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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© 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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