Characteristics of T(0, 1) Guided-Wave Point-Focusing Electromagnetic Acoustic Transducer for Pipe Inspection

Huang, Songling; Sun, Hongyu; Shen, Gongtian; Wang, Baoxuan; Wang, Qing; Zou, Jun; Wang, Shen

doi:10.1109/jsen.2019.2958953

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Specifically, this physical process could only be described by the Lorentz force theory, and the magnetization and magnetostriction mechanisms are not considered. When the pulsed current passes through the coils, a corresponding eddy current Je is induced inside the aluminium plate [27] where A indicates the magnetic field potential, σ is the conductivity, and t is the time. Due to the presence of the bias magnetic field provided by the PPM, eddy current Je will interact with it to generate the Lorentz force FL where Bs represents the static magnetic field and Bd is the dynamic magnetic field.…”

Section: Analysis Of Reflected Signal a Simulation Methodsmentioning

confidence: 99%

Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection

Huang

Sun

Peng

et al. 2021

IEEE Trans. Instrum. Meas.

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As a kind of nondestructive testing (NDT) method, shear-horizontal (SH)-guided wave detection technology is widely used on an electromagnetic acoustic transducer (EMAT). Although ultrasonic-guided waves perform well in defect location, it is difficult to obtain detailed information about defects, and the low efficiency of EMAT energy conversion still reduces the EMAT's performance. Therefore, in this work, the defect detection method of different shapes and sizes by point-focusing shear-horizontal (PFSH)-guided wave EMAT with the use of periodic permanent magnet (PPM) is investigated through simulation and experiment. For the purpose of defect classification and quantification, the extraction principles of defect features are obtained through simulation based on the circumferential scatter diagrams, and the neural network (NN) is used to process the features extracted from the experimental data. The results show that by extracting effective defect features from the scatter diagram, high-accuracy classification and high-precision quantification of defects under the influence of the focusing transducer can be achieved.

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Section: Analysis Of Reflected Signal a Simulation Methodsmentioning

confidence: 99%

Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection

Huang

Sun

Peng

et al. 2021

IEEE Trans. Instrum. Meas.

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“…In addition, utilizing a single-point excitation enables long-range transmission, thereby expanding the detection range beyond the limitations of the probe's position. There are various guided wave modes in hollow circular tubes and they can be divided into three basic categories: longitudinal modes [2], torsional modes [3] and flexural modes [4]. Different modes of guided waves have unique excitation methods and varying sensitivity to defect of different shapes.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Lv,

Chen,

Tong

et al. 2023

Meas. Sci. Technol.

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Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

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“…Cai and Li arranged the permanent magnets according to a Halbach array to realize the increase in magnetic field strength [11,12]. Huang et al optimized the unidirectional or oblique focused magnetic field by changing the magnetic poles to enhance the commutation efficiency [13]. Tu et al used the combination of applied bias and other forms of magnetic field for optimization.…”

Section: Introductionmentioning

confidence: 99%

Research on the mechanism of electromagnetic ultrasonic energy transfer based on dynamic multi-magnetic vector coupling

Zhang,

Xing,

Pang

2023

Meas. Sci. Technol.

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The traditional electromagnetic acoustic transducer(EMAT) in steel plate inspection has a problem of suboptimal energy transfer efficiency due to fixed static sensitivity. The dynamic sensitivity detection method with multi-magnetic vector coupling is proposed based on the principle of mutual conversion of electromagnetic fields inside and outside the workpiece. The conversion relationship of multi-magnetic vector coupling was analyzed, and then a mathematical model of rotational wave displacement between wave displacement and magnetic parameters was established. This approach theoretically guides the modeling, simulation, and design optimization of high energy conversion efficiency electromagnetic acoustic transducers. The experiment shows that under the action of rotating magnetic field, different rotating directions and deflection angles have varied detection effects. The optimal energy transfer efficiency of the transducer under the dynamic multi-magnetic vector coupling mechanism is effectively improved compared with the static traditional form. The optimal signal amplitude of the dynamic sensitivity detection method is 3.73 times that of traditional detection signals. The electromagnetic ultrasonic dynamic multi-magnetic vector coupling detection method explores new ideas for the mechanism analysis from static to dynamic and guarantees high sensitivity detection and evaluation of steel plates.

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Characteristics of T(0, 1) Guided-Wave Point-Focusing Electromagnetic Acoustic Transducer for Pipe Inspection

Cited by 15 publications

References 36 publications

Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection

Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Research on the mechanism of electromagnetic ultrasonic energy transfer based on dynamic multi-magnetic vector coupling

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