基于K近邻证据融合的故障诊断方法  

Fault diagnosis based on KNN evidence fusion

作　　者：侯平智[1] 张明 徐晓滨[1] 黄大荣[2] HOU Ping-zhi ZHANG Ming XU Xiao-bin HUANG Da-rong(School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China School of Information Science and Engineering, ChongqingJiaotongUniversity, Chongqing400074, China)

机构地区：[1]杭州电子科技大学自动化学院,杭州310018 [2]重庆交通大学信息科学与工程学院,重庆400074

出　　处：《控制与决策》2017年第10期1767-1774,共8页Control and Decision

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)

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.A fault diagnosis method based on KNN evidence fusion is presented to keep a balance between modeling accuracy of fault feature data and instantaneity of diagnosis decision making. For each fault feature（symptom）, its historical sample data are used to model fault template patterns（FTPs） with the form of random-fuzzy variable（RFV）,the KNN algorithm is used to find out K historical samples nearest to a testing sample and the RFV-type fault testing patterns（TPs） of the K samples are presented to describe the testing sample. The matching degree between FTP and TP can be calculated to generate the K pieces of diagnosis evidence, and then all evidence coming from the different fault features can be fused and diagnosis decision can be made based on the fused result. In this method, the fine modeling can be realized by using the RFV, and meanwhile, the diagnosis information of the single testing sample can be enriched by adding the K historical samples, and the instantaneity of diagnosis can be improved. Finally, in diagnosis experiments on a rotor test bed, the effectiveness of the proposed method is verified.

关 键 词：故障诊断 工业报警器系统 证据理论 K近邻 随机模糊变量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]