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.
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http://dx.doi.org/10.3390/s19020236 | DOI Listing |
Materials (Basel)
December 2022
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
The roughness of a flaw's surface significantly affects the scattering behavior of ultrasonic waves. It is vital to understand the impact of roughness on flaw echoes, especially when performing ultrasonic nondestructive inspection on safety-critical components. However, the current approach for creating rough flaw models fails to reconstruct complicated cracks with secondary cracks.
View Article and Find Full Text PDFSensors (Basel)
December 2021
Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China.
Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal.
View Article and Find Full Text PDFUltrasonics
December 2021
School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
Subwavelength ultrasonic imaging (SUI) can detect subwavelength flaws beyond the diffraction limit, however, SUI sometimes fails to clearly reveal flaws in C-scans when the signal-to-noise ratio (SNR) is low. In this work, a convolutional neural network (CNN) that takes structural noise into account is developed for SUI to distinguish flaw echoes from structural noise. The network contains a regression CNN for learning features from the structural noise and a learnable soft thresholding layer for classification.
View Article and Find Full Text PDFUltrasonics
March 2021
Department of Mechanical Engineering, École de technologie supérieure, 1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3, Canada. Electronic address:
Successfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the bandwidth of the ultrasonic transducer. This explains why the frequency is commonly increased in ultrasonic testing in order to improve the axial resolution. However, as the frequency increases, the penetration depth of the propagating ultrasonic waves is reduced due to an attendant increase in attenuation.
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