A feasibility study on monitoring of weld fatigue crack growth based on coda wave interferometry (CWI)

Zhou, Detian; Huo, Linsheng; Chen, Dongdong; Song, Gangbing

doi:10.1088/1361-665x/ac1304

Cited by 10 publications

(5 citation statements)

References 54 publications

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“…Due to the small thickness, the dispersed modes under 130 kHz excitation frequency only contain S 0 and A 0 mode, implying a clean and clear waveform in an infinite, unbounded plate. However, for the present study with a finite plate, the received signal is a superposition of both waveforms along the sensing path and the backscattered ones (coda waves) from the boundaries, and the coda waves have been demonstrated containing rich information for damage detection [67,68]. The sampling frequency was 50 MHz, and we deliberately elongated the sampling length (20 000 sampling points per waveform) in the time domain to obtain a chaotic but highly repeatable waveform for each sensor pair having adequate coda wave information included.…”

Section: Methodsmentioning

confidence: 92%

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Zhou¹,

Chen²,

Ni³

et al. 2021

Smart Mater. Struct.

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Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to irregular structure or boundary reflections. However, not too much research work has been conducted to thoroughly combine sensor networks with DL. Existing research using DL approaches is mainly used to train and interpret waveforms from isolated sensor pairs. The topological structure of sensor layout and sensor-damage relative positions are hardly considered in the data-driven process. Motivated by these concerns, this study offers a first-of-its-kind perspective to interpret UGW data collected from a sensor network by mapping the physical sensor-damage layout into a graph, in which sensors and potential damages serve as graph vertices bearing heterogenous properties upon coming to UGWs and the process of UGW transmission between sensors are encapsulated as wavelike message passing between the vertices. A novel physics-informed end-to-end graph neural network model, named as WaveNet, was exquisitely and meticulously developed. By utilizing wave information and topological structure, WaveNet enables inference of multiple damages in terms of severity and location with satisfactory accuracy, even when the waveforms are chaotic, and the sensor arrangement is different at the training and testing stages. More importantly, beyond the SHM scenario, the present study is expected to enlighten new thinking on interconnecting physical wave propagation with virtual messaging passing in neural networks.

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Section: Methodsmentioning

confidence: 92%

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Zhou¹,

Chen²,

Ni³

et al. 2021

Smart Mater. Struct.

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“…Pitting corrosion defect in steel bar is localized change that will lead to distortion rather than global stretching or compressing of ultrasonic waveforms. Coda wave interferometry (CWI), proposed by Snider et al 28 to detect global changes in Elberton granite, was suitable for global damages detection and condition monitoring [42][43][44][45] but may fail in local damage detection because this technique relies on the assumption that the variations are global in the medium. Pacheco and Snieder 46 attempted to improve CWI for local change evaluation, but the modified CWI may lose effectiveness in very local variations.…”

Section: Coda Wave-based Monitoring Methodsmentioning

confidence: 99%

A novel method for steel bar all-stage pitting corrosion monitoring using the feature-level fusion of ultrasonic direct waves and coda waves

Sun

Zhang

Gao

et al. 2022

Structural Health Monitoring

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Corrosion monitoring of steel bars has drawn extensive attention in recent decades. Conventional ultrasonic method, utilizing direct waves to detect damage, is adequate for severe pitting corrosion but suffers from low sensitivity to incipient pitting corrosion. Coda wave technique, a very sensitive method to subtle changes in medium using later arrival wave packets, is innovatively introduced to monitor pitting corrosion of steel bars, especially in the early stages. The decorrelation coefficient (DC) values are calculated to quantify the variations of both direct waves and coda waves. To overcome the limitations of coda waves for severe pitting corrosion and remedy the low sensitivity of direct waves for incipient pitting corrosion, a feature-level data fusion strategy is proposed to integrate the two probing waves to monitor all-stage pitting corrosion of steel bars. The combination of direct waves and coda waves could exploit the complementary merits in various pitting corrosion configurations. The proposed feature-level fusion strategy of ultrasonic coda waves and direct waves intercepted from the same recorded signals opens a new perspective in all-stage pitting corrosion monitoring of steel bars and contributes a novel scheme for whole-process damage evaluation of structures.

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“…A coda wave is considered to be a superposition of all scattered and reflected waves induced by defects and structural boundaries in a waveguide. A subtle change in the medium causes accumulated changes in coda waves, leading to a higher sensitivity of the CWI method than methods based on first arrivals [20][21][22]. Based on the CWI, various applications have been reported for the monitoring of different types of defects like weld fatigue crack [21], early-stage bolt looseness [23], corrosion [24], etc.…”

Section: Introductionmentioning

confidence: 99%

A metamaterial-assisted coda wave interferometry method with nonlinear guided waves for local incipient damage monitoring in complex structures

Shan,

Liu,

Zhang

et al. 2024

Smart Mater. Struct.

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Nonlinear guided waves exhibit high sensitivity to material microstructural changes, thus attracting increasing attention for incipient damage monitoring applications. However, conventional nonlinear guided-wave-based methods suffer from two major deficiencies which hinder their applications: 1) mostly relying on the first arrivals of wave signals, they apply to limited inspection areas in simple structures in order to avoid wave reflections from structural discontinuities or boundaries; 2) they are prone to numerous deceptive nonlinear sources in the measurement system which might overwhelm damage-induced signal components. To tackle these challenges, we propose a metamaterial-assisted coda wave interferometry method using second harmonic Lamb waves, applicable to the monitoring of local incipient damage in complex structures. Embracing the metamaterial concept, a so-called meta-screen is designed, whose geometry and layout can be flexibly tailored to target specific inspection zones in a structure. Capitalizing on its customized bandgap features, the proposed meta-screen allows for the passing of fundamental waves while preventing the second harmonic components generated by deceptive nonlinear sources from penetrating into the inspection area. Through numerical analyses on a plate with a rib stiffener, the efficacy of the meta-screen and the influence of occasional disturbance and regular pollution are evaluated. Experimental validations on an adhesive structure also confirm the superior sensitivity of the nonlinear coda waves to incipient damage, which is further enhanced by the deployment of the meta-screen alongside improved robustness against deceptive nonlinear sources outside the inspection area. The proposed metamaterial-assisted coda wave interferometry method with second harmonic Lamb waves holds great promise for local incipient damage monitoring of complex structures.

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A feasibility study on monitoring of weld fatigue crack growth based on coda wave interferometry (CWI)

Cited by 10 publications

References 54 publications

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

A novel method for steel bar all-stage pitting corrosion monitoring using the feature-level fusion of ultrasonic direct waves and coda waves

A metamaterial-assisted coda wave interferometry method with nonlinear guided waves for local incipient damage monitoring in complex structures

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