A fully automatic offline mode identification method for multi-mode processes

被引：0
|
作者：
Zhang S.-M. [1 ]
Wang F.-L. [1 ,2 ]
Tan S. [3 ]
Wang S. [1 ,2 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
[2] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
[3] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2016年 / 42卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Fully automatic; Mode identification; Multi-mode process; Stable mode; Transition mode;
D O I：
10.16383/j.aas.2016.c150048
中图分类号：
学科分类号：
摘要：
Multimode is a general characteristic of complex industrial processes. Different modes have different process characteristics, and different models should be established. Therefore, offline mode identification is one of the critical problems for multimode processes modelling. Presently, the commonly used clustering methods cannot realize offline mode identification of multimode processes automatically because human analysis and further processing are needed to gain the final identification result. A fully automatic offline mode identification method is proposed in the paper. First, the data is divided into a series of data segments by a cutting window with the designated width H. The improved K-means clustering method is used to assign the segments into different clusters. According to the clustering result, the missing stable modes are dealt with to obtain the preliminary mode identification results. Finally, the regions between the stable modes and transitional modes are further analyzed by a small moving window L to determine the accurate boundaries between different modes. The mode identification of multimode processes can be realized automatically by the method for a reasonable and effective identification result. Feasibility and practical value of the method are evaluated by case study. Copyright © 2016 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：60 / 80
页数：20
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