复杂工业过程质量相关的故障检测与诊断技术综述

质量相关的故障检测与诊断技术是保证安全生产及获得可靠产品质量的有效手段,是当前国际过程控制领域的研究热点.首先,梳理了质量相关的故障检测技术中典型方法的基本思想和改进过程;其次,概述了质量相关的故障诊断技术中常用的贡献图法及其相关改进方法之间的联系,并通过带钢热连轧过程（Hot strip mill process,HSMP）案例比较了各种典型方法在质量相关的故障检测与诊断性能上的异同;最后,面向复杂工业过程运行数据的主要特性,评析了质量相关的故障检测与诊断方法的研究现状,并指出了该研究领域亟需解决的问题和未来的发展方向....

Full description

Saved in:
Bibliographic Details
Published in自动化学报 Vol. 43; no. 3; pp. 349 - 365
Main Author 彭开香 马亮 张凯
Format Journal Article
LanguageChinese
Published 北京科技大学自动化学院 北京 100083 2017
钢铁流程先进控制教育部重点实验室 北京 100083
Subjects
偏最小二乘
故障检测
故障诊断
贡献图
质量相关
贡献图
fault diagnosis
故障诊断
contribution plot
fault detection
故障检测
偏最小二乘
质量相关
Quality-related
partial least squares (PLS)
Online AccessGet full text
ISSN0254-4156
1874-1029
DOI10.16383/j.aas.2017.c160427

Cover

More Information
Summary:质量相关的故障检测与诊断技术是保证安全生产及获得可靠产品质量的有效手段,是当前国际过程控制领域的研究热点.首先,梳理了质量相关的故障检测技术中典型方法的基本思想和改进过程;其次,概述了质量相关的故障诊断技术中常用的贡献图法及其相关改进方法之间的联系,并通过带钢热连轧过程（Hot strip mill process,HSMP）案例比较了各种典型方法在质量相关的故障检测与诊断性能上的异同;最后,面向复杂工业过程运行数据的主要特性,评析了质量相关的故障检测与诊断方法的研究现状,并指出了该研究领域亟需解决的问题和未来的发展方向.
Bibliography:Quality-related fault detection and diagnosis techniques have been extensively applied to the process control field to guarantee production safety and product quality, which, thus, have recently become an active area of research both in academia and industry. Firstly, the basic idea and improvements of typical methods for quality-related fault detection techniques are introduced in this paper. Then, quality-related fault diagnosis techniques are revisited, with special case study attention on the contribution plot based methods and their improved methods, in which on a hot strip mill process（HSMP） is used to show their different performances. Finally, the state-of-the-art research of quality-related fault detection and diagnosis methods for main characteristics of complex industrial process operation data are reviewed,and open problems, challenges and perspectives for future research are presented as well.
Quality-related;fault detection;fault diagnosis;partial least squares（PLS）;contribution plot
PENG Kai-Xian
ISSN:0254-4156
1874-1029
DOI:10.16383/j.aas.2017.c160427