| 基于AR-SVM的转子故障诊断 |
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| 引用本文： | 张龙,熊国良,陈慧,李嶷.基于AR-SVM的转子故障诊断[J].机械设计与制造,2005,25(11):138-140. |
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| 作者姓名： | 张龙 熊国良 陈慧 李嶷 |
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| 作者单位： | 华东交通大学机电工程学院,南昌,330013;华东交通大学机电工程学院,南昌,330013;华东交通大学机电工程学院,南昌,330013;华东交通大学机电工程学院,南昌,330013 |
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| 基金项目： | 江西省自然科学基金（0455017）,华东交通大学校立课题资助项目（部分） |
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| 摘 要： | 将时间序列建模与支持向量机相结合并应用于转子故障诊断领域.用时间序列理论进行故障建模,可以在缺乏对实际故障机理了解的情况下从机组自身的运行过程中动态获取故障的统计特征信息.而支持向量机作为模式识别领域的新工具,其具有小样本学习能力等显著优势.这里首先对实验台振动信号建立时间序列模型,然后用模型参数来训练一个支持向量机作为故障诊断的分类器.实验结果表明,这种方法有很好的实用性.
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| 关 键 词： | 时间序列 故障诊断 支持向量机 SVM |
| 文章编号： | 1001-3997(2005)11-0138-03 |
| 收稿时间： | 2005-01-03 |
| 修稿时间： | 2005年1月3日 |
Rotation machine fault diagnosis based on AR- SVM |
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| ZHANG Long,XIONG Guo-liang,CHEN Hui,LI Yi.Rotation machine fault diagnosis based on AR- SVM[J].Machinery Design & Manufacture,2005,25(11):138-140. |
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| Authors: | ZHANG Long XIONG Guo-liang CHEN Hui LI Yi |
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| Affiliation: | School of Mechanical Eng., East China Jiaotong Uni., Nanchang 330013, China |
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| Abstract: | A faults diagnosis method based on time series modeling and Support Vector Machine is presented.Using time series modeling,the fault pattern can still be recognized in a statistic way,even though there was little knowledge about the characteristics and features of faults.As a new tool for pattern recognition,SVM has a good performance despite of insufficient training samples.After modeling of signals collected from a simulative rotation machine,the AR(AutoRegression) coefficients were extracted as condition features and were sent to SVM as training and testing samples respectively.This method was proved to be practical and efficient by experiments on a test rig. |
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| Keywords: | Time series Fault diagnosis Support vector machine SVM |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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