复杂工业过程质量相关的故障检测与诊断技术综述

第43卷第3期自动化学报Vol.43,No.3 2017年3月ACTA AUTOMATICA SINICA March,2017

彭开香1,2马亮1,2张凯1,2

摘要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.

关键词质量相关,故障检测,故障诊断,偏最小二乘,贡献图

引用格式彭开香,马亮,张凯.复杂工业过程质量相关的故障检测与诊断技术综述.自动化学报,2017,43(3):349?365 DOI10.16383/j.aas.2017.c160427

Review of Quality-related Fault Detection and Diagnosis Techniques for

Complex Industrial Processes

PENG Kai-Xiang1,2MA Liang1,2ZHANG Kai1,2

Abstract Quality-related fault detection and diagnosis techniques have been extensively applied to the process control ?eld 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 di?erent 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.

Key words Quality-related,fault detection,fault diagnosis,partial least squares(PLS),contribution plot

Citation Peng Kai-Xiang,Ma Liang,Zhang Kai.Review of quality-related fault detection and diagnosis techniques for complex industrial processes.Acta Automatica Sinica,2017,43(3):349?365

为了适应市场对多品种、多规格、高附加值产品的需求,现代工业过程正朝着高效、大型和集成化方向发展.随着生产规模的扩大及复杂性的增加,采用合理的质量相关的故障检测与诊断方法来保障复杂工业过程的安全稳定运行及连续稳定的产品质量已经逐渐成为过程控制领域的首要任务.

复杂工业过程与生俱来的非线性、动态、多模态、多时段、高维度、间歇等特性,使得传统的基于过程机理模型的过程监控方法很难适应实际工业过

收稿日期2016-06-03录用日期2016-10-14

Manuscript received June3,2016;accepted October14,2016国家自然科学基金(61473033)资助

Supported by National Natural Science Foundation of China (61473033)

本文责任编委胡昌华

Recommended by Associate Editor HU Chang-Hua

1.北京科技大学自动化学院北京100083

2.钢铁流程先进控制教育部重点实验室北京100083

1.School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing100083

2.Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education,Beijing100083程的复杂程度;而随着大量的新型仪表、网络化仪表和传感技术应用于生产制造全流程中,大量的过程数据被采集并存储下来,使得基于数据驱动的故障检测与诊断方法成为了当今过程监控领域的主流技术,已成功应用于化工、医药、钢铁冶金、高分子聚合物、微电子制造等生产过程中[1?6].

在基于数据驱动的故障检测与诊断的众多方法中,研究论文和应用案例数量最多的是多元统计过程监控(Multivariate statistical process monitor-ing,MSPM)方法,其依托的主要理论是以主元分析(Principle component analysis,PCA)、偏最小二乘(Partial least squares,PLS)、规范变量分析(Canonical variable analysis,CVA)等为核心的投影降维方法.虽然基于PCA的故障检测与诊断技术能够有效地监测过程变量的波动和异常状况,但是企业管理人员和工程师们可能更加关心的是由过程变量引起的故障是否会导致最终产品质量和产量的变化[7].因此,我们更需要探寻易测的过程变量与难以测量的质量变量间的相关关系,以通过过程变

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