| 基于熵带法与PSO优化的SVM转子故障诊断 |
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| 引用本文： | 霍天龙,赵荣珍,胡宝权. 基于熵带法与PSO优化的SVM转子故障诊断[J]. 振动、测试与诊断, 2011, 31(3) |
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| 作者姓名： | 霍天龙 赵荣珍 胡宝权 |
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| 作者单位： | 兰州理工大学数字制造技术与应用省部共建教育部重点实验室,兰州,730050;兰州理工大学机电工程学院,兰州,730050 |
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| 基金项目： | 国家自然科学基金，甘肃省教育厅硕导基金 |
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| 摘 要： | 对转子故障信号的信息熵带作为支持向量机(support vector machine,简称SVM)的训练样本,基于粒子群算法(particle swarm optimization,简称PSO)优化SVM分类器结构参数进行了研究.对试验模拟获得的故障信号进行了时域、频域、时-频域的信息熵带计算,得到了奇异值谱熵、功率谱熵、小波空间谱熵及小波能谱熵4种熵带,并对熵带进行预处理,建立了一种基于故障信号的信息熵带作为特征量,用PSO解决SVM结构参数优化设置的转子故障识别方法.将该方法应用于转子系统在线故障诊断中,结果表明,所设计的算法具有训练速度快,测试时间短、分类准确率高等特点.
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| 关 键 词： | 转子系统 信息熵 支持向量机 故障诊断 |
Fault Diagnosis for Rotor Systems Based on Entropy Band Method and Support Vector Machine Optimized by PSO |
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| Huo Tianlong,Zhao Rongzhen,Hu Baoquan. Fault Diagnosis for Rotor Systems Based on Entropy Band Method and Support Vector Machine Optimized by PSO[J]. Journal of Vibration,Measurement & Diagnosis, 2011, 31(3) |
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| Authors: | Huo Tianlong Zhao Rongzhen Hu Baoquan |
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| Affiliation: | Huo Tianlong1,2,Zhao Rongzhen1,Hu Baoquan1,2 (1Key Laboratory of Digital Manufacturing Technology and Application,the Ministry of Education,Lanzhou University of Technology Lanzhou,730050,China) (2College of Mechano-Electronic Engineering,China) |
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| Abstract: | How to optimize structure parameters of support vector machine(SVM) classifier is reseached based on particle swarm optimization(PSO).The information entropy band of the fault signal is used as SVM training sample.Singular spectrum entropy,power spectrum entropy,wavelet energy spectrum entropy and wavelet space feature entropy values are obtained after computing the entropy band value of fault signal in the time,frequency and time-frequency domains.A fault identification method for the rotor system is estab... |
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| Keywords: | rotor system information entropy support vector machine(SVM) fault diagnosis |
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