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Proceedings Paper

Fatigue damage localization using time-domain features extracted from nonlinear Lamb waves
Author(s): Ming Hong; Zhongqing Su; Ye Lu; Li Cheng
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Paper Abstract

Nonlinear guided waves are sensitive to small-scale fatigue damage that may hardly be identified by traditional techniques. A characterization method for fatigue damage is established based on nonlinear Lamb waves in conjunction with the use of a piezoelectric sensor network. Theories on nonlinear Lamb waves for damage detection are first introduced briefly. Then, the ineffectiveness of using pure frequency-domain information of nonlinear wave signals for locating damage is discussed. With a revisit to traditional gross-damage localization techniques based on the time of flight, the idea of using temporal signal features of nonlinear Lamb waves to locate fatigue damage is introduced. This process involves a time-frequency analysis that enables the damage-induced nonlinear signal features, which are either undiscernible in the original time history or uninformative in the frequency spectrum, to be revealed. Subsequently, a finite element modeling technique is employed, accounting for various sources of nonlinearities in a fatigued medium. A piezoelectric sensor network is configured to actively generate and acquire probing Lamb waves that involve damageinduced nonlinear features. A probability-based diagnostic imaging algorithm is further proposed, presenting results in diagnostic images intuitively. The approach is experimentally verified on a fatigue-damaged aluminum plate, showing reasonably good accuracy. Compared to existing nonlinear ultrasonics-based inspection techniques, this approach uses a permanently attached sensor network that well accommodates automated online health monitoring; more significantly, it utilizes time-domain information of higher-order harmonics from time-frequency analysis, and demonstrates a great potential for quantitative characterization of small-scale damage with improved localization accuracy.

Paper Details

Date Published: 9 March 2014
PDF: 13 pages
Proc. SPIE 9064, Health Monitoring of Structural and Biological Systems 2014, 906405 (9 March 2014); doi: 10.1117/12.2044031
Show Author Affiliations
Ming Hong, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Monash Univ. (Australia)
Zhongqing Su, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Ye Lu, Monash Univ. (Australia)
Li Cheng, Hong Kong Polytechnic Univ. (Hong Kong, China)


Published in SPIE Proceedings Vol. 9064:
Health Monitoring of Structural and Biological Systems 2014
Tribikram Kundu, Editor(s)

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