Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature  selection

Abstract

Abstract: This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM’s leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM). The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.

Key words: fuzzy support vector machine, parameter tuning, fault diagnosis, key variable identification

摘要： This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM’s leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM). The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.

关键词: fuzzy support vector machine;parameter tuning;fault diagnosis;key variable identification

MAOYong,XIAZheng,YINZheng,SUNYouxian,WANZheng. Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature
selection[J]. .

毛勇, 夏铮, 尹征, 孙优贤, 万征. 基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断[J]. CIESC Journal.

References

1    Frank, P.M., Ding, S.X., Marcu, T., “Model-based fault diagnosis in technical processes”, Trans. Inst. Meas. and Control, 22(1), 57—101(2002).
2    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.W., “A review of process fault detection and di-agnosis Part I: Quantitative model-based methods”, Comput. Chem. Eng., 27, 293—311(2003).
3    Chen, J. Patton, R.J., Robust Model-based Fault Diagno-sis for Dynamic Systems, Kluwer Academic Publishers, Massachusetts (1999).
4    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.W., “A review of process fault detection and di-agnosis Part III: Process history based methods”, Comput. Chem. Eng., 27, 327—346(2003).
5    Chiang, L.H., Kotanchek, M.E., Kordon, A.K., “Fault diagnosis based on Fisher discriminant analysis and support vector machines”, Comput. Chem. Eng., 28, 1389—1401(2004).
6    Chiang, L.H., Pell, R.J., “Genetic algorithms combined with discriminant analysis for key variable identifica-tion”. J. Process Control, 14, 143—155(2004).
7    Hastie, T., Tibshirani, R., Friedman, J., “The Elements of Statistical Learning, Data Mining, Inference, and Predic-tion”, Springer-Verlag, New York(2002).
8    Jakubek, S.M., Strasser T.I., “Artificial neural networks for fault detection in large-scale data acquisition sys-tems”. Eng. Appl. Artif. Intell., 17, 233—248(2004).
9    Scholkopf, B., Smola, A.J., Learning with Kernels, Sup-port Vector Machines, Regularization, Optimization, and Beyond, MIT Press, New York (2002).
10    Vapnik, V.N., The Nature of Statistical Learning Theory, Springer, New York (1999).
11    Mao, Y., Pi, D.Y., Liu, Y.M., Sun, Y.X., “Accelerated re-cursive feature elimination by support vector machine for key variable identification”. Chin. J. Chem. Eng., 14,  65—72(2005).
12    Inoue, T., Abe, S., “Fuzzy support vecto machines for pat-tern classification”, In: Proceeding of International Joint Conference on Neural Networks, Washington DC (2001).
13    Abe, S., Inoue, T., “Fuzzy support vector machines for multiclass problems”, In: European Symposium on Arti-ficial Neural Networks, Bruges (2002).
14    Mao, Y., Zhou, X.B., Pi, D.Y., Sun, Y.X., Wong, S.T.C., “Multi-class cancer classification by using fuzzy support vector machine and binary decision tree with gene selec-tion”, J. Biomed. Biotechnol., 2, 160—171(2005).
15    Lyman, P.R., Georgakis, C., “Plant-wide control of the Tennessee Eastman problem”, Comput. Chem. Eng., 19, 321—331(1995).
16    Downs, J.J., Vogel, E.F., “A plant-wide industrial-process control problem”, Comput. Chem. Eng., 17, 245—255(1993).
17    Guyon, I., Weston, J., Barnhill, S., Vapnik, V., “Gene selection for cancer classification using support vector machines”, Machine Learning, 46, 389—422(2002).
18    Mao, Y., Zhou, X.B., Pi, D.Y., Sun, Y.X., Wong, S.T.C., “Parameters selection in gene selection using Gaussian Kernel support vector machines by genetic algorithm”. J. Zhejiang University Science, 6B(10), 961—973(2005).
19    Mao, Y., Zhou, X.B., Yin, Z., Pi, D.Y., Sun, Y.X., Wong, S.T.C., “Gene Selection Using Gaussian Kernel Support Vector Machines based recursive feature elimination with adaptive kernel width strategy”, Lecture Notes in Com-puter Science, 4062, 799—806(2006).
20    Dennis, J.E., Schnabel, R.B., Numerical methods for un-constrained optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs., NJ (1983).

