一种面向工业过程的质量异常检测与故障量化评估方法

董洁; 张伟; 彭开香; 马亮

doi:10.16383/j.aas.c190880

一种面向工业过程的质量异常检测与故障量化评估方法

doi: 10.16383/j.aas.c190880

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

1.
北京科技大学自动化学院北京 100083
2.
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(62273031, 61773053, 61873024), 中央高校基本科研业务费基金(FRF-TP-19-049A1Z), 国家重点研发计划(2017YFB0306403)资助

详细信息

作者简介:
董洁：北京科技大学自动化学院教授. 2007年获得北京科技大学控制科学与工程博士学位. 主要研究方向为智能控制理论与应用, 过程监控与故障诊断, 复杂系统建模与控制.E-mail: dongjie@ies.ustb.edu.cn

张伟：北京科技大学自动化学院硕士研究生. 主要研究方向为数据驱动的故障诊断与容错控制.E-mail: zw719228639@163.com

彭开香：北京科技大学自动化学院教授. 2007年获得北京科技大学控制科学与工程博士学位. 主要研究方向为复杂工业系统故障诊断与容错控制. 本文通信作者.E-mail: kaixiang@ustb.edu.cn

马亮：北京科技大学自动化学院副教授. 2019年获得北京科技大学控制理论与控制工程博士学位. 主要研究方向为数据驱动的故障诊断与容错控制.E-mail: mlypplover@sina.com

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出版历程
- 收稿日期: 2019-12-24
- 录用日期: 2020-04-06
- 网络出版日期: 2022-09-16
- 刊出日期: 2022-10-14

A Novel Method of Quality Abnormality Detection and Fault Quantitative Assessment for Industrial Processes

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

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

Funds: Supported by National Natural Science Foundation of China (62273031, 61773053, 61873024), Fundamental Research Funds for the China Central Universities (FRF-TP-19-049A1Z), and National Key Research and Develepment Program of China (2017YFB0306403)

More Information

Author Bio:
DONG Jie　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She received her Ph.D. degree in control science and engineering from University of Science and Technology Beijing in 2007. Her research interest covers intelligent control theory and application, process monitoring and fault diagnosis, and complex system modeling and control

ZHANG Wei　Master student at University of Science and Technology Beijing. His research interest covers data-based fault diagnosis and fault-tolerant control

PENG Kai-Xiang　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his Ph.D. degree in control science and engineering from University of Science and Technology Beijing in 2007. His research interest covers fault diagnosis and fault-tolerant control for complex industrial system. Corresponding author of this paper

MA Liang　Associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his Ph.D. degree in control science and engineering from University of Science and Technology Beijing in 2019. His research interest covers data-based fault diagnosis and fault-tolerant control

摘要

摘要: 质量异常检测(Quality abnormality detection, QAD)与故障量化评估(Fault quantitative assessment, FQA)作为工业过程监控的关键环节, 是故障诊断领域的研究热点. 本文提出了一种新的工业过程质量异常检测与故障量化评估方法. 首先, 采用弹性网络(Elastic net, EN)算法构建了质量相关的变量候选集, 借助典型相关分析(Canonical correlation analysis, CCA)构建了质量相关的特征向量, 并引入支持向量数据描述(Support vector data description, SVDD)实现质量异常检测. 其次, 从优化近邻点距离的角度提出了增强局部线性嵌入(Enhanced local linear embedding, ELLE)算法, 并提出了基于CCA-ELLE的质量异常故障量化评估方法. 最后, 通过田纳西−伊斯曼(Tennessee-Eastman, TE)过程进行仿真验证, 并与传统的方法进行对比分析, 实验结果验证了所提方法的优越性和有效性.
- 质量异常检测 /
- 支持向量数据描述 /
- 故障量化评估 /
- 局部线性嵌入 /
- 工业过程
Abstract: As the key link of industrial process monitoring, quality abnormality detection (QAD) and fault quantitative assessment (FQA) are the research hotspot in the field of fault diagnosis. In this paper, a novel framework of industrial process QAD and FQA is proposed. Firstly, the candidate set of quality related variables is constructed by elastic net (EN) algorithm. The quality related eigenvectors are constructed by canonical correlation analysis (CCA), and the support vector data description (SVDD) is introduced to detect quality anomaly. Secondly, an enhanced local linear embedding (ELLE) algorithm is proposed from the perspective of optimizing the distance between adjacent points, and a quantitative assessment method based on CCA- ELLE is proposed. Finally, the proposed framework is simulated and compared with some traditional methods by Tennessee-Eastman (TE) process simulation platform. The experimental results verify the superiority and effectiveness of the proposed method.
- Quality abnormality detection (QAD) /
- support vector data description (SVDD) /
- fault quantitative assessment (FQA) /
- local linear embedding /
- industrial process

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图 1 评估指标示意图

Fig. 1 Schematic diagram of evaluation indicator

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图 2 质量异常检测与故障量化评估流程图

Fig. 2 Flowchart of QAD and FQE

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图 3 参数分析

Fig. 3 Analysis of parameters

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图 4 两种方法的故障检测结果

Fig. 4 Detection results of the two methods

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图 5 CCA-ELLE二维投影

Fig. 5 CCA-ELLE-based two-dimensional projection

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图 6 两种方法的二维投影

Fig. 6 Two-dimensional projection results of the two methods

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图 7 两种方法的量化评估结果

Fig. 7 Evaluation results of the two methods

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表 1 TE过程变量

Table 1 Process variables in the TE process

变量描述	变量描述
1) 物料 A 流量	12) 气/液分离器液位
2) 物料 D 流量	13) 气/液分离器压力
3) 物料 E 流量	14) 气/液分离器出口流量
4) 物料 C 流量	15) 汽提塔液位
5) 压缩机返回物料流量	16) 汽提塔压力
6) 反应器给料流量	17) 汽提塔塔底流量
7) 反应器压力	18) 汽提塔温度
8) 反应器液位	19) 汽提塔蒸汽流量
9) 反应器温度	20) 压缩机功率
10) 排空物料流量	21) 反应器冷却水出口温度
11) 气/液分离器温度	22) 冷凝器冷却水出口温度

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表 2 验证数据集

Table 2 Data sets used for validation

故障程度	样本数量
正常状态样本 (TE 标准数据)	500 (训练集)
故障数据 (TE 标准数据)	960 (测试集)
正常状态样本 (生成数据 FS = 0)	500 + 160 (训练集 + 测试集)
故障程度 1 (生成数据 FS = 0.2)	160 (测试集)
故障程度 2 (生成数据 FS = 0.4)	160 (测试集)
故障程度 3 (生成数据 FS = 0.6)	160 (测试集)
故障程度 4 (生成数据 FS = 0.8)	160 (测试集)
故障程度 5 (生成数据 FS = 1.0)	160 (测试集)

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表 3 两种方法的性能比较

Table 3 Comparison of the two methods

方法	误报率 (%)	检测率 (%)	建模运行时间 (s)
KPLS	3.75	98.375	0.307
CCA-SVDD	0	97.75	0.033

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