工业过程故障根源诊断与传播路径识别技术综述

马亮; 彭开香; 董洁

doi:10.16383/j.aas.c200257

工业过程故障根源诊断与传播路径识别技术综述

doi: 10.16383/j.aas.c200257

马亮^{1, 2, 3,},
彭开香^{2, 3,},
董洁^{2, 3,}

1.
北京科技大学顺德研究生院佛山 528399
2.
北京科技大学自动化学院北京 100083
3.
工业过程知识自动化教育部重点实验室北京 100083

基金项目: 国家自然科学基金(62003030, 61873024,61773053), 中国博士后科学基金资助项目(2019M660464), 广东省基础与应用基础研究基金(2019A1515110991), 中央高校基本科研业务费专项资金资助项目(FRF-TP-19-049A1Z), 北京科技大学顺德研究生院博士后科研经费(2020BH003)资助

详细信息

作者简介:
马亮：北京科技大学自动化学院副教授. 主要研究方向为复杂工业过程分布式监测与智能诊断. E-mail: liangma@ustb.edu.cn

彭开香：北京科技大学自动化学院教授. 主要研究方向为复杂工业过程故障诊断与容错控制. 本文通信作者. E-mail: kaixiang@ustb.edu.cn

董洁：北京科技大学自动化学院教授. 主要研究方向为复杂工业系统的故障诊断与一体化控制. E-mail: dongjie@ies.ustb.edu.cn

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出版历程
- 收稿日期: 2020-04-27
- 录用日期: 2020-08-27
- 刊出日期: 2022-07-01

Review of Root Cause Diagnosis and Propagation Path Identification Techniques for Faults in Industrial Processes

MA Liang^{1, 2, 3
,},
PENG Kai-Xiang^{2, 3
,},
DONG Jie^{2, 3
,}

1.
Shunde Graduate School of University of Science and Technology Beijing, Foshan 528399
2.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083
3.
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (62003030, 61873024, 61773053), Project Funded by China Postdoctoral Science Foundation (2019M660464), Guangdong Basic and Applied Basic Research Foundation (2019A1515110991), Fundamental Research Funds for the China Central Universities (FRF-TP-19-049A1Z), and Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing (2020BH003)

More Information

Author Bio:
MA Liang　Associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His main research interest is distributed monitoring and intelligent diagnosis for complex industrial processes

PENG Kai-Xiang 　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His main research interest is fault diagnosis and fault-tolerant control for complex industrial processes. Corresponding author of this paper

DONG Jie　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her main research interest is fault diagnosis and integrated control for complex industrial system

摘要

摘要: 故障根源诊断与传播路径识别是故障诊断框架下的关键核心问题, 是保障工业过程安全生产及获得可靠产品质量的有效手段, 是当前过程控制领域的研究热点. 该技术的研究不仅丰富了故障诊断理论, 而且对故障诊断技术在工程中的推广与应用具有重要意义. 阐述了基于知识、数据及知识与数据联合驱动的故障根源诊断与传播路径识别方法的基本思想、适用条件和优劣特点, 分类概述了相关方法的研究现状. 探讨了该领域亟待解决的问题及未来的发展方向, 包括: 1)“三个维度”视角下的工业过程故障根源诊断与传播路径识别; 2)基于制造大数据分析与因果关系挖掘的工业过程质量精准追溯; 3)面向传播、耦合、多重并发特性的工业过程复合故障分布式诊断; 4)基于多源异构动态信息融合的工业过程异常工况时空追溯可视化.
- 根源诊断 /
- 传播路径识别 /
- 因果关系分析 /
- 故障诊断 /
- 工业过程
Abstract: Root cause diagnosis and propagation path identification are the key issues under fault diagnosis framework, which are effective means to guarantee safety production and obtain reliable product quality for industrial processes, and thus, have recently become active areas of process control field. Research of these two techniques not only enriches the fault diagnosis theory, but also has important significance for promotion and application of fault diagnosis technology in actual projects. In this paper, the basic ideas, application conditions, advantages, as well as disadvantages of knowledge based, data based and joint knowledge and data based methods are illustrated. Moreover, research status of related methods is classified and summarized. Finally, some urgent problems and future directions in this field are discussed, including: 1) Root cause diagnosis and propagation path identification under three-dimensional perspective; 2) Manufacturing big data analysis and causality mining based accurate quality tracing; 3) Distributed diagnosis for propagative, coupled, and concurrent multiple faults; 4) Multi-source heterogeneous dynamic information fusion based timespace traceability visualization for abnormal conditions.
- Root cause diagnosis /
- propagation path identification /
- causality analysis /
- fault diagnosis /
- industrial processes
注释:

