基于<b>DPCA</b>残差互异度的故障检测与诊断方法

张成; 戴絮年; 李元

doi:10.16383/j.aas.c190884

基于DPCA残差互异度的故障检测与诊断方法

doi: 10.16383/j.aas.c190884

张成^1,,
戴絮年^1,,
李元^1,

1.
沈阳化工大学技术过程故障诊断与安全性研究中心沈阳 110142

基金项目: 国家自然科学基金项目(61490701, 61673279), 辽宁省自然基金项目(2019-MS-262), 辽宁省教育厅基金项目(LJ2019013)

详细信息

作者简介:
张成：沈阳化工大学副教授, 东北大学博士研究生. 主要研究方向为复杂工业过程故障诊断. E-mail: zhangcheng@syuct.edu.cn

戴絮年：沈阳化工大学信息工程学院硕士研究生. 主要研究方向为基于数据驱动的多工况过程故障检测. E-mail: daixunian1996@163.com

李元：沈阳化工大学教授. 2004 年获得东北大学博士学位. 主要研究方向为系统识别, 故障检测, 复杂过程故障诊断. 本文通信作者. E-mail: li-yuan@mail.tsinghua.edu.cn

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出版历程
- 收稿日期: 2019-12-24
- 录用日期: 2020-03-11
- 网络出版日期: 2022-01-10
- 刊出日期: 2022-01-25

Fault Detection and Diagnosis Based on Residual Dissimilarity in Dynamic Principal Component Analysis

1.
Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142

Funds: Supported by National Natural Science Foundation of China (61490701, 61673279), Liaoning Natural Science Fund Project (2019-MS-262), Liaoning Provincial Department of Education Fund Project (LJ2019013)

More Information

Author Bio:
ZHANG Cheng　Associate professor at Shenyang University of Chemical Technology, Ph.D. candidate at North-eastern University. His research interest covers fault diagnosis of processes

DAI Xu-Nian　Master student at the Institute of Information Engineering, Shenyang University of Chemical Engineering. His research interest covers data-driven based techniques for multiple operating conditions fault detection

LI Yuan　Professor at Shenyang University of Chemical Technology. She received her Ph.D. degree from Northeastern University in 2004. Her research interest covers system identification, fault detection, and complex process fault diagnosis. Corresponding author of this paper

摘要

摘要: 针对动态主元分析方法中残差自相关性降低过程故障检测率问题, 提出基于动态主元分析残差互异度的故障检测与诊断方法. 首先, 应用动态主元分析(Dynamic principal component analysis, DPCA)计算动态过程数据的残差得分; 接下来, 应用滑动窗口技术并结合互异度指标(Dissimilarity)来监控过程残差得分状态; 最后, 利用基于变量贡献图的方法进行过程故障诊断分析. 本文方法通过DPCA捕获过程的动态特征, 同时互异度指标区别于传统的平方预测误差(Square prediction error, SPE), 它可以有效地对具有自相关性的残差得分进行过程状态监控. 通过一个数值例子和Tennessee Eastman (TE)过程的仿真实验并与传统方法对比分析, 仿真结果进一步证实了本文方法的有效性.
- 动态主元分析 /
- 互异度 /
- 滑动窗口 /
- 故障诊断
Abstract: Aiming at the problem of reducing process fault detection rate because of residual autocorrelation in dynamic principal component analysis, a novel fault detection and diagnosis based on residual dissimilarity in dynamic principal component analysis is proposed in this paper. Firstly, Dynamic principal component analysis (DPCA) is used to calculate residual score of a dynamic process. Next, moving window technology and dissimilarity index are utilized to monitor the status of this process in residual score. Finally, a fault diagnosis method based on contribution chart of monitored variables is used for discovering the reason causing abnormal change of this process. The proposed method to capture dynamic characteristics of a process through DPCA, meanwhile, the proposed dissimilarity index, which is different from the conventional squared prediction error (SPE), can effectively monitor the process in which the residual scores contain significant autocorrelation. The effectiveness of DPCA-Diss is tested in a numerical case and the Tennessee Eastman (TE) process.
- Dynamic principal component analysis (DPCA) /
- dissimilarity /
- moving window /
- fault diagnosis

HTML全文

图 1 主元累计方差贡献率

Fig. 1 Cumulative percent variance of principal component

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图 2 DPCA残差得分自相关性

Fig. 2 Autocorrelation of residual score in DPCA

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图 3 PCA-SPE故障检测结果

Fig. 3 Fault detection results using PCA-SPE

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图 4 DPCA-SPE故障检测结果

Fig. 4 Fault detection results using DPCA-SPE

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图 5 DPCA残差得分自相关性

Fig. 5 Autocorrelation of residual score in DPCA

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图 6 DPCA-Diss故障检测结果

Fig. 6 Fault detection results using DPCA-Diss

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图 7 监控变量贡献图

Fig. 7 Contribution charts of the monitored variables

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图 8 输入变量u

Fig. 8 Input variable u

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图 9 TE过程

Fig. 9 Layout of TE process

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图 10 故障5检测结果

Fig. 10 Detection results of Fault5

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图 11 故障19检测结果

Fig. 11 Detection results of Fault19

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图 12 故障5贡献图

Fig. 12 Contribution chart of Fault5

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图 13 故障10贡献图

Fig. 13 Contribution chart of Fault10

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图 14 变量33

Fig. 14 Variable 33

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图 15 变量18

Fig. 15 Variable 18

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表 1 各种方法故障检测率

Table 1 Fault detection rates using different methods

检测方法	FDR (%)
PCA-SPE	4.4
DPCA-SPE	16.6
DPCA-Diss	95.1

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表 2 各种方法故障检测率(%)

Table 2 Fault detection rates using different methods (%)

故障号	PCA-SPE	KPCA-SPE	DPCA-SPE	Diss	DPCA-Diss
1	99.75	93.75	98.88	34.25	96.13
2	91.88	94.50	91.75	8.88	93.63
4	99.88	53.88	4.25	9.75	21.63
5	64.25	7.50	100.00	14.13	99.38
6	100.00	63.38	100.00	96.38	99.88
7	34.25	98.63	20.00	30.25	48.75
8	79.38	40.25	65.25	70.25	96.75
10	56.50	3.63	90.13	30.88	96.63
11	67.38	53.00	7.75	76.75	91.00
12	87.00	57.75	97.75	98.88	99.75
13	94.50	62.00	93.13	65.75	93.50
14	89.13	88.13	7.75	62.00	94.38
15	3.25	3.13	2.50	2.50	77.50
16	56.63	2.88	89.63	54.25	98.38
17	94.88	74.00	71.88	88.50	96.63
18	90.38	85.75	89.50	87.00	89.00
19	51.88	6.00	46.25	73.13	97.13
20	60.13	21.63	87.38	71.00	90.00
21	37.00	5.25	16.75	22.50	33.88

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