基于多块信息提取的PCA故障诊断方法

doi:10.11949/j.issn.0438-1157.20180842

Abstract

Abstract:

Traditional monitoring methods only use sensor observation information to perform process fault monitoring, while ignoring other valid information contained in the original data. Aiming to this problem, a PCA fault monitoring algorithm based on multi-block information extraction is proposed. Firstly, two kinds of information of the cumulative error and the change rate of process variables are defined, so that new feature information can be extracted from the data. The process is divided into three sub-blocks based on each feature, and each sub-block is processed by the PCA method. Modeling and monitoring are carried out, and monitoring results are integrated by Bayesian method. Finally, a fault diagnosis method with weighted contribution graph is proposed to find the source variable which causes the fault. The validity and feasibility of the proposed method are demonstrated by numerical examples and the application of Tennessee-Eastman (TE) process monitoring.

Key words: principal component analysis, algorithm, model, fault diagnosis, information extraction, multi-block modelling

CLC Number:

TP277

Bingbin GU, Weili XIONG. Fault diagnosis based on PCA method with multi-block information extraction[J].CIESC Journal, 2019, 70(2): 736-749.

Figures/Tables 15

Fig.1

Table 1

Sub-block division results"

子块编号	训练数据集	测试样本
1	$X$	$x t e s t$
2	$X I$	$x t e s t I$
3	$X D$	$x t e s t D$

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 2

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Table 3

Comparison of some state of multi-block monitoring methods"

故障编号	PCA	DPCA	MSMBPCA	FBPCA	MBI-PCA
故障编号	T²or SPE	$B I C T 2$ or SPE	$B I C T 2$ or SPE	$B I C T 2$ or SPE	$B I C T 2$ or SPE
0	0.022	0.026	0.0214	0.06	0.0144
1	0	0	0	0	0
2	0.02	0.02	0.01	0.01	0.01
3	0.97	0.94	—	0.88	0.96
4	0	0	0	0	0
5	0.75	0	0	0	0
6	0	0	0	0	0
7	0	0	0	0	0
8	0.03	0.02	0.03	0.01	0.01
9	0.98	0.93	—	0.91	0.95
10	0.7	0.44	0.18	0.11	0.6
11	0.25	0.22	0.3	0.19	0.03
12	0.02	0.01	0.02	0.01	0.01
13	0.05	0.05	0.05	0.05	0.05
14	0	0	0	0	0
15	0.97	0.93	—	0.78	0.95
16	0.73	0.52	0.15	0.1	0.30
17	0.05	0.03	0.13	0.03	0.03
18	0.10	0.09	0.1	0.1	0.09
19	0.88	0.8	0.48	0.3	0.44
20	0.50	0.27	0.33	0.1	0.36
21	0.53	0.44	0.46	0.33	0.35

