Fatigue damage localization using time-domain features extracted from nonlinear Lamb waves

Ming Hong; Zhongqing Su; Ye Lu; Li Cheng

doi:10.1117/12.2044031

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9 March 2014 Fatigue damage localization using time-domain features extracted from nonlinear Lamb waves

Ming Hong, Zhongqing Su, Ye Lu, Li Cheng

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Proceedings Volume 9064, Health Monitoring of Structural and Biological Systems 2014; 906405 (2014) https://doi.org/10.1117/12.2044031
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2014, San Diego, California, United States

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.

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Ming Hong, Zhongqing Su, Ye Lu, and Li Cheng "Fatigue damage localization using time-domain features extracted from nonlinear Lamb waves", Proc. SPIE 9064, Health Monitoring of Structural and Biological Systems 2014, 906405 (9 March 2014); https://doi.org/10.1117/12.2044031

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