A Data-Driven Process Monitoring Approach with Disturbance Decoupling

被引：0
|
作者：
Luo, Hao [1 ]
Li, Kuan [1 ]
Huo, Mingyi [1 ]
Yin, Shen [1 ]
Kaynak, Okyay [2 ]
机构：
[1] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Heilongjiang, Peoples R China
[2] Bogazici Univ, Mechatron Res & Applicat Ctr, TR-34342 Istanbul, Turkey
来源：
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Data-driven SKR; Process monitoring; Residual generation; Deterministic disturbance; Disturbance decoupling; FAULT-DETECTION; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.
引用
收藏
页码：569 / 574
页数：6
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