Identification of fatigue crack under vibration by nonlinear guided waves

Lee, Yu Fung; Lu, Ye

doi:10.1016/j.ymssp.2021.108138

Cited by 27 publications

(4 citation statements)

References 27 publications

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Section: Introductionmentioning

confidence: 99%

“…The Lamb wave-based SHM technology relies on the interaction between elastic waves and damage in the large-scale plate or shell-like-engineering structures such as pressure vessels, aircraft skins, and so forth for damage localization by analyzing scattered, transmitted, or diffracted waves. [1][2][3] Traditional damage localization methods based on Lamb waves generally need to establish the damage index (DI) representing the changes of the state of the structure. 4 In general, the DI should be designed to be sensitive to damage signatures, and the accuracy of damage localization depends on the validity of the DI.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Liu

et al. 2023

Structural Health Monitoring

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Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Liu

et al. 2023

Structural Health Monitoring

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“…Masserey [19][20][21][22] conducted studies on a special high-frequency ultrasonic guided wave and found its high applicability in early fatigue damage detection and fatigue crack growth monitoring in aircraft components. Lee [23,24] achieved quantitative detection of fatigue cracks in steel joints under vibration using nonlinear ultrasound.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Parameters of Magnetostrictive Guided Wave Testing for Fatigue Damage of Steel Strands

Chen

et al. 2023

Materials

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Steel strands are widely used in structures such as bridge cables, and their integrity is critical to keeping these structures safe. A steel strand is under the working condition of an alternating load for a long time, and fatigue damage is unavoidable. It is necessary to find characteristic parameters for evaluating fatigue damage. In this study, nonlinear coefficients and attenuation coefficients were employed to evaluate fatigue damage based on magnetostrictive guided wave testing. Unlike pipe and steel wire structures, there is a phenomenon of a notch frequency when guided waves propagate in steel strands. The influence of the notch frequency on the nonlinear coefficient and attenuation coefficient is discussed. The relationship between the nonlinear coefficient, attenuation coefficient, and cyclic loading times was obtained through experiments. The amplitudes of the nonlinear coefficient and attenuation coefficient both increased with the increase in cyclic loading times. The experiments also showed the effectiveness of using these two characteristic parameters to evaluate fatigue damage.

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“…The most ordinary checks performed in the industrial field to verify the integrity of the structures are based on ultrasounds, eddy currents, penetrating liquids, and magnetic particles. If the ultrasound measurement in linear condition constituted a standard and well-known experimental technique, the use of nonlinear ultrasound techniques is relatively uncommon, but it is significantly increasing due to its high sensitivity to micro-fatigue cracks compared to conventional methods [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

In Situ Fatigue Damage Monitoring by Means of Nonlinear Ultrasonic Measurements

Saponaro,

Nobile

2023

Metals

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In the present work, the results of acoustic nonlinear response of ultrasonic wave propagation when monitoring the progress of damage induced by fatigue on notched C45 carbon steel specimens have been reported. Two ultrasound probes were fixed to the specimens during the tests. The input signal was sinusoidal type, while the corresponding ultrasound response signal was acquired and recorded at each stage of the test by means of a digital oscilloscope. A nonlinear frequency study was performed on the acquired data to evaluate the change in the second- and third-order nonlinearity coefficients of β1 and β2, respectively, on the tested specimens. Ultrasonic results were correlated to plastic strain at the notch tip in the initial phases of fatigue and stiffness degradation. The results showed a significant increase in second-order nonlinearity β1 in the early stages of fatigue life. Subsequently, starting from about 30–40% of the fatigue life, the nonlinearity of β1 increases. Before final failure, from 80 to 85% of fatigue life, the second-order nonlinearity further increases in the crack propagation stages. The nonlinear parameter of the third-order β2 was less sensitive to damage than the parameter β1, showing a rapid increase only starting from approximately 80 to 85% of the fatigue life. The proposed method proved to be valid for detective damage induced by fatigue and to predict the lifetime of metal materials.

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Identification of fatigue crack under vibration by nonlinear guided waves

Cited by 27 publications

References 27 publications

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

Characteristic Parameters of Magnetostrictive Guided Wave Testing for Fatigue Damage of Steel Strands

In Situ Fatigue Damage Monitoring by Means of Nonlinear Ultrasonic Measurements

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