A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

被引：1182

作者：

Yin, Shen ^{[1
,2
]}

Ding, Steven X. ^{[1
]}

Haghani, Adel ^{[1
]}

Hao, Haiyang ^{[1
]}

Zhang, Ping ^{[1
]}

机构：

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

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

来源：

JOURNAL OF PROCESS CONTROL | 2012年 / 22卷 / 09期

关键词：

Process monitoring; Fault diagnosis; Data-driven methods; Tennessee Eastman process; FISHER DISCRIMINANT-ANALYSIS; PARTIAL LEAST-SQUARES; DISTURBANCE DETECTION; IDENTIFICATION; DIRECTIONS; COMPONENTS; ALGORITHMS; CHARTS; PART;

D O I：

10.1016/j.jprocont.2012.06.009

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper provides a comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM-FD). Based on the review of these methods and their recent developments, the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail. In order to further compare their performance from the application viewpoint, an industrial benchmark of Tennessee Eastman (TE) process is utilized to illustrate the efficiencies of all the discussed methods. The study results are dedicated to provide a reference for achieving successful PM-FD on large scale industrial processes. Some important remarks are finally concluded in this paper. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：1567 / 1581

页数：15

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