Table 3

References 30

1	QinS J. Statistical process monitoring: basics and beyond [J]. Journal of Chemometrics, 2003, 17(8/9): 480-502
2	GeZ Q, SongZ H, GaoF R. Review of recent research on data-based process monitoring [J]. Industrial & Engineering Chemistry Research, 2013, 52(10): 3543-3562.
3	GeZ Q, SongZ H. Multivariate Statistical Process Control [M]. London: Springer, 2013: 169-182.
4	韩敏, 张占奎. 基于改进核主成分分析的故障检测与诊断方法[J]. 化工学报, 2015, 66(6): 2139-2149.
	HanM, ZhangZ K. Fault detection and diagnosis method based on modified kernel principal component analysis [J]. CIESC Journal, 2015, 66(6): 2139-2149.
5	李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9.
	LiH, XiaoD Y. Survey on data driven fault diagnosis methods [J]. Control and Decision, 2011, 26(1): 1-9.
6	LeeJ M, YooC K, SangW C, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1): 223-234.
7	GeZ Q, SongZ H. Process monitoring based on independent component analysis−principal component analysis (ICA− PCA) and similarity factors [J]. Industrial & Engineering Chemistry Research, 2007, 46(7): 2054-2063.
8	LiG, QinS J, ZhouD H. A new method of dynamic latent-variable modeling for process monitoring [J]. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6438-6445.
9	谭帅, 王福利, 常玉清, 等. 基于差分分段PCA的多模态过程故障监测[J]. 自动化学报, 2010, 36(11): 1626-1636.
	TanS, WangF L, ChangY Q, et al. Fault detection of multi-mode process using segmented PCA based on differential transform [J]. Acta Automatica Sinica, 2010, 36(11): 1626-1636.
10	王健, 冯健, 韩志艳. 基于流形学习的局部保持 PCA 算法在故障检测中的应用[J]. 控制与决策, 2013, 28(5): 683-687.
	WangJ, FengJ, HanZ Y. Locally preserving PCA method based on manifold learning and its application in fault detection[J]. Control and Decision, 2013, 28(5): 683-687.
11	ZhaoC, GaoF. Fault-relevant principal component analysis (FPCA) method for multivariate statistical modeling and process monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 2014, 133: 1-16.
12	MacgregorJ F, JaeckleC, KiparissidesC, et al. Process monitoring and diagnosis by multiblock PLS methods [J]. AIChE Journal, 1994, 40(5): 826-838.
13	WesterhuisJ A, KourtiT, MacGregorJ F. Analysis of multiblock and hierarchical PCA and PLS models [J]. Journal of Chemometrics, 1998, 12(5): 301-321.
14	GeZ Q, ZhangM, SongZ H. Nonlinear process monitoring based on linear subspace and Bayesian inference [J]. Journal of Process Control, 2010, 20(5): 676-688.
15	GeZ Q, SongZ H. Distributed PCA model for plant-wide process monitoring [J]. Industrial & Engineering Chemistry Research, 2013, 52(5): 1947-1957.
16	WangB, YanX F, JiangQ C, et al. Generalized Dice's coefficient-based multi-block principal component analysis with Bayesian inference for plant-wide process monitoring[J]. Journal of Chemometrics, 2015, 29(3): 165-178.
17	HuangJ, YanX F. Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference [J]. Chemometrics & Intelligent Laboratory Systems, 2015, 148: 115-127.
18	JiangQ C, YanX F, HuangB. Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference [J]. IEEE Transactions on Industrial Electronics, 2015, 63(1): 377-386.
19	GeZ Q, ChenJ. Plant-wide industrial process monitoring: a distributed modeling framework[J]. IEEE Transactions on Industrial Informatics, 2017, 12(1): 310-321.
20	叶昊, 徐海鹏. 基于重构的传感器故障诊断贡献分析[J]. 清华大学学报(自然科学版), 2012, 52(1): 36-39.
	YeH, XuH P. Reconstruction-based contribution analysis for sensor fault diagnostics[J]. Journal of Tsinghua University Science and Technology, 2012, 52(1): 36-39.
21	彭开香, 马亮, 张凯. 复杂工业过程质量相关的故障检测与诊断技术综述[J]. 自动化学报, 2017, 43(3): 349-365.
	PengK X, MaL, ZhangK. Review of quality-related fault detection and diagnosis techniques for complex industrial processes[J]. Acta Automatica Sinica, 2017, 43(3): 349-365.
22	LauC K, GhoshK, HussainM A, et al. Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS[J]. Chemometrics & Intelligent Laboratory Systems, 2013, 120(2): 1-14.
23	ZhouD H, LiG, QinS J. Total projection to latent structures for process monitoring[J]. AIChE Journal, 2010, 56(1): 168-178.
24	王海清, 余世明. 基于故障诊断性能优化的主元个数选取方法[J]. 化工学报, 2004, 55(2): 214-219.
	WangH Q, YuS M. Selection of the number of principal components based on the fault diagnosing performance analysis[J]. CIESC Journal, 2004, 55(2): 214-219.
25	苏林, 尚朝轩, 连光耀, 等. 基于故障检测率的主元个数确定方法[J]. 计算机测量与控制, 2011, 19(8): 1857-1860.
	SuL, ShangC X, LianG Y, et al. Selection of number of principal components based on fault detection accuracy[J]. Computer Measurement & Control, 2011, 19(8): 1857-1860.
26	ShenY, DingS X, HaghaniA, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of Process Control, 2012, 22(9): 1567-1581.
27	RussellE L, ChiangL H , BraatzR D . Tennessee Eastman Process[M]// Fault Detection and Diagnosis in Industrial Systems. London: Springer, 2001: 103-112.
28	衷路生, 何东, 龚锦红, 等. 基于分布式ICA-PCA模型的工业过程故障监测[J]. 化工学报, 2015, 66(11): 4546-4554.
	ZhongL S, HED, GongJ H, et al. Fault monitoring of industrial process based on distributed ICA-PCA model[J]. CIESC Journal, 2015, 66(11): 4546-4554.
29	王海清, 蒋宁. 自适应Kernel学习网络在TE过程组分仪建模中的应用[J]. 化工学报, 2007, 58(2): 425-430.
	WangH Q, JiangN. Adaptive Kernel learning networks with application to modeling of analyzer in TE process[J]. Journal of Chemical Industry and Engineering（China）, 2007, 58(2): 425-430.
30	JiangQ C, YanX F. Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA[J]. Journal of Process Control, 2015, 32: 38-50.

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故障编号	子块1		子块2		子块3		BIC
故障编号	T²	SPE	T²	SPE	T²	SPE	T²	SPE
1	0.88	0.13	1.25	0.38	89.74	83.23	0.88	0.25
2	1.63	4.26	1.63	2	97.87	97.37	1.4	2.38
3	99.12	97.37	97.75	96.25	98.25	96.62	99.25	96.25
4	79.1	0	22.4	0	98.5	97.87	36.67	0
5	75.72	79.1	66.46	0.13	91.86	87.98	70.46	0
6	0.88	0	1.13	0.13	97.12	84.61	1	0
7	0	0	0.13	0.13	79.6	76.97	0	0
8	3.13	16.4	3.38	2.13	59.95	34.67	3.38	1.38
9	98.25	98.25	98.12	93.99	97.62	96.5	99.12	94.99
10	70.09	74.22	59.2	62.33	92.37	84.73	62.33	60.08
11	59.32	25.16	33.54	4.38	93.49	93.37	31.66	3.49
12	1.63	10.51	2.38	0.88	26.78	20.65	1.13	1
13	6.38	4.76	5.88	4.63	45.56	28.29	5.88	4.41
14	0.75	0	74.47	10.76	0.13	1.38	0	0
15	98.62	97	96.37	96.37	98.37	97	98.25	95.12
16	86.61	72.59	79.72	62.33	88.36	84.61	78.6	60.45
17	23.53	4.63	11.39	2.63	89.11	57.57	12.39	2.33
18	10.76	9.89	10.51	9.51	83.73	78.35	11.01	9.41
19	88.99	87.61	88.86	88.11	74.72	45.06	75.34	44.18
20	68.34	50.31	66.96	32.79	86.98	89.61	64.58	36.47
21	60.83	52.82	54.69	31.16	98.62	98.75	58.32	34.67
平均故障漏报率	44.50	37.38	41.72	28.26	80.42	73.10	38.65	26.12
平均故障误报率	0.52	1.32	0.8	3.35	1.38	2.67	0.37	3.07

Fault diagnosis based on PCA method with multi-block information extraction

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