High-Resolution Crack Localization Approach Based on Diffraction Wave

Mu, Weilei; Sun, Jiangang; Liu, Guijie; Wang, Shuqing

doi:10.3390/s19081951

Cited by 13 publications

(4 citation statements)

References 20 publications

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“…According to Nyquist's theory, the acquisition frequency must be at least twice the maximum frequency component. In the simulation and experiment, the acquisition frequency was 2.5 MHz for full frequency range acquisition [21].…”

Section: Methodology Verification Numerical Simulationmentioning

confidence: 99%

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition

Gao

Liu

2021

Sensors

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Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localization with respect to resolution. To detect damage with fewest sensors and high resolution, a method, using only two piezoelectric transducers and based on orthogonal matching pursuit (OMP) decomposition, was proposed. First, an OMP-based decomposition and dispersion removal algorithm is introduced, which is capable of separating wave packets of different propagation paths and removing the dispersion part successively. Then, two simulation signals, with nonoverlapped and overlapped wave packets, are employed to verify the proposed method. Thereafter, with the proposed algorithm, the wave packets reflected from the defect and edge are all separated. Finally, a sparse sensor array with only two transducers succeeds in localizing the defect. The experimental results show that the OMP-based algorithm is beneficial for resolution improvement and transducer usage reduction.

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Section: Methodology Verification Numerical Simulationmentioning

confidence: 99%

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition

Gao

Liu

2021

Sensors

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“…When a wave comes across the damage, the signal scatters, resulting in the formation of minor wave packets, as shown in Figures 4(a) and 4(b) [20]. Mu et al [21] have given a damage localization approach based on diffracted wave property of the ultrasonic wave. In this paper, Carbon Steel, Q235, of 1250 mm (length) × 1250 mm (width) × 2 mm (thickness) is taken for analysis.…”

Section: The Ultrasonic Guided Wavementioning

confidence: 99%

The Behaviour of Wave Propagation for Structural System Identification: A Comparative Study

Patel

Dewangan

2022

IJE

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The way a wave behaves, while propagating across a medium, varies with the wave type and the medium. So, knowledge of the behaviour of a wave in a system with a different form of damage, and behaviour of different types of waves in a particular system is an essential prerequisite for almost all activities in structural system identification and mainly for damage detection and localization of damage. This paper presents a comparative study of various wave propagations, that has been done by researchers in various structural systems. Further, a numerical model of an isotropic plate using finite element is created both with and without damage. The behaviour of waves has been studied. Finally, the comparative result is shown. This paper offers a new perspective for ongoing research by providing the most recent developments, difficulties, and prospects of wave propagation behaviours for damage detection and localization in the commonly used structural systems and structural elements. While propagating through different structural systems and components, the most used waves, which are (a) Shear wave, (b) Rayleigh wave, (c) Ultrasonic wave and (d) Lamb wave, have been thoroughly investigated. Along with several difficult problems for future growth, the summarized observations are provided.

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“…It also improves the real-time performance of offshore platform monitoring [20]. They also proposed for the first time to locate the crack tip by diffraction wave, which provides a better method for the analysis of crack length and angle [21]. Thus far, most research focuses on the localization of through-thickness holes.…”

Section: Introductionmentioning

confidence: 99%

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

Qi¹,

et al. 2020

JMSE

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The reconstruction algorithm for the probabilistic inspection of damage (RAPID) is aimed at localizing structural damage via the signal difference coefficient (SDC) between the signals of the present and reference conditions. However, tomography is only capable of presenting the approximate location and not the length and angle of defects. Therefore, a new quantitative evaluation method called the multiple back propagation neural network (Multi-BPNN) is proposed in this work. The Multi-BPNN employs SDC values as input variables and outputs the predicted length and angle, with each output node depending on an individual hidden layer. The cracks of different lengths and angles at the center weld seam of offshore platforms are simulated numerically. The SDC values of the simulations and experiments were normalized for each sample to eliminate external interference in the experiments. Then, the normalized simulation data were employed to train the proposed neural network. The results of the simulations and experimental verification indicated that the Multi-BPNN can effectively predict crack length and angle, and has better stability and generalization capacity than the multi-input to multi-output back propagation neural network.

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High-Resolution Crack Localization Approach Based on Diffraction Wave

Cited by 13 publications

References 20 publications

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition

The Behaviour of Wave Propagation for Structural System Identification: A Comparative Study

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

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