基于收敛交叉映射的化工过程扰动因果分析方法

doi:10.11949/j.issn.0438-1157.20161318

化工学报 ›› 2016, Vol. 67 ›› Issue (12): 5082-5088.DOI: 10.11949/j.issn.0438-1157.20161318

基于收敛交叉映射的化工过程扰动因果分析方法

程非凡, 赵劲松

清华大学化学工程系, 北京 100084

收稿日期:2016-09-21 修回日期:2016-09-26 出版日期:2016-12-05 发布日期:2016-12-05
通讯作者: 赵劲松。jinsongzhao@tsinghua.edu.cn
基金资助:
国家自然科学基金项目（61433001）。

Convergent cross mapping method in analysis of disturbances in chemical processes

CHENG Feifan, ZHAO Jinsong

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

Received:2016-09-21 Revised:2016-09-26 Online:2016-12-05 Published:2016-12-05
Supported by:
supported by the National Natural Science Foundation of China(61433001).

摘要/Abstract

摘要：

当化工装置出现异常情况，操作工人往往无法及时准确地定位故障发生的原因。基于数据的方法能够通过化工过程数据，分析异常工况中的扰动传播路径，确定异常工况出现的根本原因。针对化工动态系统，提出了具有时间特性的收敛交叉映射方法（CCM），和基于赤池信息准则的维度选择方法。为了验证提出算法的有效性，在简单的生态系统，因果检测基准系统和全混釜反应器（CSTR）中进行验证，并与原有的收敛交叉映射算法进行对比，体现出具有时间特性的收敛交叉映射算法的优越性。

关键词: 扰动分析, 因果分析, 收敛交叉映射, 时间特性, 安全, 系统工程, 模拟

Abstract:

When there is a fault in the chemical plant, the operators may not be able to find the root cause of the fault. Data-driven method can be a great help to reduce the uncertainty of root cause, even to locate the root cause. By analysis of chemical process data, the disturbance propagation way can be detected, which can help to locate the root cause. In this article, convergent cross mapping(CCM) with the characteristic of time is proposed to detect the causality in dynamic chemical process. Furthermore, the usage of Akaike information criterion is proposed to determine the most proper embedding dimension. To prove the effectiveness of the method, the new method is applied to the ecosystem examples, causality testing benchmark model and CSTR model. Comparing the result calculated by the original CCM, the effectiveness of the new method is found.

Key words: disturbance analysis, causality analysis, convergent cross mapping, characteristic of time, safety, systems engineering, simulation

中图分类号:

TQ015.9

程非凡, 赵劲松. 基于收敛交叉映射的化工过程扰动因果分析方法[J]. 化工学报, 2016, 67(12): 5082-5088.

CHENG Feifan, ZHAO Jinsong. Convergent cross mapping method in analysis of disturbances in chemical processes[J]. CIESC Journal, 2016, 67(12): 5082-5088.

参考文献 20

[1]	SHU Y, MING L, CHENG F, et al. Abnormal situation management:challenges and opportunities in the big data era[J]. Computers & Chemical Engineering, 2016, 91:104-113.
[2]	KUHNERT C. Data-driven Methods for Fault Localization in Process Technology[M]. KIT Scientific Publishing, 2013:12-17.
[3]	IERMANN R. Fault-diagnosis Systems:An Introduction from Fault Detection to Fault Tolerance[M]. Springer Science & Business Media, 2006:34-40.
[4]	KUHNERT C, BEYERER J. Data-driven methods for the detection of causal structures in process technology[J]. Machines, 2014, 2(4):255-274.
[5]	GRANGER C W. Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica:Journal of the Econometric Society, 1969, 37(3):424-438.
[6]	MCCRACKEN J M, WEIGEL R S. Convergent cross-mapping and pairwise asymmetric inference[J]. Physical Review E, 2014, 90(6):062903.
[7]	BILLINGS S A. Nonlinear System Identification:NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains[M]. John Wiley & Sons, 2013:78-85.
[8]	SCHREIBER T. Measuring information transfer[J]. Physical Review Letters, 2000, 85(2):461.
[9]	SHU Y D, ZHAO J S. Data driven causal inference based on a modified transfer entropy[J]. Computers & Chemical Engineering, 2013, 57(10):173-180
[10]	BAUER M, COX J W, CAVENESS M H, et al. Finding the direction of disturbance propagation in a chemical process using transfer entropy[J]. Control Systems Technology, IEEE Transactions on, 2007, 15(1):12-21.
[11]	SUGIHARA G, MAY R, YE H, et al. Detecting causality in complex ecosystems[J]. Science, 2012, 338(6106):496-500.
[12]	MA H, AIHARA K, CHEN L. Detecting causality from nonlinear dynamics with short-term time series[J]. Scientific Reports, 2014, 4:7464.
[13]	CLARK A T, YE H, ISBELL F, et al. Spatial convergent cross mapping to detect causal relationships from short time series[J]. Ecology, 2015, 96(5):1174-1181.
[14]	WEI W W S. Time Series Analysis[M]. Addison-Wesley, 1994:77-82.
[15]	TAKENS F. Detecting strange attractors in turbulence[M]//Dynamical Systems and Turbulence, Warwick 1980. Springer Berlin Heidelberg, 1981:366-381.
[16]	AKAIKE H. Akaike's information criterion[M]//International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011:25.
[17]	WISMÜLLER A, ABIDIN A Z, D'SOUZA A M, et al. Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis(MCA):convergent cross-mapping and non-metric clustering[C]//SPIE Medical Imaging. International Society for Optics and Photonics, 2015:94170M-94170M-9.
[18]	YAMAOKA K, NAKAGAWA T, UNO T. Application of Akaike's information criterion(AIC) in the evaluation of linear pharmacokinetic equations[J]. Journal of pharmacokinetics and biopharmaceutics, 1978, 6(2):165-175.
[19]	BROOKS D G. Akaike information criterion statistics[J]. Technometrics, 1989, 31(2):270-271.
[20]	BOZDOGAN H. Model selection and Akaike's information criterion(AIC):the general theory and its analytical extensions[J]. Psychometrika, 1987, 52(3):345-370.

基于收敛交叉映射的化工过程扰动因果分析方法

Convergent cross mapping method in analysis of disturbances in chemical processes

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