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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Huo-Ching Sun Yann-Chang Huang |
| Copyright Year | 2012 |
| Abstract | This paper presents particle swarm optimization (PSO)-based support vector machine (SVM) to extract the optimal support vector from database for vibration fault diagnosis of steam turbine-generator sets (STGS). In this paper, the SVM is used to construct the vibration fault diagnosis model and the proposed PSO is then adopted to determine automatically the optimal parameters in the SVM. Test results demonstrated that the proposed PSO-based SVM method can achieve greater diagnostic accuracy than the SVM method. Thus, this paper has demonstrated the feasibility of applying the proposed PSO-based SVM method to the practical vibration fault diagnosis of the STGS. |
| Starting Page | 797 |
| Ending Page | 800 |
| File Size | 420344 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467307673 |
| DOI | 10.1109/IS3C.2012.206 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-04 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vibrations Support vector machines Fault diagnosis Training Accuracy Vibration fault diagnosis Support vector machine Particle swarm optimization Turbines Testing |
| Content Type | Text |
| Resource Type | Article |
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