引用本文:徐晓滨,叶梓发,徐晓健,侯平智,王琪冰,茹晓英.基于分层式证据推理的信息融合故障诊断方法[J].控制理论与应用,2020,37(8):1681~1692.[点击复制]
XU Xiao-bin,YE Zi-fa,XU Xiao-jian,HOU Ping-zhi,WANG Qi-bing,RU Xiao-ying.Information fusion-based fault diagnosis method via hierarchical evidence reasoning[J].Control Theory and Technology,2020,37(8):1681~1692.[点击复制]
基于分层式证据推理的信息融合故障诊断方法
Information fusion-based fault diagnosis method via hierarchical evidence reasoning
摘要点击 2124  全文点击 753  投稿时间：2019-11-15  修订日期：2020-03-21
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DOI编号  10.7641/CTA.2020.90951
  2020,37(8):1681-1692
中文关键词  故障诊断  信息融合  证据推理  k–NN算法  分层式证据推理
英文关键词  fault diagnosis  information fusion  evidence reasoning (ER)  k–NN algorithm  hierarchical ER
基金项目  NSFC-浙江两化融合联合基金(U1709215),国家自然科学基金( 61903108),浙江省重点研发计划(2019C03104,2018C04020,2018C01031)
作者单位E-mail
徐晓滨 杭州电子科技大学 xuxiaobin1980@163.com 
叶梓发 杭州电子科技大学  
徐晓健* 杭州电子科技大学 xxj03@hdu.edu.cn 
侯平智 杭州电子科技大学  
王琪冰 森赫电梯股份有限公司  
茹晓英 森赫电梯股份有限公司  
中文摘要
      针对基于信息融合的故障诊断方法中, 诊断证据的精细化获取问题和在线诊断信息量受限问题, 提出分层 式的证据推理(ER)诊断方法. 在诊断证据获取过程中, 给出故障特征参考值投点方法, 按比例求取特征样本点对相 邻参考值的相似度, 生成点值型参考证据矩阵(REM)和在线故障特征样本的诊断证据, 实现了诊断信息的精细化提 取; 在证据融合过程中, 设计分层式ER融合模型. 第1层融合中利用k–NN算法找到在线样本的近邻历史样本, 然后 利用ER规则实现在线样本与近邻历史样本对应证据的融合. 在第2层融合中, 将多个特征源提供的第1层融合结果 再次融合, 并根据两层融合所获证据进行故障决策; 此外, 在分层融合模型中, 根据证据之间的欧氏距离构造目标函 数及相应的证据重要性权重优化方法. 最后, 在多功能电机转子试验台上实施了故障诊断实验, 与已有单层ER模型 诊断结果进行比较, 说明所提方法通过提升诊断证据的精确性、增加历史样本扩充诊断信息量, 能够有效提升确诊率.
英文摘要
      In the framework of information fusion, this paper presents a hierarchical evidence reasoning (ER)-based fusion method to deal with accurate acquirement problem of diagnosis evidence and the information limitation problem in online diagnosis. In the process of diagnosis evidence acquirement, the casting strategy using reference values of fault features is proposed to proportionally calculate the similarity degree between feature sample and its neighboring reference values. In this way, the reference evidence matrix (REM) with the form of point values can be obtained and then the accurate diagnosis evidence of online fault feature sample can be generated by the REM. In the process of evidence fusion, the hierarchical ER fusion model is designed which includes two-level fusion operations. In the first level operation, k–NN algorithm is used to search for the k historical samples close to the online sample and then ER rule is used to fuse the k+1 pieces of diagnosis evidence of k historical samples and the online sample. In the second level operation, the multiple first level fusion results coming from different features can be fused again. Thus the diagnosis decision can be made according to the second level fusion results. Furthermore, the objective fusion is constructed based on evidential Euclidean distance to optimize the importance weights of evidence. Final, some diagnosis experiments on a rotor test bed are conducted to compare the proposed hierarchical ER fusion method with the previous single level ER fusion method. The experimental results show that the new method can effectively promote diagnosis rate by enhancing the evidential accuracy and adding historical sample for information expansion.