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  [in Japanese] Mining of Co-occurring Clusters for Damage Pattern Extraction of a Fuel Cell  [in Japanese]

Access this Article

Author(s)

Abstract

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.

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 27(3), 121-132, 2012

    The Japanese Society for Artificial Intelligence

Cited by:  1

Codes

  • NII Article ID (NAID)
    130001885111
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    1346-0714
  • Data Source
    CJPref  J-STAGE 
Page Top