Co-occurring cluster mining for damage patterns analysis of a fuel cell

Daiki Inaba, Ken Ichi Fukui, Kazuhisa Sato, Junichirou Mizusaki, Masayuki Numao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Pages49-60
Number of pages12
EditionPART 2
DOIs
Publication statusPublished - 2012
Event16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
Duration: 2012 May 292012 Jun 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
CountryMalaysia
CityKuala Lumpur
Period12/5/2912/6/1

Keywords

  • clustering
  • co-occurrence pattern
  • damage evaluation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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