时序约束NPE算法在化工过程故障检测中的应用

doi:10.11949/j.issn.0438-1157.20160543

化工学报 ›› 2016, Vol. 67 ›› Issue (12): 5131-5139.DOI: 10.11949/j.issn.0438-1157.20160543

时序约束NPE算法在化工过程故障检测中的应用

杨健, 宋冰, 谭帅, 侍洪波

华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237

收稿日期:2016-04-26 修回日期:2016-07-26 出版日期:2016-12-05 发布日期:2016-12-05
通讯作者: 侍洪波。hbshi@ecust.edu.cn
基金资助:
国家自然科学基金项目(61374140)；国家自然科学基金青年科学基金项目（61403072）。

Time constrained NPE for fault detection in chemical processes

YANG Jian, SONG Bing, TAN Shuai, SHI Hongbo

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2016-04-26 Revised:2016-07-26 Online:2016-12-05 Published:2016-12-05
Supported by:
supported by the National Natural Science Foundation of China(61374140) and the Young Scientists Fund of the National Natural Science Foundation of China(61403072).

摘要/Abstract

摘要：

针对动态过程的故障检测问题，在邻域保持嵌入算法中改进邻域挑选，提出一种新的维度约简方法：时序约束邻域保持嵌入（time constrained neighborhood preserving embedding，TCNPE）算法。与邻域保持嵌入（neighborhood preserving embedding，NPE）算法只通过欧氏距离挑选邻域不同的是，TCNPE考虑到数据之间的时序相关性，在一定长度的时间窗之内采用k-近邻方法挑选邻域，并对时间窗内近邻与非近邻构造局部约束关系。首先，利用TCNPE提取数据特征，进行线性降维，然后构造T²和SPE统计量并利用密度估计（kernel density estimation，KDE）确定其控制限。最后，通过数值例子和TE过程（Tennessee-Eastman process）仿真来说明本文方法的有效性。

关键词: 过程控制, 动态建模, 邻域保持嵌入, 线性降维, 故障检测, 实验验证

Abstract:

For fault detection in dynamic processes, a novel dimensionality reduction method was proposed on the basis of improved neighbor selection in neighborhood preserving embedding algorithm, i.e., time constrained neighborhood preserving embedding(TCNPE). Compared to neighborhood preserving embedding(NPE), which selected neighborhood only by Euclidean distance, TCNPE selected neighborhoods within certain timeframe by k-nearest neighboring method with a consideration of time series correlation between data points and constructed a localized constraining relationship between near and distant neighborhoods within this time window. First, TCNPE algorithm extracted main features of the process data and performed linear dimensionality reduction. Next, Hotelling's T² and SPE statistics were established for online process monitoring and kernel density estimation(KDE) was used to determine control limits. Case study and simulation of Tennessee-Eastman Process demonstrated efficacy of the proposed method.

Key words: process control, dynamic modeling, neighborhood preserving embedding, linear dimensionality reduction, fault detection, experimental validation

中图分类号:

TP277

杨健, 宋冰, 谭帅, 侍洪波. 时序约束NPE算法在化工过程故障检测中的应用[J]. 化工学报, 2016, 67(12): 5131-5139.

YANG Jian, SONG Bing, TAN Shuai, SHI Hongbo. Time constrained NPE for fault detection in chemical processes[J]. CIESC Journal, 2016, 67(12): 5131-5139.

