Feature Extraction and Classification of Operational Data for Diagnosis of Hot Strip Mill Looper Control

• Torigoe Takashi

• Konishi Masami

• Imai Jun

• Nishi Tatsushi

抄録

In these days, mechanical systems are becoming more complex and highly automated. So, there exist wide variety of demands for reliable diagnostic technology. A reliable data analysis and quantitative diagnosis method of mechanical system is necessary for the purpose. In this paper a quantitative diagnosis method for looper height control system has been developed based on neural network technologies. The wavelet transformation is used for pre-processing to analyze characteristics of looper height control system. And, self organizing map neural network is used for the purpose of classification based on the pre-processed data. After that, the classified results are used for quantitative diagnosis in hierarchical neural network.

収録刊行物

• Memoirs of the Faculty of Engineering, Okayama University

  Memoirs of the Faculty of Engineering, Okayama University 38 (1-2), 15-27, 2004-03

  Faculty of Engineering, Okayama University

詳細情報

• CRID: 1390009224822813056
• NII論文ID: 80016785934
• NII書誌ID: AA10699856
• ISSN: 04750071
• DOI: 10.18926/46948
• Web Site: https://ousar.lib.okayama-u.ac.jp/46948
• 本文言語コード: en
• データソース種別:
  • JaLC
  • IRDB
  • CiNii Articles