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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,*
1
Key Laboratory of Modern Acoustics (Nanjing University), Ministry of Education, Institute of Acoustics, Nanjing 210093, China
2
School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
3
Nanjing Manse Acoustics Technology Co. Ltd., Nanjing 210017, China
4
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; https://doi.org/10.3390/app9010095
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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