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APPLICATION OF SUPPORT VECTOR MACHINE BASED FAULT DIAGNOSIS
- Source :
- IFAC Proceedings Volumes. 35:221-226
- Publication Year :
- 2002
- Publisher :
- Elsevier BV, 2002.
-
Abstract
- The fault diagnosis is important in continuously monitoring the performance and quality of manufacturing processes. Overcoming the drawbacks of threshold approach, artificial neural network may extract the symptom of the faults through learning from the samples, but it is difficult to design its structure. Moreover, it needs a large numbers of samples in practice. In this paper, support vector machine approach was proposed to overcome these limitations based on statistics learning theory, and a new fault diagnosis system is developed. The experimental results showed that it is an efficient and practical on-line intelligent monitoring system for the stamping processes.
- Subjects :
- Structure (mathematical logic)
Engineering
Artificial neural network
business.industry
media_common.quotation_subject
Monitoring system
General Medicine
Stamping
Fault (power engineering)
Machine learning
computer.software_genre
Support vector machine
Learning theory
Quality (business)
Artificial intelligence
business
computer
media_common
Subjects
Details
- ISSN :
- 14746670
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- IFAC Proceedings Volumes
- Accession number :
- edsair.doi...........7a88a9280873b34adfb2ce2adfea98cf