燃料電池における損傷パターン抽出のための共起クラスタマイニング

• 稲場 大樹

  大阪大学大学院情報科学研究科

• 福井 健一

  大阪大学産業科学研究所

• 佐藤 一永

  東北大学工学研究科

• 水崎 純一郎

  東北大学多元物質科学研究所

• 沼尾 正行

  大阪大学産業科学研究所

書誌事項

タイトル別名

• Mining of Co-occurring Clusters for Damage Pattern Extraction of a Fuel Cell

抄録

Solid oxide fuel cell (SOFC) is an efficient generator and researched for practical use. However, one of the problems is the durability. In this study, we research the mechanical correlations among components of SOFC by analyzing the co-occurrence of acoustic emission (AE) events which are caused by damage. Then we proposed a novel method for mining patterns from the numerical data such as AE. The conventional method has possible problems when mining patterns from the numerical data. In the clustering, clusters may contain data which does not contribute a certain pattern, or may not contain data which contribute a pattern. On the other hand, the proposed method extracts patterns of two clusters considering co-occurrence between clusters and similarity within each cluster at the same time. In addition, the dendrogram obtained from hierarchical clustering is utilized for the reduction of search space. First, we evaluate the performance of proposed method with artificial data, and demonstrate that we can obtain appropriate clusters corresponding to patterns. Then, we apply the proposed method to AE data, and the damage patterns which represent the major mechanical correlations were extracted. We can acquire novel knowledge about damage mechanism of SOFC from the results.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 27 (3), 121-132, 2012

  一般社団法人 人工知能学会

キーワード

• data mining
• clustering
• co-occurring pattern
• damage evaluation
• fuel cell

詳細情報

• CRID: 1390282680083480576
• NII論文ID: 130001885111
• DOI: 10.1527/tjsai.27.121
• BIBCODE: 2012TJSAI..27..121I
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://www.jstage.jst.go.jp/article/tjsai/27/3/27_3_121/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可