Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/2921
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mardapittas, AS | - |
dc.contributor.author | Au, YHJ | - |
dc.coverage.spatial | 4 | en |
dc.date.accessioned | 2008-12-18T15:25:53Z | - |
dc.date.available | 2008-12-18T15:25:53Z | - |
dc.date.issued | 1992 | - |
dc.identifier.citation | IEE Colloquium on Intelligent Fault Diagnosis - Part 1: Classification-Based Techniques, London, UK, 25 Feb 1992. pp. 2/1 - 2/4 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/2921 | - |
dc.description.abstract | A description is given of a simple yet powerful expert system created using the CRYSTAL shell which is able to monitor the potential and functional failures of the tool and the monitoring equipment. The techniques of feature extraction, selection and classification using the Bayesian rule are presented. Finally supervised learning, necessary when new situations are encountered, is also discussed. | en |
dc.format.extent | 218512 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | IEEE | en |
dc.title | Expert system for tool wear monitoring in blanking | en |
dc.type | Conference Paper | en |
Appears in Collections: | Advanced Manufacturing and Enterprise Engineering (AMEE) Brunel Design School Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Expert System for.pdf | 213.39 kB | Adobe PDF | View/Open |
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