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http://dx.doi.org/10.12989/sss.2014.14.3.397

A new method to detect cracks in plate-like structures with though-thickness cracks  

Xiang, Jiawei (College of Mechanical & Electrical Engineering, Wenzhou University)
Nackenhorst, Udo (Institute of Mechanics and Computational Mechanics, Leibniz Universitat Hannover)
Wang, Yanxue (School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology)
Jiang, Yongying (College of Mechanical & Electrical Engineering, Wenzhou University)
Gao, Haifeng (College of Mechanical & Electrical Engineering, Wenzhou University)
He, Yumin (College of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology)
Publication Information
Smart Structures and Systems / v.14, no.3, 2014 , pp. 397-418 More about this Journal
Abstract
In this paper, a simple two-step method for structural vibration-based health monitoring for beam-like structures have been extended to plate-like structures with though-thickness cracks. Crack locations and severities of plate-like structures are detected using a hybrid approach. The interval wavelet transform is employed to extract crack singularity locations from mode shape and support vector regression (SVR) is applied to predict crack serviettes form crack severity detection database (the relationship of natural frequencies and crack serviettes) using several natural frequencies as inputs. Of particular interest is the natural frequencies estimation for cracked plate-like structures using Rayleigh quotient. Only the natural frequencies and mode shapes of intact structures are needed to calculate the natural frequencies of cracked plate-like structures using a simple formula. The crack severity detection database can be easily obtained with this formula. The hybrid method is investigated using numerical simulation and its validity of the usage of interval wavelet transform and SVR are addressed.
Keywords
plate-like structures; Rayleigh quotient; interval wavelet transform; natural frequency estimation; crack detection; support vector regression;
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Times Cited By KSCI : 9  (Citation Analysis)
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