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Open AccessArticle

Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal

by Xudong Teng 1,2,†, Xin Zhang 3,†, Yuantao Fan 4 and Dong Zhang 1,*
Key Laboratory of Modern Acoustics (Nanjing University), Ministry of Education, Institute of Acoustics, Nanjing 210093, China
School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Nanjing Manse Acoustics Technology Co. Ltd., Nanjing 210017, China
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE-30118 Halmstad, Sweden
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(1), 95;
Received: 24 November 2018 / Revised: 18 December 2018 / Accepted: 22 December 2018 / Published: 28 December 2018
(This article belongs to the Special Issue Modelling, Simulation and Data Analysis in Acoustical Problems)
Non-linear acoustic technique is an attractive approach in evaluating early fatigue as well as cracks in material. However, its accuracy is greatly restricted by external non-linearities of ultra-sonic measurement systems. In this work, an acoustical data-driven deviation detection method, called the consensus self-organizing models (COSMO) based on statistical probability models, was introduced to study the evolution of localized crack growth. By using pitch-catch technique, frequency spectra of acoustic echoes collected from different locations of a specimen were compared, resulting in a Hellinger distance matrix to construct statistical parameters such as z-score, p-value and T-value. It is shown that statistical significance p-value of COSMO method has a strong relationship with the crack growth. Particularly, T-values, logarithm transformed p-value, increases proportionally with the growth of cracks, which thus can be applied to locate the position of cracks and monitor the deterioration of materials. View Full-Text
Keywords: crack growth; acoustic echo; COSMO; p-value crack growth; acoustic echo; COSMO; p-value
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MDPI and ACS Style

Teng, X.; Zhang, X.; Fan, Y.; Zhang, D. Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal. Appl. Sci. 2019, 9, 95.

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