Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

doi:10.1016/S1004-9541(13)60454-1

Abstract

Abstract: Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Key words: nonlinear locality preserving projection, kernel trick, sparse model, fault detection

摘要： Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

关键词: nonlinear locality preserving projection, kernel trick, sparse model, fault detection

DENG Xiaogang, TIAN Xuemin. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. Chin.J.Chem.Eng., 2013, 21(2): 163-170.

邓晓刚, 田学民. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 163-170.

References

1 Liang, J., Qian, J., “Multivariate statistical process monitoring and control: recent developments and applications to chemical industry”, Chin. J. Chem. Eng., 11 (2), 191-203 (2003).

2 Ye, L., Shi, X., Liang J., “A multi-level approach for complex fault isolation based on structured residuals”, Chin. J. Chem. Eng., 19 (3), 462-472 (2011).

3 Liu, X., Li, K., McAfee, M., Irwin, G.W., “Improved nonlinear PCA process monitoring using support vector data description”, Journal of Process Control, 21 (9), 1306-1317 (2011).

4 Wang, Z., Yuan, J., “Online supervision of penicillin cultivations based on rolling MPCA”, Chin. J. Chem. Eng., 15 (1), 92-96 (2007).

5 Zhang, Y., Ma, C., “Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS”, Chem. Eng. Sci., 66 (1), 64-72 (2011).

6 Deng, X., Tian, X., “Multivariate statistical process monitoring using multiscale kernel principal component analysis”, In: Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Tsinghua University, Beijing, 108-112 (2006).

7 He, X., Niyogi, P., “Locality preserving projections”, Adv. Neural Infor. Process. Systems, 16, 153-160 (2003).

8 Roweis, S.T., Saul, L.K., “Nonlinear dimensionality reduction by locally linear embedding”, Science, 290 (5500), 2323-2326 (2000).

9 Zhang, T., Yang, J., Zhao, D., Ge, X., “Linear local tangent space alignment and application to face recognition”, Neurocomputing, 70 (7-9), 1547-1553 (2007).

10 Hu, K., Yuan, J., “Multivariate statistical process control based on multiway locality preserving projections”, Journal of Process Control, 18 (7-8), 797-807 (2008).

11 Yu, J. B., “Bearing performance degradation assessment using locality preserving projections”, Expert Systems Application, 38 (6), 7440-7450 (2011).

12 Zhang, M., Ge, Z., Song, Z.,“Global-local structure analysis model and its application for fault detection and identification”, Ind. Eng. Chem. Res., 50 (11), 6837-6848 (2011).

13 He, X., Cai, D., Min, W., “Statistical and computational analysis of locality preserving projection”, In: Proceedings of the 22th International Conference on Machine Learning, Bonn, Germanny, 281-288 (2005).

14 Mika, S., Ratsch, G. Weston, J., Scholkopf, B., Smola, A., Muller, K.R., “Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces”, IEEE Transactions on Pattern Anal. Machine Intelligent, 25 (5), 623-628 (2003).

15 Melzer, T., Reiter, M., Bischof, H., “Appearance models based on kernel canonical correlation analysis”, Pattern Recognition, 36 (9), 1961-1971 (2003).

16 Choi, S.W., Lee, C., Lee, J.M., Park, J.H., Lee, I.B., “Fault detection and identification of nonlinear processes based on kernel PCA”, Chemometrics and Intelligent Laboratory Systems, 75 (1), 55-67 (2005).

17 Sumana, C., Bhushan, M., Venkateswarlu, C., Gudi, R.D., “Improved nonlinear process monitoring using KPCA with sample vector selection and combined index”, Asia-Pacific J. Chem. Eng., 6 (3), 460-469 (2011).

18 Baudat, G., Anouar, F., “Feature vector selection and projection using kernels”, Neurocomputing, 55 (1-2), 21-38 (2003).

19 Lee, J.M., Yoo, C.K., Lee, I.B., “Statistical process monitoring with independent component analysis”, Journal of Process Control, 14 (5), 467-485 (2004).

20 Tian, X., Zhang, X., Deng, X., Chen, S., “Multiway kernel independent component analysis based on feature samples for batch process monitoring”, Neurocomputing, 72 (7-9), 1584-1596 (2009).

21 Singhal, A., Seborg, D.E., “Effect of data compression on pattern matching in historical data”, Ind. Eng. Chem. Res., 44 (9), 3203-3212 (2005).

22 Johannesmeyer, M.C., Singhal, A., Seborg, D.E., “Pattern matching in historical data”, AIChE J., 48 (9), 2022-2038 (2002).

[1]	Muhammad Nawaz, Abdulhalim Shah Maulud, Haslinda Zabiri, Syed Ali Ammar Taqvi, Alamin Idris. Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework [J]. Chinese Journal of Chemical Engineering, 2021, 29(1): 253-265.
[2]	Cen Guo, Wenkai Hu, Fan Yang, Dexian Huang. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data [J]. Chinese Journal of Chemical Engineering, 2020, 28(9): 2358-2367.
[3]	Liangliang Sun, Jianghua Wu, Haiqi Jia, Xuebin Liu. Research on fault detection method for heat pump air conditioning system under cold weather [J]. Chin.J.Chem.Eng., 2017, 25(12): 1812-1819.
[4]	Guozhu Wang, Jianchang Liu, Yuan Li, Cheng Zhang. Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy [J]. , 2016, 24(7): 869-880.
[5]	Fan Wang, Honglin Zhu, Shuai Tan, Hongbo Shi. Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring [J]. , 2016, 24(7): 856-860.
[6]	Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes [J]. Chin.J.Chem.Eng., 2015, 23(8): 1357-1363.
[7]	Kangling Liu, Xin Jin, Zhengshun Fei, Jun Liang. Adaptive partitioning PCA model for improving fault detection and isolation [J]. Chin.J.Chem.Eng., 2015, 23(6): 981-991.
[8]	Shijin Ren, Zhihuan Song, Maoyun Yang, Jianguo Ren . A novel multimode processmonitoring method integrating LCGMMwith modified LFDA [J]. Chin.J.Chem.Eng., 2015, 23(12): 1970-1980.
[9]	Bing Song, Yuxin Ma, Hongbo Shi. Improved performance of process monitoring based on selection of key principal components [J]. Chin.J.Chem.Eng., 2015, 23(12): 1951-1957.
[10]	Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring [J]. , 2014, 22(7): 820-827.
[11]	WANG Li, SHI Hongbo . Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes [J]. Chin.J.Chem.Eng., 2014, 22(6): 657-663.
[12]	Xiaogang Deng, Xuemin Tian. Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis [J]. , 2014, 22(11/12): 1260-1267.
[13]	Lianfang Cai, Xuemin Tian, Ni Zhang. A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring [J]. , 2014, 22(11/12): 1243-1253.
[14]	LIYunfeng,WANGZhifeng, YUANJingqi. On-line fault detection using SVM-based dynamic MPLS for batch processes [J]. , 2006, 14(6): 754-758.
[15]	LIANG Jun, QIAN Jixin. Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry [J]. , 2003, 11(2): 191-203.