遗传算法优化最小二乘支持向量机的故障诊断
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国家高技术研究发展计划)863计划) )No.2009AA04Z411),国家自然科学基金)No. 50875272),高等学校博士学科点专项科研基金)No.20090191110005),重庆大学“211工程”三期建设研究生开放实验室)S-0916)资助项目。


Fault diagnosis based on least square support vector machine optimized by genetic algorithm
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    摘要:

    提出一种基于遗传算法分层优化多类最小二乘支持向量机(least squares support veotor machine,LS-SVM)的故障诊断模型.首先将故障信号经验模态分解(empirical mode decomposition,EMD) 为平稳本征模态(intrinsic mode function,IMF)分量,再选择表征故障调制特征的IMF 分量并提取瞬时幅值能量作为故障特征输入到遗传算法分层优化好的采用多项式核的多类LS-SVM 中进行故障识别.EMD分解可自适应分离故障调制信号;瞬时

    Abstract:

    A new fault diagnosis model is proposed based on Multi-Class Least Square Support Vector Machine optimized hierarchically by Genetic Algorithm(GA). Original vibration signals are decomposed into several stationary IMFs. Then the instantaneous amplitude energy of the IMFs with fault modulation characteristics is computed and regarded as the input characteristic measure of the Poly-kernel Multi-Class LS-SVM for fault classification. EMD decomposition adaptively isolates the fault modulation signals from original signals. The differences among instantaneous amplitude energy vectors reflect the separability of different fault types. Adopting GA to optimize punish parameter and Poly-kernel parameters hierarchically can not only enhance fault prediction accuracy of Multi-Class LS-SVM with Poly-kernel, but also improve adaptive diagnosis capacity of LS-SVM. The GA-based hierarchical optimization is also applicable to Multi-Class LS-SVM with Lin-kernel, RBF-kernel or Sigmoid-kernel. The deep groove ball bearings fault diagnosis experiment shows the effectivity of this new model.

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李锋,汤宝平,刘文艺.遗传算法优化最小二乘支持向量机的故障诊断[J].重庆大学学报,2010,33(12):14-20.

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  • 收稿日期:2010-07-20
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