Fracture monitoring and damage pattern recognition for carbon nanotube‐crumb rubber mortar using acoustic emission techniques

2019 ◽  
Vol 26 (10) ◽  
Author(s):  
Jie Xu ◽  
Zhengwu Fu ◽  
Qinghua Han ◽  
Hongliang Li
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Carbon Nanotube ◽  
Crumb Rubber ◽  
Damage Pattern

Damage Pattern Recognition of Refractory Materials Based on k-Means Clustering Analysis

2012 ◽  
Vol 602-604 ◽  
pp. 990-994
Author(s):  
Xi Li ◽  
Zhi Gang Wang ◽  
Chang Ming Liu ◽  
Bing Qiang Han
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Clustering Analysis ◽  
Bending Test ◽  
Refractory Materials ◽  
Damage Pattern ◽  
Three Point Bending ◽  
Centroid Frequency ◽  
Acquisition Device ◽  
Microscopic Damage

The k-means algorithm was used to divide the acoustic emission signals collected during the three-point bending test into two types. Combining with the analysis of AE parameters can we distinguish the micro-damage pattern recognition of the refractory materials. The bending test equipment is HMOR/STRAIN, and the AE acquisition device is DISP from PAC. Amplitude, counts, risetime, duration and centroid frequency were selected as the AE parameters .The microscopic damage modes of the refractory materials were recognized.

Acoustic Emission Characterization of Damage Patterns in Composite Scarf Joints Using Unsupervised Learning

2020 ◽  
Author(s):  
D. Xu ◽  
Z. P. Chen ◽  
P. F. Liu ◽  
J. H. Wu ◽  
P. Jiao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Composite Structures ◽  
Tensile Loading ◽  
Matrix Cracking ◽  
Interface Debonding ◽  
Engineering Structures ◽  
Damage Pattern ◽  
Damage Initiation ◽  
Non Destructive

Abstract Interest in damage detection and damage pattern recognition of engineering structures by non-destructive techniques has been increasingly growing. As a non-destructive technique, acoustic emission (AE) has developed rapidly to detect dynamic defects and their evolution behaviors of composite structures, based on the transient elastic waves produced by rapid energy release due to the geometry change of structures. In this paper, AE technology is utilized to monitor the real-time condition of the composite scarf joint (SJ) under tensile loading. First, after AE signal acquisition, dimensionality reduction of eight AE features is realized by employing principal component analysis such that the Curse of Dimensionality can be avoided. Second, feature selection is continued by introducing two evaluation indexes, i.e., correlation coefficient and Laplacian score. Third, after the optimal cluster number is determined, damage pattern recognition is accomplished by introducing k-means++ algorithm which explores the distribution of each pattern in the space constructed by four informative AE features. Based on the clustering results, damage initiation and evolution in SJ specimens under tensile loading are subsequently explored. The shear failure of the adhesive layer which is a characteristic damage pattern for SJ specimens shows a relatively-high activity after the early stage. Matrix cracking and fiber/matrix interface debonding are two fundamental damage patterns which keep active in the whole process.

Diagnosis of damage evolution process for asphalt mixtures using pattern recognition with acoustic emission signals

2021 ◽  
Vol 280 ◽  
pp. 122536
Author(s):  
Xin Qiu ◽  
Jingxian Xu ◽  
Wenyi Xu ◽  
Qing Yang ◽  
Feng Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Damage Evolution ◽  
Asphalt Mixtures ◽  
Evolution Process
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