烟草科技2017，Vol.50Issue(9)：84-91,102,9.
基于变点检测理论的制丝过程稳态识别方法
Method for identifying steady state of process in primary processing based on change point detection theory
摘要
Abstract
For the purpose of intelligent identification of the steady state of a batch process in primary processing, a method based on change point detection theory was proposed. The primary processing data of a whole processed batch of a selected specification of cigarette brand"Yunyan"were researched via being divided dynamically into subintervals by mean-variance change point detection model and PELT algorithm, then the threshold of variance and/or the mean of the subintervals were set according to technology standard, the subintervals which conformed with the threshold conditions were extracted to form a steady state data set. Finally, the formed data set was compared with the data sets identified by two existing rules of steady state defining. The results showed that: 1)Different steady state identification methods significantly affected the accuracy of batch process data measurement in primary processing. 2)The identification method based on change point detection theory featured better adaptability and identification effect. 3)Combining the said method with Shapiro-Wilk normality test via the built-in R language program in the information system, the online intelligent assessment of process capability was realizable. The proposed method provides a technical support for the intelligent identification of steady state of process in primary processing.关键词
卷烟生产/制丝过程/批过程数据/变点检测/稳态识别Key words
Cigarette production/Primary processing/Batch process data/Change point detection/Steady state identification分类
轻工纺织引用本文复制引用
马晓龙,李宽,王慧,何雪平,刘继辉,张云飞,许磊,杨晶津,崔宇翔,李兴绪,李达..基于变点检测理论的制丝过程稳态识别方法[J].烟草科技,2017,50(9):84-91,102,9.基金项目
红云红河烟草(集团)有限责任公司科技项目"基于云平台的工艺质量智能管控研究及应用"(HYHH2016GY04). （集团）