1) 收稿日期2020-04-27 录用日期2020-08-27 Manuscript received April 27, 2020; accepted August 27, 2020 国家自然科学基金(62003030, 61873024, 61773053), 中国博士后科学基金资助项目(2019M660464), 广东省基础与应用基础研究基金(2019A1515110991), 中央高校基本科研业务费专项资金资助项目(FRF-TP-19-049A1Z), 北京科技大学顺德研究生院博士后科研经费(2020BH003)资助 Supported by National Natural Science Foundation of China (62003030, 61873024, 61773053), Project Funded by China Postdoctoral Science Foundation (2019M660464), Guangdong Basic and Applied Basic Research Foundation (2019A1515110991), Fundamental Research Funds for the China Central Universities (FRF-TP-19-049A1Z), and Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing (2020BH003) 本文责任编委杨涛 Recommended by Associate Editor YANG Tao 1. 北京科技大学顺德研究生院佛山528399 2. 北京科技大学自动化学院北京100083 3. 工业过程知识自动化教育部重点实验室北京100083

2) 1. Shunde Graduate School of University of Science and Technology Beijing, Foshan 528399 2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083 3. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083

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图 1 近10年论文数量统计图

Fig. 1 Statistical graph of quantity of papers in recent ten years

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图 2 近10年三类方法的论文数量分析图

Fig. 2 Statistical graph of quantity of papers with three methods in recent ten years

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图 3 近5年三类方法的论文数量分析图

Fig. 3 Statistical graph of quantity of papers with three methods in recent five years

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图 4 双容水箱控制系统示意图

Fig. 4 Schematic diagram of two-tank control systems

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图 5 双容水箱控制回路符号有向图

Fig. 5 Singed directed graph of two-tank control loops

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图 6 热轧过程综合自动化系统示意图

Fig. 6 Schematic layout of integrated automation system for hot rolling process

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表 1 基于知识的故障根源诊断与传播路径识别方法对比

Table 1 Comparisons of knowledge based root cause diagnosis and propagation path identification methods

方法	优势	劣势
SDG	模型构建简单, 分析完备性强	面向定性信息缺乏的工业系统易产生虚假和冗余解
邻接矩阵	基本原理简单, 结果易于理解	模型构建工作量巨大, 缺少节点间间接关联关系信息
FT	模型直观明了, 推理过程清晰	高度依赖专家经验, 对不确定性问题诊断准确率较低
MFM	语义符号简单, 诊断规则一致	面向复杂系统功能节点抽象与深层次诊断难度较大
Petri 网	数学表述严格, 图形表达直观	建模高度依赖机理, 随机不确定条件下诊断准确率低

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表 2 基于数据的故障根源诊断与传播路径识别方法对比

Table 2 Comparisons of data based root cause diagnosis and propagation path identification methods

方法	优势	劣势
CCA	基本原理简单, 过程易于实现	无法分析非线性因果关系及区分直接与间接因果关系
GC	计算复杂度低, 具有预测功能	无法分析非线性因果关系, 容易出现虚假冗余因果关系
TE	可分析线性及非线性因果关系	联合概率分布函数计算量大, 无法区分直接与间接因果关系
KNN	对数据无假设, 计算复杂度低	对样本不平衡问题诊断效果较差, $ k $值选择无理论依据
BN	能够融合表达过程多源信息	网络训练过程相对繁琐, 最优网络结构难以获取与保证

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