Name
Title | ||
|---|---|---|
Rough Set Feature Selection and Diagnostic Rule Generation for Industrial Applications |
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) 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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45813-1_75 | Rough Sets and Current Trends in Computing |
Keywords | Field | DocType |
crucial part,multi-dimensional feature selection,industrial process maintenance system,potential feature,causal relationship,industrial system,rough set feature selection,diagnostic rule generation,fault detection,feature preparation,industrial applications,fault feature selection,rough set theory,rough set,feature selection | Data mining,Feature selection,Fault detection and identification,Computer science,Industrial systems,Raw data,Rough set,Artificial intelligence,System identification,Machine learning | Conference |
Volume | ISSN | ISBN |
2475 | 0302-9743 | 3-540-44274-X |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-koo Lee | 1 | 148 | 13.83 |
| Nicholas Propes | 2 | 1 | 0.35 |
| Guangfan Zhang | 3 | 39 | 4.64 |
| Yongshen Zhao | 4 | 1 | 0.35 |
| George J. Vachtsevanos | 5 | 137 | 16.28 |