基于混合型判别分析的工业过程监控及故障诊断

陈晓露; 王瑞璇; 王晶; 周靖林

doi:10.16383/j.aas.c180089

基于混合型判别分析的工业过程监控及故障诊断

doi: 10.16383/j.aas.c180089

陈晓露^1,,
王瑞璇^1,,
王晶^1, ,,
周靖林^1,

1.
北京化工大学信息科学与技术学院北京 100029

基金项目:

国家自然科学基金 61573050

国家自然科学基金 61473025

东北大学流程工业综合自动化国家重点实验室开放课题基金 PAL-N201702

详细信息

作者简介:
陈晓露  北京化工大学信息科学与技术学院控制科学与工程专业博士研究生.主要研究方向复杂工业过程的建模与故障诊断, 数据因果关系分析和智能学习算法. E-mail: 18810493810@163.com

王瑞璇  北京化工大学信息科学与技术学院控制工程专业硕士研究生.主要研究方向为复杂工业过程的过程监控和故障诊断.E-mail: wangruixuan11@126.com

周靖林  北京化工大学信息科学与技术学院教授. 1999年获得了大庆石油学院的工程学士学位, 2002年获得了长沙湖南大学硕士学位, 2005年获得了中国科学院自动化研究所博士学位.英国谢菲尔德大学自动控制与系统工程系的学者.主要研究内容为随机分布控制, 故障检测与诊断, 变结构控制及其应用. E-mail: jinglinzhou@mail.buct.edu.cn

通讯作者:
王晶北京化工大学信息科学与技术学院教授. 1994年和1998年分别获得了东北大学自动化专业的学士学位和控制理论与控制工程专业博士学位. 2014年在美国特拉华大学担任访问教授.主要研究方向为非线性、多变量、受约束的工业过程的先进控制方法的应用, 复杂的工业过程的建模, 优化和控制, 化学反应器中聚合物微观质量的非线性模型控制, 过程监控和复杂工业过程的故障诊断.本文通信作者.E-mail: jwang@mail.buct.edu.cn

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出版历程
- 收稿日期: 2018-02-06
- 录用日期: 2018-10-26
- 刊出日期: 2020-08-26

Industrial Process Monitoring and Fault Diagnosis Based on Hybrid Discriminant Analysis

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029

Funds:

National Natural Science Foundation of China 61573050

National Natural Science Foundation of China 61473025

The Open-project Grant Funded by the State Key Laboratory of Synthetical Automation for Process Industry at the Northeastern University PAL-N201702

More Information

Author Bio:
CHEN Xiao-Lu  Ph.D. candidate in control science and engineering at the College of Information Science and Technology, Beijing University of Chemical Technology. Her research interest covers the modeling and fault diagnosis of complex industrial processes, data causality analysis and intelligent learning algorithm

WANG Rui-Xuan  Master student in control engineering at the College of Information Science and Technology, Beijing University of Chemical Technology. Her research interest covers the process monitoring and fault diagnosis of the complex industrial process

ZHOU Jing-Lin  Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor, master and Ph.D. degrees from Daqing Petroleum Institute, Hunan University, and the Institute of Automation, Chinese Academy of Sciences, in 1999, 2002, and 2005, respectively. He was an academic visitor in the Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K. His research interest covers stochastic distribution control, fault detection and diagnosis, variable structure control and their applications

Corresponding author: WANG Jing Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. She received bachelor degree in industry automation, the Ph.D. degree in control theory and control engineering from the Northeastern University, in 1994 and 1998, respectively. She was a visiting professor at the University of Delaware, USA in 2014. Her research interest covers orient, application of advanced control schemes to nonlinear, multivariable, constrained industrial processes, modeling, optimization and control for complex industrial process, nonlinear model-based control of polymer microscopic quality in chemical reactor, process monitoring and fault diagnosis for complex industrial process. Corresponding author of this paper

摘要

摘要: 工业过程数据具有规模性大、复杂性高、变量多、关联性强等特点.如何从数据出发准确并快速地发现故障并处理, 保证过程高效运行意义重大.本文针对复杂的工业过程, 提出了一种多方法结合的混合型过程监控与故障诊断方法, 完成数据分类, 构建故障模型库, 故障在线诊断及可视化相关处理.首先通过常规主成分分析(Principal component analysis, PCA)方法对历史数据进行初筛, 区分出正常和故障信息, 然后利用聚类方法对故障数据集进行分类, 接着利用局部线性指数判别分析方法(Local linear exponential discriminant analysis, LLEDA)建立故障模型库进而进行故障诊断.本文将基于监督学习的LLEDA方法拓展到无监督学习, 便于复杂工业大量无标签数据的处理.最后利用典型的田纳西伊士曼(Tennessee Eastman, TE)过程对所提出的方法进行有效性验证.
- 复杂工业过程 /
- 混合型故障诊断 /
- 局部线性指数判别分析 /
- 可视化
Abstract: Industrial process data has the characteristics of large scale, high complexity, multivariable and strong correlation. There is great signiflcance on how to flnd out and deal with the fault from data accurately and quickly, which can help ensure the e–cient operation of the process. The paper proposed a hybrid multi-method process monitoring and fault diagnosis framework for the complex industrial process. The framework include the data classiflcation, model library establishment, timely diagnosis. Firstly, the historical data is simply screened by principal component analysis (PCA) methods to distinguish normal and fault information. Then the clustering method is used to classify the fault data set, and the fault model libraries are established by local linear exponential discriminant analysis (LLEDA) method. Finally, the fault diagnosis is carried out. The LLEDA method based on supervised learning is extended to unsupervised learning, which facilitates the processing of a large number of unlabeled data in complex industries. Finally, a typical Tennessee Eastman process is used to verify the efiectiveness of the proposed method.
- Industrial process /
- hybrid fault diagnosis /
- local linear exponential discriminant analysis (LLEDA) /
- visualization
Recommended by Associate Editor MU Chao-Xu
注释:

