基于贝叶斯最优核判别分析的机械故障诊断

郝腾飞;陈 果

振动与冲击 ›› 2012, Vol. 31 ›› Issue (13) : 26-30.

PDF(1794 KB)
PDF(1794 KB)
振动与冲击 ›› 2012, Vol. 31 ›› Issue (13) : 26-30.
论文

基于贝叶斯最优核判别分析的机械故障诊断

  • 郝腾飞,陈 果
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Machinery Fault Diagnosis Based on Bayes Optimal Kernel Discriminant Analysis

  • HAO Teng-fei, CHEN Guo
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摘要



摘 要:针对应用核判别分析到机械故障诊断时核参数选取困难的问题,提出一种基于贝叶斯最优核判别分析的机械故障诊断方法。用梯度下降法优化同方差性准则确定最优核参数;用最优核参数使用核判别分析将原始样本投影到一个最优子空间,在该子空间中使各类样本具有最佳判别性;基于投影后的样本用最近邻方法进行故障分类。将该方法应用于滚动轴承故障诊断,并与相关方法的诊断结果进行比较,实验结果表明:该方法可获得与支持向量机同样的性能,并避免核参数需人工选择的问题。


Abstract

Abstract: When kernel discriminant analysis is applied to machinery fault diagnosis, the main challenge is to determine the parameters of kernel function. Aiming at this problem, a method for machinery fault diagnosis based on bayes optimal kernel discriminant analysis is proposed. Firstly, the optimal kernel parameter is selected by optimizing the homoscedastic criterion using gradient descent; Secondly, with the optimal kernel parameter, the original examples are projected onto an optimal subspace where the examples are best separated. Finally, fault classification is performed in the optimal subspace by nearest neighbour method. the proposed method is applied to fault diagnosis of roller bearings and comparison is made with the results of related methods, experimental results demonstrate the performance of the proposed method is comparable to that of support vector machines, but avoiding the problem that kernel parameters need to be manually selected.

关键词

故障诊断 / 滚动轴承 / 判别分析 / 核优化 / 支持向量机

Key words

fault diagnosis / roller bearings / discriminant analysis / kernel optimization / support vector machines

引用本文

导出引用
郝腾飞;陈 果. 基于贝叶斯最优核判别分析的机械故障诊断[J]. 振动与冲击, 2012, 31(13): 26-30
HAO Teng-fei;CHEN Guo. Machinery Fault Diagnosis Based on Bayes Optimal Kernel Discriminant Analysis[J]. Journal of Vibration and Shock, 2012, 31(13): 26-30

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