基于粗糙集、模糊集和贝叶斯最优分类器，提出一种变压器绝缘故障诊断与维护的综合决策模型。
Based on Bayesian optimal classifier, combining with rough set and fuzzy set, a new transformer fault diagnosis and maintenance mode is presented in the paper.
电力变压器故障诊断的仿真实验结果表明，利用该分类方法可以提高数据分类的精确性。
The result of simulative experiment for power transformers fault diagnosis shows that the method can overcome the before-mentioned shortcomings and improve the accuracy of data classification.
实验表明提出的选择性贝叶斯分类器适于变压器故障诊断。
Experimental results show that Bayes classifier is suitable for the transformer fault diagnosis.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
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