1) 本文责任编委穆朝絮

HTML全文

图 1 混合型故障检测和诊断方法

Fig. 1 Hybrid fault detection and diagnostic framework

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图 2 混合型故障检测和诊断信息流程

Fig. 2 The flow diagram of Hybrid fault detection and diagnostic information

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图 3 TE过程流程图

Fig. 3 The flow diagram of TE process

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图 4 基于PCA的故障检测图

Fig. 4 The fault detection diagram based on PCA

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图 5 层次聚类分析

Fig. 5 Hierarchical cluster analysis

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图 6 故障数据的平行坐标可视化图

Fig. 6 Parallel coordinate visualization of fault data

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图 7 LLEDA方法中邻近点个数对识别率的影响

Fig. 7 The influence of the neighboring points's number on recognition rate in LLEDA method

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图 8 FDA、LLE + FDA和LLEDA方法下不同特征矢量对应的故障4, 8和13的识别率

Fig. 8 Recognition rate of fault 4, 8 and 13 corresponding to different eigenvectors under the FDA, LLE + FDA and LLEDA methods

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图 9 在EDA和LLEDA方法下故障数据的投影

Fig. 9 Projection of fault data under EDA and LLEDA methods

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图 10 LLEDA方法下故障4, 8和13的诊断结果

Fig. 10 Diagnostic results of fault 4, 8 and 13 under the LLEDA method

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表 1 TE故障汇总

Table 1 TE fault summary

编号	描述	类型
IDV (0)	正常操作	-
IDV (1)	A/C进料比率, B成分不变	阶跃
IDV (2)	B成分, A/C进料比不变	阶跃
IDV (3)	D的进口温度	阶跃
IDV (4)	反应器冷却水的入口温度	阶跃
IDV (5)	冷凝器冷却水的入口温度	阶跃
IDV (6)	A进料损失	阶跃
IDV (7)	C存在压力损失-可用性降低	阶跃
IDV (8)	A、B、C进料成分	随机变量
IDV (9)	D的进料温度	随机变量
IDV (10)	C的进料温度	随机变量
IDV (11)	反应器冷却水的入口温度	随机变量
IDV (12)	冷凝器冷却水的入口温度	随机变量
IDV (13)	反应动态	慢漂移
IDV (14)	反应器冷却水阀门	粘住
IDV (15)	冷凝器冷却水阀门	粘住
IDV (16)	未知	未知
IDV (17)	未知	未知
IDV (18)	未知	未知
IDV (19)	未知	未知
IDV (20)	未知	未知
IDV (21)	阀固定在稳态位置	未知的恒定位置

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表 2 基于PCA方法的故障识别率

Table 2 Fault recognition rate based on PCA method

故障识别率	故障1	故障2	故障3	故障4	故障5	故障6	故障7	故障8	故障9	故障10	故障11	故障12	故障13	故障14	故障15	故障16	故障17	故障18	故障19	故障20	故障21
T²	0.5212	0.995	0.9825	0.0225	0.41	0.2625	0.99	1	0.975	0.0362	0.4163	0.9875	0.9513	0.9988	0.0488	0.2325	0.8013	0.8912	0.0675	0.3738	0.3775
SPE	0.8163	0.9988	0.9925	0.2675	1	0.5025	1	1	0.9825	0.235	0.7638	0.99	0.9625	1	0.2625	0.6937	0.975	0.9375	0.5913	0.735	0.6687

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表 3 不同方法下故障4, 8和13的识别率

Table 3 Recognition rate of fault 4, 8 and 13 under different methods

特征向量数	识别率	FDA	LLE + EDA	LLEDA
2	Fault 8	0.525	0.675	0.85
	Fault 4	1	1	1
	Fault 13	0.2275	0.37	0.265
3	Fault 8	0.525	0.67	0.955
	Fault 4	1	1	1
	Fault 13	0.215	0.375	0.125
4	Fault 8	0.5475	0.66	0.945
	Fault 4	1	1	1
	Fault 13	0.225	0.365	0.4
5	Fault 8	0.585	0.655	0.93
	Fault 4	1	1	1
	Fault 13	0.585	0.655	0.93
6	Fault 8	0.5925	0.68	0.9025
	Fault 4	1	1	1
	Fault 13	0.2525	0.345	0.5725
7	Fault 8	0.605	0.6475	0.8875
	Fault 4	1	1	1
	Fault 13	0.265	0.3925	0.6025
8	Fault 8	0.615	0.6775	0.8775
	Fault 4	1	1	1
	Fault 13	0.2475	0.375	0.62
9	Fault 8	0.6525	0.675	0.8925
	Fault 4	1	1	1
	Fault 13	0.2425	0.255	0.615
10	Fault 8	0.6625	0.7175	0.9125
	Fault 4	1	1	1
	Fault 13	0.26	0.2325	0.7325

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