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. 2018 Mar 7;18(3):802.
doi: 10.3390/s18030802.

Smart Sound Processing for Defect Sizing in Pipelines Using EMAT Actuator Based Multi-Frequency Lamb Waves

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Free PMC article

Smart Sound Processing for Defect Sizing in Pipelines Using EMAT Actuator Based Multi-Frequency Lamb Waves

Joaquín García-Gómez et al. Sensors (Basel). .
Free PMC article

Abstract

Pipeline inspection is a topic of particular interest to the companies. Especially important is the defect sizing, which allows them to avoid subsequent costly repairs in their equipment. A solution for this issue is using ultrasonic waves sensed through Electro-Magnetic Acoustic Transducer (EMAT) actuators. The main advantage of this technology is the absence of the need to have direct contact with the surface of the material under investigation, which must be a conductive one. Specifically interesting is the meander-line-coil based Lamb wave generation, since the directivity of the waves allows a study based in the circumferential wrap-around received signal. However, the variety of defect sizes changes the behavior of the signal when it passes through the pipeline. Because of that, it is necessary to apply advanced techniques based on Smart Sound Processing (SSP). These methods involve extracting useful information from the signals sensed with EMAT at different frequencies to obtain nonlinear estimations of the depth of the defect, and to select the features that better estimate the profile of the pipeline. The proposed technique has been tested using both simulated and real signals in steel pipelines, obtaining good results in terms of Root Mean Square Error (RMSE).

Keywords: EMAT actuators; Lamb waves; defect sizing; pipeline inspection; smart sound processing.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Conventional Ultrasound vs EMAT.
Figure 2
Figure 2
Phase velocity and group velocity depending on the product frequency by thickness.
Figure 3
Figure 3
Phase velocity and group velocity of the different modes represented according to the energy in a range of frequencies.
Figure 4
Figure 4
Dispersion suffered by the wave packet when traveling 0.8 m in the pipe (S0 mode, f=300 kHz, C=4 cycles).
Figure 5
Figure 5
Measuring equipment used in the experiments.
Figure 6
Figure 6
Model of the simulated defects.
Figure 7
Figure 7
Feature values depending on the depth of the defect.
Figure 8
Figure 8
Scheme of an SSP system.
Figure 9
Figure 9
Scheme of the evolutionary algorithm applied in the experiments.
Figure 10
Figure 10
RMSE obtained with neural networks predictor depending on the number of features selected at different frequencies.
Figure 11
Figure 11
RMSE obtained depending on the number of selected features considering different number of neurons in the MLPs.
Figure 12
Figure 12
Image of the real pipe.
Figure 13
Figure 13
Axial scan of the real pipeline at two frequencies.
Figure 14
Figure 14
Estimation of the real pipeline model.

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