A novel vibration based breathing crack localization technique using a single sensor measurement

被引:23
|
作者
Prawin, J. [1 ]
Lakshmi, K. [1 ]
Rao, A. Rama Mohan [1 ]
机构
[1] CSIR, Struct Engn Res Ctr, CSIR Campus, Chennai, TN, India
关键词
Breathing crack; Superharmonics; Spectral density function; Nonlinearity; Curvature; Singular spectrum analysis; Pairwise eigenvalues; Zero strain energy nodes; Particle swarm optimisation; Nelder Mead algorithm; SINGULAR SPECTRUM ANALYSIS; EULER-BERNOULLI BEAM; DAMAGE DETECTION; NONLINEAR IDENTIFICATION; DELAMINATION;
D O I
10.1016/j.ymssp.2018.12.019
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Structural damages such as a fatigue-breathing crack can result in nonlinear dynamical signatures that can significantly enhance their detection. Majority of the existing vibration based breathing crack diagnosis techniques demands rather a dense sensor network in order to detect, precisely locate the spatial location and characterize the breathing crack as the change in the dynamic characteristics of a structure with breathing crack is much smaller when compared to open cracks of the same magnitude. Further, the quality of identification improves with the increase in the number of sensors. In this paper, a novel vibration-based damage detection technique based on the zero strain energy nodes concept is proposed for the first time to identify the exact spatial location of the breathing crack using a single sensor measurement. Two different procedures based on sweep sine and harmonic excitation are outlined for breathing crack localization. Multi-level Singular Spectrum Analysis (M-SSA) is used in the present work to conclude about the presence of nonlinear behaviour of the structure and also for the identification of frequencies at which the cracked structure behaves linearly. Numerical and experimental investigations presented in this paper clearly establish that the proposed approach has the ability to identify single and as well as multiple breathing cracks present anywhere in the structure even with noise contaminated measurements. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:117 / 138
页数:22
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