol. 6932:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008
Masayoshi Tomizuka, Editor(s)