Co-occurring Cluster Mining for Damage Patterns Analysis of a Fuel Cell

Co-occurring Cluster Mining for Damage Patterns Analysis of a Fuel Cell
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用于燃料电池损坏模式分析的共生集群挖掘

DOI:
10.1007/978-3-642-30220-6_5
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发表时间:
2012
期刊:
Advances in Knowledge Discovery and Data Mining (Lecture Notes in Computer Science)
影响因子:
--
通讯作者:
and Masayuki Numao
and Masayuki Numao
中科院分区:
--
文献类型:
--
作者:
Daiki Inaba;Ken-ichi Fukui;Kazuhisa Sato;Junichiro Mizusaki;and Masayuki Numao

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本文通过分析固体氧化物燃料电池(SOFC)损伤引起的声发射(AE)事件的共发性,研究了固体氧化物燃料电池(SOFC)部件间的力学相关性。然后提出了一种从声发射等数值数据中挖掘模式的新方法。该方法同时考虑了聚类之间的共现性和聚类内部的相似性,提取了两个聚类的模式。此外,我们利用从层次聚类得到的树状图来减少搜索空间。将该方法应用于声发射数据,提取了代表主要力学相关性的损伤模式。我们可以从这些结果中获得关于SOFC损伤机理的新知识。
In this study, we research the mechanical correlations among components of solid oxide fuel cell (SOFC) by analyzing the co-occurrence of acoustic emission (AE) events which are caused by damage. Then we propose a novel method for mining patterns from the numerical data such as AE. The proposed method extracts patterns of two clusters considering co-occurrence between clusters and similarity within each cluster at the same time. In addition, we utilize the dendrogram obtained from hierarchical clustering for reduction of the search space. We applied the proposed method to AE data, and the damage patterns which represent the main mechanical correlations were extracted. We can acquire novel knowledge about damage mechanism of SOFC from the results.
水解老化对聚酯复合材料拉伸试验期间发生的损伤机制的声发射特征的影响:Kohonen 图的应用
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