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

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Advances in Knowledge Discovery and Data Mining (PAKDD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7302))

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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.

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Osaka University, Japan

    Daiki Inaba

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Ken-ichi Fukui & Masayuki Numao

  3. Graduate School of Engineering, Tohoku University, Japan

    Kazuhisa Sato

  4. Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Japan

    Junichirou Mizusaki

Authors
  1. Daiki Inaba
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  2. Ken-ichi Fukui
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  3. Kazuhisa Sato
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  4. Junichirou Mizusaki
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  5. Masayuki Numao
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, 48824-1226, East Lansing, MI, USA

    Pang-Ning Tan

  2. School of Information Technologies, University of Sydney, 1 Cleveland St., 2006, Sydney, NSW, Australia

    Sanjay Chawla

  3. Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Chin Kuan Ho

  4. Department of Computing and Information Systems, The University of Melbourne, 111 Barry Street, 3053, Melbourne, VIC, Australia

    James Bailey

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© 2012 Springer-Verlag Berlin Heidelberg

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Inaba, D., Fukui, Ki., Sato, K., Mizusaki, J., Numao, M. (2012). Co-occurring Cluster Mining for Damage Patterns Analysis of a Fuel Cell. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-30220-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30219-0

  • Online ISBN: 978-3-642-30220-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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