参考文献 24

[1]	葛志强, 宋执环. 基于PICA的过程监控方法[J]. 化工学报, 2008, 59(7):1665-1670. GE Z Q, SONG Z H. PICA based process monitoring method[J]. Journal of Chemical Industry and Engineering(China), 2008, 59(7):1665-1670.
[2]	DENG X G, TIAN X M, CHEN S. Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis[J]. Chemometrics & Intelligent Laboratory Systems, 2013, 127(16):195-209.
[3]	CAI L F, TIAN X M. A new process monitoring method based on noisy time structure independent component analysis[J]. Chinese Journal of Chemical Engineering, 2014, 23(1):162-172.
[4]	GE Z Q, SONG Z H. Mixture Bayesian regularization method of PPCA for multimode process monitoring[J]. AIChE Journal, 2010, 56(11):2838-2849
[5]	常鹏, 王普, 高学金, 等. 基于统计量模式分析的MKPLS间歇过程监控与质量预报[J]. 仪器仪表学报, 2014, 35(6):1409-1416. CHANG P, WANG P, GAO X J, et al. Batch process monitoring and quality prediction based on statistics pattern analysis and MKPLS[J]. Chinese Journal of Scientific Instrument, 2014, 35(6):1409-1416.
[6]	冯海亮, 潘竞文, 黄鸿. 一种改进的邻域保持嵌入高光谱影像分类方法[J]. 光电工程, 2014, 41(9):45-50. FENG H L, PAN J W, HUANG H. An improved neighborhood preserving embedding method used in hyperspectral image classification[J]. Opto-Electronic Engineering, 2014, 41(9):45-50.
[7]	ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5):2323-2326.
[8]	BELKIN M, NIYOGI P. Laplacian Eigenmaps and spectral techniques for embedding and clustering[J]. Advances in Neural Information Processing Systems, 2002, 14(6):585-591.
[9]	TENENBAUM J, SILVA V D, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323.
[10]	HE X F, CAI D, YAN S C, et al. Neighborhood preserving embedding[C]//Proceedings of the Tenth IEEE International Conference on Computer Vision. 2005, 2:1208-1213.
[11]	HUANG H, HUANG Y B. Improved discriminant sparsity neighborhood preserving embedding for hyperspectral image classification[J]. Neurocomputing, 2014, 136(1):224-234.
[12]	CHEN X, ZHANG J S. A novel maximum margin neighborhood preserving embedding for face recognition[J]. Future Generation Computer Systems, 2012, 28(1):212-217.
[13]	QI M M, ZHANG Y Q, LV D D, et al. Study on orthogonal tensor sparse neighborhood preserving embedding algorithm for dimension reduction[C]//Advanced Research and Technology in Industry Applications(WARTIA), 2014 IEEE Workshop on IEEE. 2014:1392-1396.
[14]	SONG B, TAN S, SHI H B. Process monitoring via enhanced neighborhood preserving embedding[J]. Control Engineering Practice, 2016, 50:48-56.
[15]	MA Y X, SONG B, SHI H B. Fault detection via local and nonlocal embedding[J]. Chemical Engineering Research and Design, 2015, 94:538-548.
[16]	MIAO A M, GE Z Q, SONG Z H, et al. Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection[J]. Chemometrics & Intelligent Laboratory Systems, 2015, 142:184-196.
[17]	宋冰, 马玉鑫, 方永锋, 等. 基于LSNPE算法的化工过程故障检测[J].化工学报, 2014, 65(2):620-627. SONG B, MA Y X, FANG Y F, et al. Fault detection for chemical process based on LSNPE method[J]. CIESC Journal, 2014, 65(2):620-627.
[18]	KU W F, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics & Intelligent Laboratory Systems, 1995, 30:179-196.
[19]	ODIOWEI P P, CAO Y. State-space independent component analysis for nonlinear dynamic process monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 2010, 103:59-65.
[20]	MIAO A M, GE Z Q, SONG Z H, et al. Time neighborhood preserving embedding model and it's application for fault detection[J]. Industrial & Engineering Chemistry Research, 2013, 52(38):13717-13729.
[21]	HU K L, YUAN J Q. Statistical monitoring of fed-batch process using dynamic multi-way neighborhood preserving embedding[J]. Chemometrics & Intelligent Laboratory Systems, 2008, 90(2):195-203.
[22]	SONG B, MA Y X, SHI H B. Multimode process monitoring using improved dynamic neighborhood preserving embedding[J]. Chemometrics & Intelligent Laboratory Systems, 2014, 135(2014):17-30.
[23]	CHEN Q, WYNNE R J, GOULDING P, et al. The application of principal component analysis and kernel density estimation to enhance process monitoring[J]. Control Engineering Practice, 2000, 8(5):531-543.
[24]	SAUL L K, ROWEIS S T. Think globally, fit locally:unsupervised learning of low dimensional manifolds[J]. The Journal of Machine Learning Research, 2003, 4:119-155.

时序约束NPE算法在化工过程故障检测中的应用

Time constrained NPE for fault detection in chemical processes

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 24

相关文章 15

编辑推荐

Metrics

本文评价

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	邵伟明, 韩文学, 宋伟, 杨勇, 陈灿, 赵东亚. 基于分布式贝叶斯隐马尔可夫回归的动态软测量建模方法[J]. 化工学报, 2023, 74(6): 2495-2502.
[6]	邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537.
[7]	苏晓丹, 朱干宇, 李会泉, 郑光明, 孟子衡, 李防, 杨云瑞, 习本军, 崔玉. 湿法磷酸半水工艺考察与石膏结晶过程研究[J]. 化工学报, 2023, 74(4): 1805-1817.
[8]	许文烜, 江锦波, 彭新, 门日秀, 刘畅, 彭旭东. 宽速域三种典型型槽油气密封泄漏与成膜特性对比研究[J]. 化工学报, 2023, 74(4): 1660-1679.
[9]	宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638.
[10]	罗来明, 张劲, 郭志斌, 王海宁, 卢善富, 相艳. 1~5 kW高温聚合物电解质膜燃料电池堆的理论模拟与组装测试[J]. 化工学报, 2023, 74(4): 1724-1734.
[11]	张中秋, 李宏光, 石逸林. 基于人工预测调控策略的复杂化工过程多任务学习方法[J]. 化工学报, 2023, 74(3): 1195-1204.
[12]	张江淮, 赵众. 碳三加氢装置鲁棒最小协方差约束控制及应用[J]. 化工学报, 2023, 74(3): 1216-1227.
[13]	雍加望, 赵倩倩, 冯能莲. 基于非线性动态模型的质子交换膜燃料电池故障诊断[J]. 化工学报, 2022, 73(9): 3983-3993.
[14]	刘潜, 张香兰, 李志平, 李玉龙, 韩梦醒. 油酚分离过程低共熔溶剂的筛选及萃取性能研究[J]. 化工学报, 2022, 73(9): 3915-3928.
[15]	杨明辉, 刘晓月, 邓晓刚, 廖明燕, 侯春望. 基于加权概率CVDA的动态化工系统微小故障检测[J]. 化工学报, 2022, 73(9): 3963-3972.