基于标准距离k近邻的多模态过程故障检测策略
Fault detection strategy of standard-distance-based k nearest neighbor rule in multimode processes
摘要点击 134  全文点击 178  投稿时间:2017-11-06  修订日期:2018-09-13
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DOI编号  10.7641/CTA.2018.70806
  2019,36(4):553-560
中文关键词  主元分析  核主元分析  k近邻  多模态  故障检测
英文关键词  Principal Component Analysis  Kernel Principal Component Analysis  k Nearest Neighbor rule  multimode  fault detection
基金项目  国家自然科学基金项目(61673279);国家自然科学基金重点项目(61490701);2015辽宁省自然科学基金资助项目(2015020164)
学科分类代码  
作者单位E-mail
冯立伟 沈阳化工大学数理系 feng-li-wei@163.com 
张成 沈阳化工大学数理系  
李元 沈阳化工大学过程故障诊断研究中心 li-yuan@mail.tsinghua.edu.cn 
谢彦红 沈阳化工大学过程故障诊断研究中心  
中文摘要
      工业产品的生产经常需要在不同模态间切换, 多模态过程数据具有多中心和方差差异大等特点. 针对多模 态过程数据的特征, 通过构造标准距离, 提出了基于标准距离k近邻的故障检测策略(SD-kNN). 首先在标准距离度 量下计算样本与其前k近邻的距离; 其次将近邻距离的平方和的均值作为样本的统计量D2; 最后, 根据D2的分布确 定检测方法的控制限, 当新样本的D2大于控制限时, 判定其为故障, 否则为正常. 标准距离使不同模态中样本间的 近邻距离能够在同一尺度下度量, 使得SD-kNN的D2能够准确反映样本间的相似程度. 进行了数值模拟过程和青霉 素发酵过程故障检测实验. SD-kNN 方法检测出了数值模拟过程的全部故障和青霉素过程95%以上的故障, 相对 于PCA, kPCA, FD-kNN 等方法具有更高的故障检测率. SD-kNN 继承了FD-kNN对一般多模态过程的故障检测能 力, 还能够对方差差异显著的多模态过程进行故障检测.
英文摘要
      The production of industrial products often switches between different modes, and the multi-mode process data has the characteristics of multi center and large difference of variances. According to the characteristics, a standard distance was constructed, and a fault detection strategy based on standard distance k nearest neighbor rule (SD-kNN) was proposed. Firstly, calculated the k nearest neighborhood distances between samples in the standard distance metric; secondly, the mean of square sum of the neighborhood distances was taking as the sample’s statistic D2; finally, according to the its distribution, the control limit of the detection method was determined . When D2of a new sample is greater than the control limit, it was judged as fault; otherwise it was normal. Since the standard distance enables that the neighborhood distances of samples in different modes are measured at the same scale, the statistic D2of SD-kNN can accurately reflect the similarity between samples. Fault detection experiments in numerical simulation process and penicillin fermentation process were carried out. The SD-kNN detected all faults in a numerical simulation process and more than 95% faults in penicillin fermentation process, and it had higher fault detection rate than PCA, kPCA, FD-kNN and so on. SD-kNN inherits fault detection ability of FD-kNN in the general multimode process, and detects fault in the multimode process with obviously different variances.