| 基于支持向量机的船舶柴油机故障诊断 |
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| 引用本文： | 朱志宇,刘维亭.基于支持向量机的船舶柴油机故障诊断[J].船舶工程,2006,28(5):31-33. |
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| 作者姓名： | 朱志宇 刘维亭 |
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| 作者单位： | 江苏科技大学,电子信息学院,镇江,212003;江苏科技大学,电子信息学院,镇江,212003 |
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| 摘 要： | 介绍了支持向量机(SVM)的机理,应用SVM对船舶电站主柴油机进行故障诊断,研究了SVM参数的选择方法,仿真结果表明,SVM具有较好的诊断效果和较强的抗噪声能力;对复合故障样本诊断准确度较RBF神经网络高.
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| 关 键 词： | 柴油机 故障诊断 支持向量机 |
| 文章编号： | 1000-6982(2006)05-0031-03 |
| 收稿时间： | 2005-08-31 |
| 修稿时间： | 2005-11-28 |
Fault diagnosis of marine diesel engine based on support vector machine |
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| ZHU Zhi-yu,LIU Wei-ting.Fault diagnosis of marine diesel engine based on support vector machine[J].Ship Engineering,2006,28(5):31-33. |
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| Authors: | ZHU Zhi-yu LIU Wei-ting |
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| Institution: | Dept. of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China |
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| Abstract: | The mechanism of support vector machine (SVM) is first introduced in this paper. Then SVM is applied to diagnose faults of marine diesel engine. The method to choose parameters of SVM is studied. The simulation result shows that SVM has good diagnosis effect and strong ability to resist noise. Compared with RBF neural network, SVM has higher diagnosis accuracy on compound fault samples. |
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| Keywords: | diesel engine fault diagnosis support vector machine (SVM) |
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