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Sensors 2019, 19(2), 236; https://doi.org/10.3390/s19020236

Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition

1
State Key Lab of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2
CRRC Zhuzhou Institute Co. Ltd., Zhuzhou 412001, China
*
Author to whom correspondence should be addressed.
Received: 3 December 2018 / Revised: 29 December 2018 / Accepted: 6 January 2019 / Published: 9 January 2019
(This article belongs to the Special Issue Sensor Signal and Information Processing II)
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Abstract

The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu’s method was used for interval thresholding of the first relevant mode, and a window was used to separate the flaw echoes in the relevant modes. The flaw echo was reconstructed by adding the residue and the separated flaw echoes. The established methodology was successfully employed for simulated signal and experimental signal processing. For the simulated signals, an improvement of 9.42 dB in the signal-to-noise ratio (SNR) and an improvement of 0.0099 in the modified correlation coefficient (MCC) were achieved. For experimental signals obtained from two cracks at different depths, the flaw echoes were also significantly enhanced. View Full-Text
Keywords: ultrasonic flaw echo enhancement; empirical mode decomposition; sample entropy; Otsu’s method for thresholding; flaw echo separation ultrasonic flaw echo enhancement; empirical mode decomposition; sample entropy; Otsu’s method for thresholding; flaw echo separation
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Feng, W.; Zhou, X.; Zeng, X.; Yang, C. Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition. Sensors 2019, 19, 236.

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