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Rough Set Feature Selection and Diagnostic Rule Generation for Industrial Applications

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Rough Sets and Current Trends in Computing (RSCTC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2475))

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Abstract

Diagnosis or Fault Detection and Identification is a crucial part of industrial process maintenance systems. In this paper, a methodology is proposed for fault feature selection that includes (1) feature preparation to obtain potential features from raw data, (2) multidimensional feature selection based on rough set theory, and (3) diagnostic rule generation to identify impending failures of an industrial system and to provide the causal relationships between the input conditions and related abnormalities.

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Author information

Authors and Affiliations

  1. Georgia Institute of Technology, Electrical & Computer Engineering, Atlanta, GA, 30332-0250

    Seungkoo Lee, Nicholas Propes, Guangfan Zhang, Yongshen Zhao & George Vachtsevanos

Authors
  1. Seungkoo Lee
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  2. Nicholas Propes
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  3. Guangfan Zhang
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  4. Yongshen Zhao
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  5. George Vachtsevanos
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Editor information

Editors and Affiliations

  1. Penn State Great Valley School of Professional Graduate Studies, 30 East Swedesford Road, Malvern, PA, 19355, USA

    James J. Alpigini

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Systems and Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong

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© 2002 Springer-Verlag Berlin Heidelberg

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Lee, S., Propes, N., Zhang, G., Zhao, Y., Vachtsevanos, G. (2002). Rough Set Feature Selection and Diagnostic Rule Generation for Industrial Applications. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_75

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  • DOI: https://doi.org/10.1007/3-540-45813-1_75

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44274-5

  • Online ISBN: 978-3-540-45813-5

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