Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis
| Autor: | Wangs Shen Hao, Jian Cai Zhao, Biao Jun Tian, Xun Sheng Zhu |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Engineering
Basis (linear algebra) Artificial neural network business.industry Mechanical Engineering Control engineering Condensed Matter Physics Fault (power engineering) computer.software_genre Fuzzy logic Field (computer science) Domain (software engineering) Support vector machine Support vector machine algorithm Mechanics of Materials General Materials Science Data mining business computer |
| Zdroj: | Materials Science Forum. :496-499 |
| ISSN: | 1662-9752 |
| DOI: | 10.4028/www.scientific.net/msf.532-533.496 |
| Popis: | In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved. |
| Databáze: | OpenAIRE |
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