A Hybrid Deterministic Model Based on Rough set and Fuzzy set and Bayesian Optimal Classifier
A Hybrid Deterministic Model Based on Rough set and Fuzzy set and Bayesian Optimal Classifier
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DOI：
10.1109/icicic.2006.200
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
H. Su;Qunzhan Li
中科院分区：
文献类型：
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作者：
H. Su;Qunzhan Li
提出了一种基于粗糙集、模糊集和贝叶斯最优分类器的变压器故障诊断与维护方法。该方法首先利用观测信息的模糊服从度函数在贝叶斯最优分类器中建立原始假设的后验概率，然后计算基于每个故障信息的分类结果，将这些结果加权平均后得到最佳诊断结果。然后基于贝叶斯风险决策的粗略模型，识别所有故障信息的诊断结果，以构成可能的维护策略。实际应用表明，该方法能够解决贝叶斯最优分类器模糊知识获取的“瓶颈”，具有较强的自学习能力，是一种有效的变压器故障诊断与维护方法。
Based on rough set and fuzzy set and Bayesian optimal classifier, a novel transformer fault diagnosis and maintenance method is proposed in the paper. The method firstly applies fuzzy subjection degree function of the observed information to establish posterior probability of original assumption in Bayesian optimal classifier, the classified results based on each fault information then are calculated, the best diagnosis result is acquired after all these results are weighted average. Then based on rough model of Bayesian risk decision, the diagnosis results of all faults information are identified to constitute possible maintenance strategies. Actual application shows that the proposed method can deal with the "bottle neck" of fuzzy knowledge acquisition in Bayesian optimal classifier and possesses stronger self-learning abilities, and is an effective transformer fault diagnosis and maintenance method