A robust signal processing method for quantitative high-cycle fatigue crack monitoring using soft elastomeric capacitor sensors
AffiliationUniv Arizona, Dept Civil Engn & Engn Mech
KeywordsFatigue crack detection
structural health monitoring
power spectral density
MetadataShow full item record
PublisherSPIE-INT SOC OPTICAL ENGINEERING
CitationXiangxiong Kong, Jian Li, William Collins, Caroline Bennett, Simon Laflamme, Hongki Jo, "A robust signal processing method for quantitative high-cycle fatigue crack monitoring using soft elastomeric capacitor sensors", Proc. SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 101680B (12 April 2017); doi: 10.1117/12.2260364; http://dx.doi.org/10.1117/12.2260364
Rights© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractA large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is capable to monitor strain changes over a large structural surface and undergo large deformations under cracking. Previous tests verified that the SEC can detect and localize fatigue cracks under low-cycle fatigue loading. In this paper, we further investigate the SEC's capability for monitoring high-cycle fatigue cracks, which are commonly seen in steel bridges. The peak-to-peak amplitude (pk-pk amplitude) of the SEC measurement is proposed as an indicator of crack growth. This technique is is robust and insensitive to long-term capacitance drift. To overcome the difficulty of identifying the pk-pk amplitude in time series due to high signal-to-noise ratio, a signal processing method is established. This method converts the measured SEC capacitance and applied load to power spectral densities (PSD) in the frequency domain, such that the pk-pk amplitudes of the measurements can be accurately extracted. Finally, the performance of this method is validated using a fatigue test of a compact steel specimen equipped with a SEC. Results show that the crack growth under high-cycle fatigue loading can be successfully monitored using the proposed signal processing method.
VersionFinal published version
SponsorsTransportation Pooled Fund [TPF-5(328)]; Iowa Department of Transportation [RT454-494]