[1]	Qian MING, Yi GAO, Jian HU, Shengjie LI, Jinjiang WANG. Virtual sensing method for leakage fault of heat exchanger [J]. CIESC Journal, 2023, 74(4): 1836-1846.
[2]	Xuejin GAO, Kun CHENG, Huayun HAN, Huihui Gao, Yongsheng QI. Fault diagnosis of chillers using central loss conditional generative adversarial network [J]. CIESC Journal, 2022, 73(9): 3950-3962.
[3]	Zhe SUN, Huaqiang JIN, Kang LI, Jiangping GU, Yuejin HUANG, Xi SHEN. Fault diagnosis method of refrigeration and air-conditioning system based on digitized knowledge representation [J]. CIESC Journal, 2022, 73(7): 3131-3144.
[4]	Cheng ZHANG, Lizhi PAN, Yuan LI. Fault detection and diagnosis method based on weighted statistical feature KICA [J]. CIESC Journal, 2022, 73(2): 827-837.
[5]	ZHANG Chi, LI Hao, HU Haitao, ZHU Chong, ZHANG Yuying, NAN Guopeng, SHU Yue. Aircraft bearing fault diagnosis based on automatic feature engineering [J]. CIESC Journal, 2021, 72(S1): 430-436.
[6]	Xin YANG, Zherui MA, Henan SHEN, Hongwei CHEN. Fault diagnosis of airflow jamming fault in double circulating fluidized bed based on multi-scale feature energy and KELM [J]. CIESC Journal, 2019, 70(7): 2616-2625.
[7]	Jiawei DENG, Xiaogang DENG, Yuping CAO, Xiaoling ZHANG. Incipient fault diagnosis method of nonlinear chemical process based on weighted statistical local KPCA [J]. CIESC Journal, 2019, 70(7): 2594-2605.
[8]	Kaixiang PENG, Chuanfang ZHANG, Liang MA, Jie DONG, Ruihua JIAO, Peng TANG. System-levels-based holographic fault diagnosis for complex industrial processes [J]. CIESC Journal, 2019, 70(2): 590-598.
[9]	Bingbin GU, Weili XIONG. Fault diagnosis based on PCA method with multi-block information extraction [J]. CIESC Journal, 2019, 70(2): 736-749.
[10]	Kun ZHAI, Wenxia DU, Feng LYU, Tao XIN, Xiyuan JU. Fault detect method based on improved dynamic kernel principal component analysis [J]. CIESC Journal, 2019, 70(2): 716-722.
[11]	Chunyan LU, Wei LI. Fault diagnosis method of petrochemical air compressor based on deep belief network [J]. CIESC Journal, 2019, 70(2): 757-763.
[12]	LI Peijie, YANG Bo, LI Hongguang. Association rules based conditional state fuzzy Petri nets with applications in fault diagnosis [J]. CIESC Journal, 2018, 69(8): 3517-3527.
[13]	WANG Zhanwei, WANG Lin, LIANG Kunfeng, YUAN Junfei, WANG Zhiwei. Chiller fault diagnosis based on fusional Bayesian network [J]. CIESC Journal, 2018, 69(7): 3167-3173.
[14]	XU Yuge, SUN Chengli, LAI Chunling, LUO Fei. Ensemble WELM method for imbalanced learning in fault diagnosis of wastewater treatment process [J]. CIESC Journal, 2018, 69(7): 3114-3124.
[15]	HUA Li, YU Haichen, SHAO Cheng, GONG Shixin. Monitoring and diagnosis of abnormal condition in ethylene production process based on SVM-BOXPLOT [J]. CIESC Journal, 2018, 69(3): 1053-1063.

Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature
selection

基于结合参数整定和特征选择策略的模糊支持向量机的故障诊断

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments