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

Xi Li; Zhi Gang Wang; Chang Ming Liu; Bing Qiang Han

doi:10.4028/www.scientific.net/AMR.602-604.990

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A Study of Relationship Between Residual Asphalt in Aggregate Surface and Aging Degree of its Asphalt
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Damage Pattern Recognition of Refractory Materials Based on k-Means Clustering Analysis
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Damage Pattern Recognition of Refractory Materials Based on k-Means Clustering Analysis

Abstract:

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.