| 引用本文: | 冯立伟,张成,李元,谢彦红.基于标准距离k近邻的多模态过程故障检测策略[J].控制理论与应用,2019,36(4):553~560.[点击复制] |
| FENG Li-wei,ZHANG Cheng,LI Yuan,XIE Yan-hong.Fault detection strategy of standard-distance-based k nearest neighbor rule in multimode processes[J].Control Theory and Technology,2019,36(4):553~560.[点击复制] |
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| 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 2860 全文点击 1182 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) |
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| 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. |
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