基于K均值聚类的耐火材料损伤模式识别
首发时间:2012-04-06
摘要:耐火材料声发射信号的聚类分析可以实现其微观损伤机理的模式识别,并且确定不同损伤机制对应的声发射特征。采用K均值聚类算法将材料弯曲试验过程中采集的声发射信号划分为两类,综合声发射参数关联分析实现了耐火材料的微观损伤模式识别。为建立耐火材料微观损伤的数学模型,研究耐火材料的力学行为的微观损伤机理奠定基础。
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Damage pattern recognition of refractory materials based on k-means clustering analysis
Abstract: Clustering of acoustic emission signals can be used to identify the micro-damage mechanisms of refractory materials and determine the acoustic emission characteristics corresponding to the different micro-damage mechanisms. The k-means algorithm is 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 research establishes a foundation for further building the micro-damage mathematical model and studying the mechanical behavior of refractory materials.
Keywords: acoustic emission clustering analysis k-means pattern recognition
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