Evaluation of fatigue cracks using nonlinearities of acousto-ultrasonic waves acquired by an active sensor network

被引:105
作者
Zhou, Chao [1 ,2 ]
Hong, Ming [1 ]
Su, Zhongqing [1 ]
Wang, Qiang [1 ,3 ]
Cheng, Li [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China
[2] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China
[3] Nanjing Univ Post & Telecommun, Coll Automat, Nanjing 210023, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
DAMAGE IDENTIFICATION; LAMB WAVES; PART II; LOCALIZATION; SPECTROSCOPY; PROPAGATION;
D O I
10.1088/0964-1726/22/1/015018
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
There has been increasing interest in using the nonlinear features of acousto-ultrasonic (AU) waves to detect damage onset (e. g., micro-fatigue cracks) due to their high sensitivity to damage with small dimensions. However, most existing approaches are able to infer the existence of fatigue damage qualitatively, but fail to further ascertain its location and severity. A damage characterization approach, in conjunction with the use of an active piezoelectric sensor network, was established, capable of evaluating fatigue cracks in a quantitative manner (including the co-presence of multiple fatigue cracks, and their individual locations and severities). Fundamental investigations, using both experiment and enhanced finite element analysis dedicated to the simulation of nonlinear AU waves, were carried out to link the accumulation of nonlinearities extracted from high-order AU waves to the characteristic parameters of a fatigue crack. A probability-based diagnostic imaging algorithm was developed, facilitating an intuitive presentation of identification results in images. The approach was verified experimentally by evaluating multi-fatigue cracks near rivet holes of a fatigued aluminum plate, showing satisfactory precision in characterizing real, barely visible fatigue cracks. Compared with existing methods, this approach innovatively (i) uses permanently integrated active sensor networks, conducive to automatic and online health monitoring; (ii) characterizes fatigue cracks at a quantitative level; (iii) allows detection of multiple fatigue cracks; and (iv) visualizes identification results in intuitive images.
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页数:12
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