A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery
In this paper, we approach the problem of automatically designing fuzzy diagnosis rules for rotating machinery, which can give an appropriate evaluation of the vibration data measured in the target machines. In particular, we explain the implementation to this aim and analyze the advantages and draw...
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| Published in: | Applied soft computing Vol. 4; no. 4; pp. 413 - 422 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.09.2004
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| ISSN: | 1568-4946 |
| Online Access: | Get full text |
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| Summary: | In this paper, we approach the problem of automatically designing fuzzy diagnosis rules for rotating machinery, which can give an appropriate evaluation of the vibration data measured in the target machines. In particular, we explain the implementation to this aim and analyze the advantages and drawbacks of two soft computing techniques: knowledge-based networks (KBN) and genetic algorithms (GA). An application of both techniques is evaluated on the same case study, giving special emphasis to their performance in terms of classification success and computation time. A reduced version of this paper first appeared under the title 'A comparative assessment on the application of knowledge-based networks and genetic algorithms to the design of fuzzy diagnosis systems for rotating machinery', published in the book 'Soft Computing in Industry;Recent Appliactions' (Springer Engineering). |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2004.02.004 |