Fault monitoring based on mutual information feature engineering modeling in chemical process

doi:10.1016/j.cjche.2018.11.008

Abstract

Abstract: A large amount of information is frequently encountered when characterizing the sample model in chemical process. A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper. From the whole process point of view, the method makes use of the characteristic of mutual information to select the optimal variable subset. It extracts the correlation among variables in the whitening process without limiting to only linear correlations. Further, PCA (Principal Component Analysis) dimension reduction is used to extract feature subset before fault diagnosis. The application results of the TE (Tennessee Eastman) simulation process show that the dynamic modeling process of MIFE (Mutual Information Feature Engineering) can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

Key words: Big data, Fault diagnosis, Mutual information, TE process, Process modeling

摘要： A large amount of information is frequently encountered when characterizing the sample model in chemical process. A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper. From the whole process point of view, the method makes use of the characteristic of mutual information to select the optimal variable subset. It extracts the correlation among variables in the whitening process without limiting to only linear correlations. Further, PCA (Principal Component Analysis) dimension reduction is used to extract feature subset before fault diagnosis. The application results of the TE (Tennessee Eastman) simulation process show that the dynamic modeling process of MIFE (Mutual Information Feature Engineering) can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

关键词: Big data, Fault diagnosis, Mutual information, TE process, Process modeling

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. Chinese Journal of Chemical Engineering, 2019, 27(10): 2491-2497.

Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. 中国化学工程学报, 2019, 27(10): 2491-2497.

References

[1] Z. Li, X. Ma, H. Xin, Feature engineering of machine-learning chemisorption models for catalyst design, Catal. Today 280(2017) 232-238.
[2] M. Spellings, S.C. Glotzer, Machine learning for crystal identification and discovery, AIChE J. 64(6) (2018) 2198-2206.
[3] J. J Cai, J. Luo, S. Wang, et al., Feature selection in machine learning:A new perspective, Neurocomputing 300(2018) 70-79.
[4] Z. Geng, K. Wu, Y. Han, Research and application of FLANN neural network based on analytic hierarchy process, CIESC J. 67(3) (2016) 805-811(in Chinese).
[5] J. Yu, M.M. Rashid, A novel dynamic Bayesian network-based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis, AIChE J. 59(7) (2013) 2348-2365.
[6] I. Guyon, A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3(Mar) (2003) 1157-1182.
[7] R. Cai, Z. Hao, X. Yang, et al., An efficient gene selection algorithm based on mutual information, Neurocomputing 72(4-6) (2009) 991-999.
[8] F. Amiri, M.M.R. Yousefi, C. Lucas, et al., Mutual information-based feature selection for intrusion detection systems, J. Netw. Comput. Appl. 34(4) (2011) 1184-1199.
[9] H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell. 27(8) (2005) 1226-1238.
[10] P.A. Estévez, M. Tesmer, C.A. Perez, et al., Normalized mutual information feature selection, IEEE Trans. Neural Netw. 20(2) (2009) 189-201.
[11] F. Fleuret, Fast binary feature selection with conditional mutual information, J. Mach. Learn. Res. 5(Nov) (2004) 1531-1555.
[12] J.C.B. Melo, G.D.C. Cavalcanti, K.S. Guimaraes, PCA feature extraction for protein structure prediction, International Joint Conference on Neural Networks, vol. 4, IEEE 2003, pp. 2952-2957.
[13] Q. Jiang, X. Yan, W. Zhao, Fault detection and diagnosis in chemical processes using sensitive principal component analysis, Ind. Eng. Chem. Res. 52(4) (2013) 1635-1644.
[14] Q. Liang, H. Han, X.Y. Cui, et al., Fault diagnosis for refrigeration system based on PCA-PNN, CIESC J 67(3) (2016) 1023-1031(in Chinese).
[15] Q. Zhang, Y. Wu, J. Xu, Application of principal component analysis and genetic neural network in fault diagnosis of refrigeration system, Comput Meas Control 24(9) (2016) 23-27.
[16] L.J. Cao, W.K. Chong, Feature Extraction in Support Vector Machine:A Comparison of PCA, XPCA and ICA, International Conference on Neural Information Processing, IEEE 2002, pp. 1001-1005.
[17] Y. Zhang, S. Li, Y. Teng, Dynamic processes monitoring using recursive kernel principal component analysis, Chem. Eng. Sci. 72(16) (2012) 78-86.
[18] A.M. Jade, B. Srikanth, V.K. Jayaraman, et al., Feature extraction and denoising using kernel PCA, Chem. Eng. Sci. 58(19) (2003) 4441-4448.
[19] R. Rosipal, M. Girolami, L.J. Trejo, et al., Kernel PCA for feature extraction and de-noising in nonlinear regression, Neural Comput. Applic. 10(3) (2001) 231-243.
[20] Y. Xu, D. Zhang, F. Song, et al., A method for speeding up feature extraction based on KPCA, Neurocomputing 70(4-6) (2007) 1056-1061.
[21] L.Y. Yang, A. Qin, Face Recognition Algorithms Based on Uncorrelated Multilinear PCA, Radio Communications Technology 42(1) (2016) 73-75.
[22] T. Rato, M. Reis, E. Schmitt, et al., A systematic comparison of PCA-based statistical process monitoring methods for high-dimensional, time-dependent processes, AIChE J. 62(5) (2016) 1478-1493.
[23] R. Dunia, T.F. Edgar, M. Nixon, Process monitoring using principal components in parallel coordinates, AICHE J. 59(2) (2013) 445-456.
[24] L. Liu, C. Li, Y. Lei, et al., Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA, Arab. J. Geosci. 10(14) (2017) 307.
[25] A. Bouzalmat, J. Kharroubi, A. Zarghili, Comparative study of PCA, ICA, LDA using SVM classifier, J. Emerg. Technol. Web Intell. 6(1) (2014) 64-68.
[26] S.J. Qin, Process data analytics in the era of big data, AIChE J. 60(9) (2014) 3092-3100.
[27] F. Dikbaş, A new two-dimensional rank correlation coefficient, Water Resour. Manag. 32(5) (2018) 1-15.
[28] H. Zhou, Z. Deng, Y. Xia, et al., A new sampling method in particle filter based on Pearson correlation coefficient, Neurocomputing 216(2016) 208-215.
[29] A.A. Aburomman, M.B.I. Reaz, Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection[C], Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016 IEEE, IEEE 2016, pp. 636-640.
[30] R.J. Martis, U.R. Acharya, L.C. Min, ECG beat classification using PCA, LDA, ICA and discrete wavelet transform, Biomed. Signal Process. Control 8(5) (2013) 437-448.
[31] J. Huang, X. Yan, Related and independent variable fault detection based on KPCA and SVDD, J. Process Control 39(2016) 88-99.
[32] M.M. Rashid, Y. Jie, Nonlinear and non-Gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach, Indonesia 51(33) (2012) 10910-10920.
[33] Y. He, L. Zhou, Z. Ge, et al., Dynamic mutual information similarity based transient process identification and fault detection, Can. J. Chem. Eng. 96(7) (2017) 1541-1558.
[34] C.D. Tong, X.H. Shi, Mutual information based PCA algorithm with application in process monitoring, CIESC J. 66(10) (2015) 4101-4106(in Chinese).
[35] C.D. Tong, T. Lan, X.H. Shi, Fault detection by decentralized dynamic PCA algorithm on mutual information, CIESC J. 67(10) (2016) 4317-4323(in Chinese).