基于K近邻证据融合的故障诊断方法
发布时间：2018-08-12 20:26
【摘要】：为了兼顾数据建模的准确性和诊断的实时性,提出一种K近邻诊断证据融合新方法.利用故障特征的历史样本构建随机模糊变量(RFV)形式的故障样板模式,由KNN算法获取测试样本的K个近邻历史样本,并定义它们的RFV待检模式;经样板和待检模式的匹配获取K个诊断证据,再将各特征的K个诊断证据融合,并作出故障决策;使用RFV实现对故障数据的精准建模,利用K个历史样本丰富诊断信息,并增加诊断的时效性.诊断效果在电机转子试验台上得到了验证.
[Abstract]:In order to give consideration to the accuracy of data modeling and real-time diagnosis, a novel K-nearest neighbor diagnostic evidence fusion method is proposed. Using the history samples of fault features to construct the fault pattern in the form of (RFV) with random fuzzy variables, the KNN algorithm is used to obtain the K nearest neighbor historical samples of the test samples, and their RFV mode is defined. K diagnostic evidence is obtained by matching sample and untested pattern, then K diagnostic evidence of each feature is fused, and fault decision is made. The accurate modeling of fault data is realized by using RFV, and the diagnosis information is enriched by K historical samples. And to increase the timeliness of diagnosis. The diagnosis effect is verified on the motor rotor test rig.
【作者单位】： 杭州电子科技大学自动化学院;重庆交通大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61433001,61374123,61573076,61573275) 浙江省公益性技术应用研究计划项目(2016C31071) 重庆市高等学校优秀人才支持计划项目(2014-18)
【分类号】：TP277
，
本文编号：2180270
[Abstract]:In order to give consideration to the accuracy of data modeling and real-time diagnosis, a novel K-nearest neighbor diagnostic evidence fusion method is proposed. Using the history samples of fault features to construct the fault pattern in the form of (RFV) with random fuzzy variables, the KNN algorithm is used to obtain the K nearest neighbor historical samples of the test samples, and their RFV mode is defined. K diagnostic evidence is obtained by matching sample and untested pattern, then K diagnostic evidence of each feature is fused, and fault decision is made. The accurate modeling of fault data is realized by using RFV, and the diagnosis information is enriched by K historical samples. And to increase the timeliness of diagnosis. The diagnosis effect is verified on the motor rotor test rig.
【作者单位】： 杭州电子科技大学自动化学院;重庆交通大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61433001,61374123,61573076,61573275) 浙江省公益性技术应用研究计划项目(2016C31071) 重庆市高等学校优秀人才支持计划项目(2014-18)
【分类号】：TP277
，
本文编号：2180270
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