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Fuzzy Support Vector Machine and Its Application to Mechanical Condition Monitoring

机译:模糊支持向量机及其在机械条件监控的应用

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Fuzzy support vector machine (FSVM) is applied in this paper, in order to resolve problem on bringing different loss for classification error to different fault type in mechanical fault diagnosis. Based on basic principle of FSVM, a method of determining numerical value range of fuzzy coefficient is proposed. Classification performance of FSVM is tested and verified by means of simulation data samples. A fuzzy fault classifier is constructed, and applied to condition monitoring of flue-gas turbine set. The results show that fuzzy coefficient can indicate importance degree of data sample, and classification error rate of important data sample can be decreased.
机译:模糊支持向量机(FSVM)应用于本文,以解决对机械故障诊断中不同故障类型带来不同损耗的问题。基于FSVM的基本原理,提出了一种确定模糊系数的数值范围的方法。通过模拟数据样本测试和验证FSVM的分类性能。构造模糊故障分类器,并应用于烟道燃气轮机组的条件监测。结果表明,模糊系数可以指示数据样本的重要程度,并且可以减少重要数据样本的分类误差率。

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