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Proceedings Paper

Sensor topologies for application of strain energy damage diagnostics and prognostication
Author(s): Timothy P. Kernicky; Matthew J. Whelan
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Paper Abstract

Structural health monitoring methodologies devised over the past two decades have increasing shown improved robustness in capability to identify the onset of structural damage and locate the source of the damage. However, the pathway to prognostication and life-cycle assessment through structural health monitoring remains stalled by a lack of success in the diagnostic step of experimentally quantifying the severity of damage in suitable, engineering quantities. Of the methods devised, strain energy approaches have demonstrated not only strength in identifying and localizing structural damage but also uniquely provide a theoretical basis for quantifying damage through measurement of relative stiffness loss in individual members. Conventional applications of strain energy methods use distributed accelerometers, often being single-axis and oriented in the same direction. The limited degrees-of-freedom measured limits the modal parameter extraction to a reduced subset and yields only partial reconstruction of the strain energy in the system. Furthermore, it has been shown experimentally and proven analytically that improvement in strain energy methods through increased spatial density of the sampling array is constrained by the effect of measurement noise on the accuracy of the numerical computations. In this paper, alternative sensor topologies are explored for improving the reconstruction of strain energy estimates. An experimental component of the research includes strain energy estimates for a fixed-free beam heavily instrumented with accelerometers. Prescribed damage is incrementally applied to the beam to permit a basis for comparison amongst the sensor topologies in addressing the damage diagnostics problem with specific emphasis on quantification of severity through stiffness loss.

Paper Details

Date Published: 30 March 2012
PDF: 11 pages
Proc. SPIE 8346, Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2012, 83461B (30 March 2012); doi: 10.1117/12.915400
Show Author Affiliations
Timothy P. Kernicky, The Univ. of North Carolina at Charlotte (United States)
Matthew J. Whelan, The Univ. of North Carolina at Charlotte (United States)


Published in SPIE Proceedings Vol. 8346:
Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2012
Theodore E. Matikas, Editor(s)

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