基于双近邻标准化和PCA的多阶段过程故障检测

doi:10.11949/j.issn.0438-1157.20171629

化工学报 ›› 2018, Vol. 69 ›› Issue (7): 3159-3166.DOI: 10.11949/j.issn.0438-1157.20171629

基于双近邻标准化和PCA的多阶段过程故障检测

冯立伟^1,2, 张成^1,2, 李元², 谢彦红^1,2

1 沈阳化工大学数理系, 辽宁沈阳 110142;
2 沈阳化工大学过程故障诊断研究中心, 辽宁沈阳 110142

收稿日期:2017-12-12 修回日期:2018-02-12 出版日期:2018-07-05 发布日期:2018-07-05
通讯作者: 李元
基金资助:
国家自然科学基金项目（61490701，61673279）；辽宁省教育厅基金项目（L2015432）；辽宁省自然科学基金项目（2015020164）。

DLNS-PCA-based fault detection for multimode batch process

FENG Liwei^1,2, ZHANG Cheng^1,2, LI Yuan², XIE Yanhong^1,2

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:2017-12-12 Revised:2018-02-12 Online:2018-07-05 Published:2018-07-05
Supported by:
supported by the National Natural Science Foundation of China (61490701,61673279), the Project of Education Department in Liaoning Province (L2015432) and the Natural Science Foundation of Liaoning Province (2015020164).

摘要/Abstract

摘要：

现代工业产品的生产往往需要多个生产阶段，多阶段生产过程的故障检测成为一个重要问题。多阶段过程数据具有多中心、各工序数据结构不同等特征。针对多阶段过程数据的特征，提出了基于双近邻标准化和主元分析的故障检测方法（DLNS-PCA）。首先寻找样本的双层局部近邻集；其次使用双层局部近邻集的信息标准化样本，得到标准样本；最后在标准样本集上使用主元分析方法进行故障检测。双局部近邻标准化能够将各阶段数据的中心平移到同一点，并且调整各阶段数据的离散程度，使之近似相等，从而将多阶段过程数据融合为服从单一多元高斯分布的数据。进行了青霉素发酵过程故障检测实验，实验结果表明DLNS-PCA方法相对于PCA、KPCA、FDkNN等方法对多阶段过程故障具有更高的检测率。DLNS-PCA方法提高了多阶段过程故障检测能力。

关键词: 多阶段, 故障检测, 模型, 主元分析, 过程控制

Abstract:

Modern industrial products often require multiple production stages, and the fault detection of multi-stage production process has become an important issue. Multi-stage process data have the characteristics of multi center, different structure of each stage and so on. Aiming at the characteristics, a fault detection method based on double local neighborhood standardization and principal component analysis (DLNS-PCA) is proposed. Firstly, the double local neighborhood set of the sample is found. Secondly, the standard samples are obtained by using the information of the double local neighborhood set. Finally, the PCA method is used to detect the fault on the standard sample set. The DLNS can move the data centers of each stage to the same point, and adjust the degrees of dispersion of data at each stage to make its approximately equal, then multi-stage process data is fused to a single modal data following multivariate Gauss distribution. A fault detection of penicillin simulation process was carried out. The results showed that DLNS-PCA has higher fault detection rate than PCA, KPCA and FD-kNN methods. DLNS-PCA method improves the efficiency of multi-stage process fault detection.

Key words: multi stage process, fault detection, model, principal component analysis, process control

中图分类号:

TP277

冯立伟, 张成, 李元, 谢彦红. 基于双近邻标准化和PCA的多阶段过程故障检测[J]. 化工学报, 2018, 69(7): 3159-3166.

FENG Liwei, ZHANG Cheng, LI Yuan, XIE Yanhong. DLNS-PCA-based fault detection for multimode batch process[J]. CIESC Journal, 2018, 69(7): 3159-3166.

