基于加权统计特征KICA的故障检测与诊断方法

doi:10.11949/0438-1157.20211295

化工学报 ›› 2022, Vol. 73 ›› Issue (2): 827-837.doi: 10.11949/0438-1157.20211295

基于加权统计特征KICA的故障检测与诊断方法

张成¹(),潘立志²,李元²()

^1.沈阳化工大学理学院，辽宁沈阳 110142
^2.沈阳化工大学技术过程故障诊断与安全性研究中心，辽宁沈阳 110142

收稿日期:2021-09-06 修回日期:2021-11-09 出版日期:2022-02-05 发布日期:2022-02-18
通讯作者: 李元 E-mail:zhangcheng@syuct.edu.cn;li-yuan@mail.tsinghua.edu.cn
作者简介:张成（1979—），男，博士，副教授，zhangcheng@syuct.edu.cn
基金资助:
国家自然科学基金项目(61673279);辽宁省自然科学基金项目(2019-MS-262);辽宁省教育厅基金项目(LJ2019013)

Fault detection and diagnosis method based on weighted statistical feature KICA

Cheng ZHANG¹(),Lizhi PAN²,Yuan LI²()

^1.College of Science, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China
^2.Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142, Liaoning, China

Received:2021-09-06 Revised:2021-11-09 Published:2022-02-05 Online:2022-02-18
Contact: Yuan LI E-mail:zhangcheng@syuct.edu.cn;li-yuan@mail.tsinghua.edu.cn

摘要/Abstract

摘要：

针对核独立元分析（kernel independent component analysis， KICA）在非线性动态过程中对微小故障检测率低的问题，提出一种基于加权统计特征KICA（weighted statistical feature KICA， WSFKICA）的故障检测与诊断方法。首先，利用KICA从原始数据中捕获独立元数据和残差数据；然后，通过加权统计特征和滑动窗口获取改进统计特征数据集，并由此数据集构建统计量进行故障检测；最后，利用基于变量贡献图的方法进行过程故障诊断。与传统KICA统计量相比，所提方法的统计量对非线性动态过程中的微小故障具有更高的故障检测性能。应用该方法对一个数值例子和田纳西-伊斯曼（Tennessee-Eastman， TE）过程进行仿真测试，仿真结果显示出所提方法相对于独立元分析（ICA）、KICA、核主成分分析（kernel principal component analysis， KPCA）和统计局部核主成分分析（statistical local kernel principal component analysis， SLKPCA）检测的优势。

关键词: 独立元分析, 微小故障, 统计特征, 过程控制, 参数估值, 故障诊断, 动态仿真

Abstract:

Aiming at the problem of low early fault detection rate in the nonlinear dynamic process of kernel independent component analysis (KICA), a fault detection and diagnosis approach based on weighted statistical feature KICA (WSFKICA) is proposed. First, the independent component data and residual data are captured from the original data by using KICA. Then, the improved statistical feature data set is obtained by weighted statistical feature and sliding window, and the statistics is constructed from the data set for fault detection. Finally, the method based on contribution chart of monitored variables is used for process fault diagnosis. Compared with traditional KICA statistics, the statistics of the proposed approach has higher fault detection performance for incipient faults in nonlinear dynamic processes. The proposed approach is tested in a simulated case and in the Tennessee-Eastman (TE) process. The simulation results show that the proposed approach has an advantage over ICA, KICA, KPCA and statistical local kernel principal component analysis (SLKPCA).

Key words: independent component analysis, incipient faults, statistical feature, process control, parameter estimation, fault diagnosis, dynamic simulation

中图分类号:

TP 277

张成, 潘立志, 李元. 基于加权统计特征KICA的故障检测与诊断方法[J]. 化工学报, 2022, 73(2): 827-837.

ZHANG Cheng, PAN Lizhi, LI Yuan. Fault detection and diagnosis method based on weighted statistical feature KICA[J]. CIESC Journal, 2022, 73(2): 827-837.

图/表 25

图1

图2

表1

参数?β?取不同值时的校验准确率"

$β$	校验准确率		平均值
$β$	R	H	平均值
$2$	90.8	76.7	83.7
$3$	91.7	75.6	83.6
$4$	97.6	71.4	84.5
$5$	97.6	69.1	83.3
$6$	97.6	65.7	81.6

表1

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

表2

数值例子中各方法的故障检测率"

ICA		KICA		KPCA		SLKPCA		WSFKICA
$I 2$	$S P E$	$I 2$	$S P E$	$Τ 2$	$S P E$	$Τ 2$	$S P E$	$R$	$H$
27.6	10.4	27.8	18.6	6.4	2.8	86	87	97.2	92.2

表2

图14

表3

TE过程中各方法的故障检测率"

序号	ICA		KICA		KPCA		WSFKICA
序号	$I 2$	$S P E$	$I 2$	$S P E$	$Τ 2$	$S P E$	$R$	$H$
1	95.7	99.3	98	98.7	99.1	99.3	99.7	100
2	87.9	94.9	86.6	94	94.4	94.7	93	99.4
3	11.1	10.7	1.6	1.3	0.6	10.3	21	98.6
4	99.9	99.9	99.9	99.9	99.9	99.9	100	100
5	34.7	19.9	4	0.6	1	23.3	43	99.6
6	—	—	—	—	—	—	—	—
7	99.9	99.9	99.9	99.9	99.9	99.9	100	100
8	86.6	88.6	86.4	85.9	87.6	89.4	90.4	93
9	8.7	18.29	2.4	7.3	4.3	13.9	38.3	88.4
10	88	88.6	80.9	84.7	86.9	89.1	93.3	96.7
11	89.4	98.7	95.1	96.6	98.4	98.4	99.4	100
12	32.4	74.1	33.1	35.9	40.4	77	95.1	100
13	92	94.71	93.6	94.1	94.3	94.9	96.7	100
14	83.9	98.9	97.9	98.7	98.7	98.9	100	100
15	14.6	0.4	0.1	0	0	1.9	54.4	99.4
16	5	6.1	6.3	5.9	1.4	0.9	6.7	5.7
17	81.1	84.9	76.3	80	84.7	84.9	88.7	90.7
18	52.9	62.86	48.1	49.6	51.6	62.1	68.7	70.9
19	93.7	95	88.9	90.6	93.1	95.4	94	98.7
20	85.9	85.1	83.6	85.4	85.3	85.3	87.4	91.4
21	5	6.4	6.4	5.7	1.6	0.9	7.3	12.4
22	17.3	13.1	1.6	0.9	0.4	13.1	23.9	87.4
23	16.7	12.6	11.4	18.7	4.7	3.7	44.9	100
24	79.3	87.7	70.1	83.9	83	88.4	91.4	93.1
25	94.1	93.4	83.7	88.3	87	95	98.1	100
26	62.3	87.1	58.1	77.3	85.6	88.6	88.1	95.1
27	50.1	87	59.7	58.4	71.6	90	96.4	100
28	15.7	13	2.4	0.4	0.6	15	3.7	91.7

表3

图15

图16

图17

图18

图19

图20

图21

图22

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基于加权统计特征KICA的故障检测与诊断方法

Fault detection and diagnosis method based on weighted statistical feature KICA

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