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.
