A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

被引：1374

作者：

Yin, Shen ^{[1
]}

Ding, Steven X. ^{[2
]}

Xie, Xiaochen ^{[3
]}

Luo, Hao ^{[2
]}

机构：

[1] Harbin Inst Technol, Res Ctr Intelligent Control & Syst, Harbin 150001, Peoples R China

[2] Univ Duisburg Essen, Inst Automat Control & Complex Syst AKS, D-47057 Duisburg, Germany

[3] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2014年 / 61卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Data-driven; fault diagnosis; industrial operating conditions; process monitoring; FISHER DISCRIMINANT-ANALYSIS; PARTIAL LEAST-SQUARES; FAULT-DIAGNOSIS; IDENTIFICATION; OBSERVER; SYSTEMS; PROJECTION; ROBUST;

D O I：

10.1109/TIE.2014.2301773

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

引用

页码：6418 / 6428

页数：11

共 79 条

[41] Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter [J].

Huang, Sunan ;

Tan, Kok Kiong ;

Lee, Tong Heng .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2012, 59 (11) :4285-4292

[42] A Survey of Fault Detection, Isolation, and Reconfiguration Methods [J].

Hwang, Inseok ;

Kim, Sungwan ;

Kim, Youdan ;

Seah, Chze Eng .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2010, 18 (03) :636-653

[43]

Isermann R., 2005, Fault-diagnosis systems: an introduction from fault detection to fault tolerance

[44] Support vector machines for detection and characterization of rolling element bearing faults [J].

Jack, LB ;

Nandi, AK .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2001, 215 (09) :1065-1074

[45] On-line batch process monitoring using batch dynamic kernel principal component analysis [J].

Jia, Mingxing ;

Chu, Fei ;

Wang, Fuli ;

Wang, Wei .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2010, 101 (02) :110-122

[46] Monitoring independent components for fault detection [J].

Kano, M ;

Tanaka, S ;

Hasebe, S ;

Hashimoto, I ;

Ohno, H .

AICHE JOURNAL, 2003, 49 (04) :969-976

[47] Disturbance detection and isolation by dynamic principal component analysis [J].

Ku, WF ;

Storer, RH ;

Georgakis, C .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 30 (01) :179-196

[48]

Larimore W. E., 1983, Proceedings of the 1983 American Control Conference, P445

[49]

LARIMORE WE, 1990, PROCEEDINGS OF THE 29TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-6, P596, DOI 10.1109/CDC.1990.203665

[50] Statistical process monitoring with independent component analysis [J].

Lee, JM ;

Yoo, CK ;

Lee, IB .

JOURNAL OF PROCESS CONTROL, 2004, 14 (05) :467-485

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