Industrial Process Fault Detection Based on Incremental Isometric Mapping and Double Local Density Method

doi:10.16183/j.cnki.jsjtu.2022.423

Abstract

Abstract:

To address the nonlinearity and dynamics of industrial processes, an incremental isometric mapping (IISOMAP) in combination with double local density (DLD) is proposed as a fault detection method (IISOMAP-DLD) based on stream shape learning. First, IISOMAP is used to map the raw data into a low-dimensional manifold feature subspace and a residual subspace. Then, the double local density method is introduced in the two subspaces respectively to construct statistics to monitor the process. Finally, the IISOMAP-DLD method is applied to the Tennessee-Eastman (TE) process, and the experimental results show that IISOMAP-DLD has a higher fault detection rate than the other methods. IISOMAP preserves the intrinsic characteristics of the data and solves the nonlinear problems of the process, while the double local density method can eliminate the dynamic of the process.

Key words: manifold learning, isometric mapping (ISOMAP), local density, fault detection, dynamic

CLC Number:

TP277

FENG Liwei, SUN Liwen, GU Huan, LI Yuan. Industrial Process Fault Detection Based on Incremental Isometric Mapping and Double Local Density Method[J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 525-533.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.1

Fault detection rate of each method in TE process

组别	KPCA		DKPCA		WKNN D/%	KSFA		IISOMAP-DLD
组别	T²/%	SPE/%	T²/%	SPE/%	WKNN D/%	T²/%	$T e 2$ /%	τ/%	τ_e/%
f1	98.86	99.14	98.54	99.43	98.86	99.00	98.57	99.14	99.54
f2	94.01	94.86	92.44	94.15	96.57	97.00	94.86	99.00	99.54
f3	0.43	15.55	0.57	1.41	20.86	25.00	52.29	69.00	19.86
f4	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f5	0	30.10	0	0.70	37.71	42.00	57.71	73.71	14.71
f6	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f7	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f8	86.88	89.87	85.84	89.84	88.43	88.71	88.71	90.00	86.00
f9	3.42	17.26	1.57	3.34	22.00	17.71	47.00	62.14	15.57
f10	85.16	89.30	83.12	84.55	88.14	90.86	87.29	90.43	83.00
f11	96.86	98.43	94.13	98.00	97.86	98.14	92.00	98.71	95.29
f12	36.09	80.88	24.75	46.35	70.86	60.57	73.29	79.71	42.57
f13	94.01	95.01	94.28	94.99	94	94.43	93.71	95.57	93.71
f14	98.57	98.72	98.71	98.86	98.71	98.71	91.43	99.00	98.00
f15	0	3.57	0	0	13.29	7.29	58.86	67.14	7.57
f16	1.28	1.28	1.29	1.00	0.86	0.71	0.71	29.86	16.43
f17	83.17	84.88	82.98	84.55	84.71	84.57	82.43	86.86	83.00
f18	49.07	63.62	49.50	58.08	56.86	55.57	59.86	68.86	62.00
f19	91.87	95.72	89.56	94.42	94.57	96.00	94.43	96.71	95.14
f20	84.88	85.16	84.98	85.41	84.29	84.43	81.86	85.14	83.71
f21	1.43	1.28	1.43	1.00	0.71	0.71	1.41	29.29	15.86

