Intelligent analysis for evaluating physical degradation using acoustic emission

K. Fukui, Kazuhisa Sato, Toshiyuki Hashida, J. Mizusaki, M. Numao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We previously developed a technique by which to measure the mechanical damage of SOFC using the acoustic emission (AE) method. In the present paper, we applied an adapted Self-Organizing Map (SOM), which is an artificial neural network model, to produce a cluster map reflecting the similarity of AE events. The obtained map visualized the change in occurrence patterns of similar AE events, revealing six phases of damage progress. Moreover, we inferred mechanical interactions among components of SOFC from a series of AE events by our proposed data mining method called co-occurring cluster mining. Our methods provide a common foundation for a comprehensive damage evaluation system and a damage monitoring system.

Original languageEnglish
Pages (from-to)571-580
Number of pages10
JournalECS Transactions
Volume57
Issue number1
DOIs
Publication statusPublished - 2013 Jan 1

ASJC Scopus subject areas

  • Engineering(all)

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