参考文献 30

[1]	WISE B M, GALLAGHER N B, BUTLER S W, et al. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process[J]. Journal of Chemometrics, 1999, 13(3/4):379-396.
[2]	CHERRY G A, QIN S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis[J]. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(2):159-172.
[3]	GE Z, SONG Z. Semiconductor manufacturing process monitoring based on adaptive substatistical PCA[J]. IEEE Transactions on Semiconductor Manufacturing, 2010, 23(1):99-108.
[4]	张成, 李元. 基于统计模量分析间歇过程故障检测方法研究[J]. 仪器仪表学报, 2013, 34(9):2103-2110. ZHANG C, LI Y. Study on the fault-detection method in batch process based on statistical pattern analysis[J]. Chinese Journal of Scientific Instrument, 2013, 34(9):2103-2110.
[5]	RATO T J, REIS M S. Advantage of using decorrelated residuals in dynamic principal component analysis for monitoring large-scale systems[J]. Industrial & Engineering Chemistry Research, 2013, 52(38):13685-13698.
[6]	KRESTA J V, MACGREGOR J F, MARLIN T E. Multivariate statistical monitoring of process operating performance[J]. Canadian Journal of Chemical Engineering, 1991, 69(1):35-47.
[7]	ZHAO S J, ZHANG J, XU Y M. Performance monitoring of processes with multiple operating modes through multiple PLS models[J]. Journal of Process Control, 2006, 16(7):763-772.
[8]	LI G, QIN S J, ZHOU D. Geometric properties of partial least squares for process monitoring[J]. Automatica, 2010, 46(1):204-210.
[9]	LEE J M, YOO C K, SANG W C, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1):223-234.
[10]	ROSIPAL R, TREJO L J. Kernel partial least squares regression in reproducing kernel Hilbert space[J]. Journal of Machine Learning Research, 2001, 2(3):97-123.
[11]	ZHANG Y, LI S, HU Z. Improved multi-scale kernel principal component analysis and its application for fault detection[J]. Chemical Engineering Research & Design, 2012, 90(9):1271-1280.
[12]	ZHANG Y, HU Z. Multivariate process monitoring and analysis based on multi-scale KPLS[J]. Chemical Engineering Research & Design, 2011, 89(12):2667-2678.
[13]	SHI J Z, JIE Z, YONG M X. Monitoring of processes with multiple operating modes through multiple principal component analysis models[J]. Industrial & Engineering Chemistry Research, 2004, 43(22):7025-7035.
[14]	NG Y S, SRINIVASAN R. An adjoined multi-model approach for monitoring batch and transient operations[J]. Computers & Chemical Engineering, 2009, 33(4):887-902.
[15]	CHANG K Y, VILLEZ K, LEE I B, et al. Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor[J]. Biotechnology & Bioengineering, 2007, 96(4):687-701.
[16]	GE Z, SONG Z. Multimode process monitoring based on Bayesian method[J]. Journal of Chemometrics, 2010, 23(12):636-650.
[17]	NATARAJAN S, SRINIVASAN R. Multi-model based process condition monitoring of offshore oil and gas production process[J]. Chemical Engineering Research & Design, 2010, 88(5):572-591.
[18]	YU J, QIN S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J]. AIChE Journal, 2008, 54(7):1811-1829.
[19]	HE Q P, WANG J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J].IEEE Transactions on Semiconductor Manufacturing, 2007, 20(4):345-354.
[20]	ZHOU Z, WEN C, YANG C. Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2015, 28(1):70-79.
[21]	HE Q P, WANG J. Principal component based k-nearest-neighbor rule for semiconductor process fault detection[C]//Proceedings of the American Control Conference. Washington, USA, 2008:1606-1611.
[22]	VERDIER G, FERREIRA A. Fault detection with an adaptive distance for the k-nearest neighbors rule[C]//International Conference on Computers & Industrial Engineering. Troyes, France:IEEE, 2009:1273-1278.
[23]	冯立伟, 张成, 李元, 等. 基于局部马氏距离的加权k近邻故障检测方法[J]. 通化师范学院学报, 2017, 38(4):57-63. FENG L W, ZHANG C, LI Y, et al. Local Mahalanobis distance based weighted k nearest neighbor rule for fault detection[J]. Journal of Tonghua Normal University, 2017, 38(4):57-63.
[24]	LANE S, MARYIN E B, KOOIJMANS R, et al. Performance monitoring of a multi-product semi-batch process[J]. Journal of Process Control, 2001, 11(1):1-11.
[25]	WANG G, LIU J, ZHANG Y, et al. A novel multi-mode data processing method and its application in industrial process monitoring[J]. Journal of Chemometrics, 2015, 29(2):126-138.
[26]	马贺贺, 胡益, 侍洪波. 基于距离空间统计量分析的多模态过程无监督故障检测[J]. 化工学报, 2012, 63(3):873-880. MA H H, HU Y, SHI H B. Unsupervised fault detection for multimode processes using distance space statistics analysis[J]. CIESC Journal, 2012, 63(3):873-880.
[27]	LEE J M, YOO C K, LEE I B. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers & Chemical Engineering, 2004, 28(9):1837-1847.
[28]	TAN S, WANG F, PENG J, et al. Multimode process monitoring based on mode identification[J]. Industrial & Engineering Chemistry Research, 2011, 51(1):374-388.
[29]	刘毅, 王海清. Pensim仿真平台在青霉素发酵过程的应用研究[J]. 系统仿真学报, 2006, 18(12):3524-3527. LIU Y, WANG H Q. Pensim simulator and its application in penicillin fermentation process[J]. Journal of System Simulation, 2006, 18(12):3524-3527.
[30]	WANG G, LIU J, LI Y, et al. Fault detection based on diffusion maps and k nearest neighbor diffusion distance of feature space[J]. Journal of Chemical Engineering of Japan, 2015, 48(9):756-765.