Tab.1

Fig.7

References 30

[1]	RAMAKRISHNA K, MRINMAYEE B, MUDDU M. Kantorovich distance based fault detection scheme for non-linear processes[J]. IEEE Access, 2022, 10: 1051-1067 doi: 10.1109/ACCESS.2021.3138696 URL
[2]	陈法法, 杨晓青, 陈保家, 等. 基于正交邻域保持嵌入与多核相关向量机的滚动轴承早期故障诊断[J]. 计算机集成制造系统, 2018, 24(8): 1946-1954.
	CHEN Fafa, YANG Xiaoqing, CHEN Baojia, et al. Early fault diagnosis of rolling bearing based on orthogonal neighbourhood preserving embedding and multi-kernel relevance vector machine[J]. Computer Integrated Manufacturing Systems, 2018, 24(8): 1946-1954.
[3]	GUO J Y, ZHONG L L, LI Y. Fault detection of multi-mode batch process based on statistics difference LPP[J]. Application Research of Computers, 2019, 36(1): 123-126.
[4]	CAO L J, CHUA K S, CHONG W K, et al. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine[J]. Neurocomputing, 2003, 55(1/2): 321-336. doi: 10.1016/S0925-2312(03)00433-8 URL
[5]	赵小强, 姚红娟. 改进邻域保持嵌入—独立元分析的间歇过程故障检测算法[J]. 计算机集成制造系统, 2021, 27(4): 1062-1071. doi: 10.13196/j.cims.2021.04.010
	ZHAO Xiaoqiang, YAO Hongjuan. Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis[J]. Computer Integrated Manufacturing Systems, 2021, 27(4): 1062-1071.
[6]	KONG X Y, LI Q, AN Q S, et al. Quality-related fault detection based on partial least squares score reconstruction[J]. Control Theory and Applications, 2020, 37(11): 2321-2332.
[7]	董顺, 李益国, 孙栓柱, 等. 基于状态空间主成分分析网络的故障检测方法[J]. 化工学报, 2018, 69(8): 237-245.
	DONG Shun, LI Yiguo, SUN Shuanzhu, et al. Fault detection method based on state space principal component analysis network[J]. CIESC Journal, 2018, 69(8): 237-245.
[8]	LEE J, YOO C, LEE I. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers and Chemical Engineering, 2004, 28(9): 1837-1847. doi: 10.1016/j.compchemeng.2004.02.036 URL
[9]	邓佳伟, 邓晓刚, 曹玉苹, 等. 基于加权统计局部核主元分析的非线性化工过程微小故障诊断方法[J]. 化工学报, 2019, 70(7): 2594-2605. doi: 10.11949/j.issn.0438-1157.20181307
	DENG Jiawei, DENG Xiaogang, CAO Yuping, et al. Incipient fault diagnosis method of nonlinear chemical process based on weighted statistical local KPCA[J]. CIESC Journal, 2019, 70(7): 2594-2605.
[10]	KU W, ROBERT H, CHRISTOS G. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30: 175-196.
[11]	张佳鑫, 罗文嘉, 戴一阳. 基于CTA-DKPCA的化工过程故障诊断[J]. 控制工程, 2021, 28(5): 844-850.
	ZHANG Jiaxin, LUO Wenjia, DAI Yiyang. Chemical process fault diagnosis based on CTA-DKPCA[J]. Control Engineering, 2021, 28(5): 844-850.
[12]	冯立伟, 张成, 李元. 基于PC-WKNN的多工况间歇过程故障检测方法研究[J]. 计算机应用研究, 2018, 35(4): 1130-1134.
	FENG Liwei, ZHANG Cheng, LI Yuan. Research on fault detection method of multi-mode intermittent process based on PC-WKNN[J]. Application Research of Computers, 2018, 35(4): 1130-1134.
[13]	ZHANG C, GAO X W, LI Y, et al. Fault detection strategy based on weighted distance of k nearest neighbors for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2018, 32(1): 75-81. doi: 10.1109/TSM.2018.2857818 URL
[14]	WANG Q F, WANG S, WEI B K, et al. Weighted k-NN classification method of bearings fault diagnosis with multi-dimensional sensitive features[J]. IEEE Access, 2021, 9: 45428-45440. doi: 10.1109/ACCESS.2021.3066489 URL
[15]	SHANG C, HUANG B, YANG F, et al. Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling[J]. AIChE Journal, 2015, 61(12): 4126-4139. doi: 10.1002/aic.v61.12 URL
[16]	BOHMER W, GRUNEWALDER S, NICKISCH H, et al. Regularized sparse kernel slow feature analysis[C]// European Conference on Machine Learning and Knowledge Discovery in Databases-Volume Part I. Heidelberg, Germany: Springer, 2011: 235-248.
[17]	卢依容. 基于核慢特征分析算法的故障检测与诊断[D]. 上海: 上海交通大学, 2015.
	LU Yirong. Fault detection and diagnosis based on kernel slow feature analysis algorithm[D]. Shanghai: Shanghai Jiao Tong University, 2015.
[18]	ZHANG H, TIAN X, CAI L. Nonlinear process fault diagnosis using kernel slow feature discriminant analysis[J]. IFAC Papersonline, 2015, 48(21): 607-612. doi: 10.1016/j.ifacol.2015.09.593 URL
[19]	黄健, 杨旭, 陈先中. 基于故障相关慢特征分析的过程监测方法[J]. 高校化学工程学报, 2020, 34(5): 1290-1296.
	HUANG Jian, YANG Xu, CHEN Xianzhong. Process monitoring method based on fault related slow characteristic analysis[J]. Journal of Chemical Engineering of Chinese Universities, 2020, 34(5): 1290-1296.
[20]	TENENBAUM J, SILVA V, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323. doi: 10.1126/science.290.5500.2319 pmid: 11125149
[21]	ZHANG Y, LI B W, WANG Z B, et al. Fault diagnosis of rotating machine by isometric feature mapping[J]. Journal of Mechanical Science & Technology, 2013, 27(11): 3215-3221.
[22]	张妮, 田学民, 蔡连芳. 基于RISOMAP的非线性过程故障检测方法[J]. 化工学报, 2013, 64(6): 2125-2130. doi: 10.3969/j.issn.0438-1157.2013.06.031
	ZHANG Ni, TIAN Xuemin, CAI Lianfang. Nonlinear process fault detection method based on RISOMAP[J]. CIESC Journal, 2013, 64(6): 2125-2130.
[23]	COX T, COX M. Multidimensional scaling[J]. Journal of the Royal Statistical Society, 2001, 46(2): 1050-1057.
[24]	NASIR S, HAEWOON N, MIAN I U H, et al. A survey on multidimensional scaling[J]. ACM Computing Surveys (CSUR), 2018, 51(3): 471-496.
[25]	LAW M, JAIN A. Incremental nonlinear dimensionality reduction by manifold learning[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2006, 28(3): 377-391.
[26]	SHANBHAG D N, RAO C R. Handbook of statistics stochastic processes: Theory and methods[M]. Amsterdam,Netherlands: Elsevier, 2001.
[27]	DENG X, TIAN X, CHEN S, et al. Nonlinear process fault diagnosis based on serial principal component analysis[J]. IEEE Transactions on Neural Networks & Learning Systems, 2018, 29(3): 560-572.
[28]	郭金玉, 李文涛, 李元. 在线压缩核主元分析的自适应过程监控[J]. 上海交通大学学报, 2022, 56(10): 1397-1408. doi: 10.16183/j.cnki.jsjtu.2021.084
	GUO Jinyu, LI Wentao, LI Yuan. Adaptive process monitoring based on on-line compressed kernel principal component analysis[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1397-1408.
[29]	ZHONG X, HAN M, QIU T, et al. Fault diagnosis of complex process using sparse kernel local fisher discriminant analysis[J]. IEEE Transactions on Neural Networks & Learning Systems, 2020, 31(5): 1581-1591.
[30]	BATHELT A, RICKER N, JELALI M. Revision of the Tennessee Eastman process model[J]. IFAC PapersOnLine, 2015, 48(8): 309-314.