Lamb Wave Visualization of Microcrack Growth Based on Acoustic Nonlinearity Aware-Dictionary and Gradient Projection Sparse Representation

The visualization of the microcrack growth is vital for structural integrity evaluation and remaining useful life prediction in the industrial field, but there is a scarcity of practical and low-cost techniques today to detect and image microcracks at the early stage of damage. A novel framework for visualizing fatigue crack is developed, integrating the nonlinear ultrasonic Lamb wave phased array technology with the sparse representation algorithm. A designed nonlinear phased array is utilized in conjunction with the introduction of pulse inversion technology for enhancing weak nonlinear ultrasonic features. Based on the inherent dispersion nature of Lamb waves and second harmonic propagation features, a kind of acoustic nonlinearity aware-dictionary is designed, and the gradient projection sparse representation algorithm is employed to reconstruct the weak second harmonic waves embedded in the original time domain signals. The experimental validation, derived from the imaging result of the fatigue crack at different fatigue cycles and the fatigue crack growth path visualization under structural health monitoring conditions, demonstrates the capability of the developed framework to extract the weak nonlinear features generated by the fatigue crack. Some essential parameters can be calculated based on the obtained growth path, thus laying the foundation for future assessment of structural integrity using fracture mechanics theory.

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Publication Details

Journal
IEEE Transactions on Industrial Informatics
Published
2024-12-19
DOI
https://doi.org/10.1109/tii.2024.3514160
Citations
7
Primary Topic
Ultrasound Imaging and Elastography
Type
article
Field-Weighted Citation Impact
2.83

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article

Lamb Wave Visualization of Microcrack Growth Based on Acoustic Nonlinearity Aware-Dictionary and Gradient Projection Sparse Representation

Lishuai Liu, Fu‐Zhen Xuan, Haiming Xu, Jiachen Zhou et al.
7 citations
IEEE Transactions on Industrial Informatics
Ultrasound Imaging and Elastography
2.83
article

Lamb Wave Visualization of Microcrack Growth Based on Acoustic Nonlinearity Aware-Dictionary and Gradient Projection Sparse Representation

Lishuai Liu, Fu‐Zhen Xuan, Haiming Xu, Jiachen Zhou, Yanxun Xiang, Xuan Li, Zheng Hu, Siyuan Peng
article en
7 citations

Abstract

The visualization of the microcrack growth is vital for structural integrity evaluation and remaining useful life prediction in the industrial field, but there is a scarcity of practical and low-cost techniques today to detect and image microcracks at the early stage of damage. A novel framework for visualizing fatigue crack is developed, integrating the nonlinear ultrasonic Lamb wave phased array technology with the sparse representation algorithm. A designed nonlinear phased array is utilized in conjunction with the introduction of pulse inversion technology for enhancing weak nonlinear ultrasonic features. Based on the inherent dispersion nature of Lamb waves and second harmonic propagation features, a kind of acoustic nonlinearity aware-dictionary is designed, and the gradient projection sparse representation algorithm is employed to reconstruct the weak second harmonic waves embedded in the original time domain signals. The experimental validation, derived from the imaging result of the fatigue crack at different fatigue cycles and the fatigue crack growth path visualization under structural health monitoring conditions, demonstrates the capability of the developed framework to extract the weak nonlinear features generated by the fatigue crack. Some essential parameters can be calculated based on the obtained growth path, thus laying the foundation for future assessment of structural integrity using fracture mechanics theory.

IEEE Transactions on Industrial InformaticsVol. 21(3)
East China University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Ultrasound Imaging and Elastography
2.83
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