基于双近邻标准化和PCA的多阶段过程故障检测

DLNS-PCA-based fault detection for multimode batch process

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 30

相关文章 15

编辑推荐

Metrics

本文评价

[1]	宋嘉豪, 王文. 斯特林发动机与高温热管耦合运行特性研究[J]. 化工学报, 2023, 74(S1): 287-294.
[2]	连梦雅, 谈莹莹, 王林, 陈枫, 曹艺飞. 地下水预热新风一体化热泵空调系统制热性能研究[J]. 化工学报, 2023, 74(S1): 311-319.
[3]	金正浩, 封立杰, 李舒宏. 氨水溶液交叉型再吸收式热泵的能量及分析[J]. 化工学报, 2023, 74(S1): 53-63.
[4]	康飞, 吕伟光, 巨锋, 孙峙. 废锂离子电池放电路径与评价研究[J]. 化工学报, 2023, 74(9): 3903-3911.
[5]	李科, 文键, 忻碧平. 耦合蒸气冷却屏的真空多层绝热结构对液氢储罐自增压过程的影响机制研究[J]. 化工学报, 2023, 74(9): 3786-3796.
[6]	王浩, 王振雷. 基于自适应谱方法的裂解炉烧焦模型化简策略[J]. 化工学报, 2023, 74(9): 3855-3864.
[7]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[8]	张逸豪, 王振雷. 基于最大信息系数的分组支持向量数据描述故障检测[J]. 化工学报, 2023, 74(9): 3865-3878.
[9]	李锦潼, 邱顺, 孙文寿. 煤浆法烟气脱硫中草酸和紫外线强化煤砷浸出过程[J]. 化工学报, 2023, 74(8): 3522-3532.
[10]	于旭东, 李琪, 陈念粗, 杜理, 任思颖, 曾英. 三元体系KCl + CaCl₂ + H₂O 298.2、323.2及348.2 K相平衡研究及计算[J]. 化工学报, 2023, 74(8): 3256-3265.
[11]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[12]	闫琳琦, 王振雷. 基于STA-BiLSTM-LightGBM组合模型的多步预测软测量建模[J]. 化工学报, 2023, 74(8): 3407-3418.
[13]	郭雨莹, 敬加强, 黄婉妮, 张平, 孙杰, 朱宇, 冯君炫, 陆洪江. 稠油管道水润滑减阻及压降预测模型修正[J]. 化工学报, 2023, 74(7): 2898-2907.
[14]	刘春雨, 周桓宇, 马跃, 岳长涛. CaO调质含油污泥干燥特性及数学模型[J]. 化工学报, 2023, 74(7): 3018-3027.
[15]	王光, 单发顺, 钱禹丞, 焦建芳. 基于集成学习传递熵的化工过程微小故障检测方法[J]. 化工学报, 2023, 74(7): 2967-2978.