Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Set Data Mining of Diabetes Data
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
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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) multi-dimensional 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.