Key principal components with recursive local outlier factor for multimode chemical process monitoring

Journal of Process Control

Volumn 47, Issue , 2016, Pages 136-149

  Song, Bing ^a   Tan, Shuai ^a   Shi, Hongbo ^a  

^a School of Information Science and Engineering   (China)

Author keywords

Cumulative percent expression; Key principal components; Multimode process monitoring; Principal component analysis; Recursive local outlier factor

Indexed keywords

BENCHMARKING; FAULT DETECTION; PROCESS CONTROL; PROCESS MONITORING; STATISTICS;

CHEMICAL PROCESS MONITORING; CONTINUOUS STIRRED TANK REACTOR (CSTR) PROCESS; CUMULATIVE PERCENT EXPRESSION; LOCAL OUTLIER FACTOR; MONITORING PERFORMANCE; MULTIMODE PROCESS MONITORING; PRINCIPAL COMPONENTS; SEQUENTIAL INFORMATION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84988527643     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2016.09.006     Document Type: Article

References (49)

1. [1] Ge, Z.Q., Song, Z.H., Mixture Bayesian regularization method of PPCA for multimode process monitoring. AIChE J. 56 (2010), 2838–2849.
2. [2] Zhang, Y.W., Qin, S.J., Adaptive actuator/component fault compensation for nonlinear systems. AIChE J. 54 (2008), 2404–2412.
3. [3] Zhao, C.H., Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring. J. Process Control 24 (2014), 856–870.
4. [4] Ge, Z.Q., Song, Z.H., Bagging support vector data description model for batch process monitoring. J. Process Control 23 (2013), 1090–1096.
5. [5] Lu, N.Y., Yao, Y., Gao, F.R., Wang, F.L., Two-dimentional dynamic PCA for batch process monitoring. AIChE J. 51 (2005), 3300–3304.
6. [6] Yao, Y., Chen, T., Gao, F.R., Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information. J. Process Control 20 (2010), 1188–1197.
7. [7] Ma, H.H., Hu, Y., Shi, H.B., A novel local neighborhood standardization strategy and its application in fault detection of multimode processes. Chemom. Intell. Lab. Syst. 118 (2012), 287–300.
8. [8] Ge, Z.Q., Song, Z.H., Online monitoring of nonlinear multiple mode processes based on adaptive local model approach. Control Eng. Pract. 16 (2008), 1427–1437.
9. [9] Song, B., Ma, Y.X., Shi, H.B., Multimode process monitoring using improved dynamic neighborhood preserving embedding. Chemom. Intell. Lab. Syst. 135 (2014), 17–30.
10. [10] Yu, J., A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes. Chem. Eng. Sci. 68 (2011), 506–519.
11. [11] Xie, X., Shi, H.B., Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models. Ind. Eng. Chem. Res. 51 (2012), 5497–5505.
12. [12] Ma, Y.X., Song, B., Shi, H.B., Yang, Y.W., Neighborhood based global coordination for multimode process monitoring. Chemom. Intell. Lab. Syst. 139 (2014), 84–96.
13. [13] Rashid, M.M., Yu, J., Hidden Markov model based adaptive independent component analysis approach for complex chemical process monitoring and fault detection. Ind. Eng. Chem. Res. 51 (2012), 5506–5514.
14. [14] Ma, Y.X., Shi, H.B., Ma, H.H., Wang, M.L., Dynamic process monitoring using adaptive local outlier factor. Chemom. Intell. Lab. Syst. 127 (2013), 89–101.
15. [15] Zhu, J.L., Ge, Z.Q., Song, Z.H., Robust supervised probabilistic principal component analysis model for soft sensing of key process variables. Chem. Eng. Sci. 122 (2015), 573–584.
16. [16] Chen, W.C., Wang, M.S., A fuzzy c-means clustering-based fragile watermarking scheme for image authentication. Expert Syst. Appl. 36 (2009), 1300–1307.
17. [17] Sebzalli, Y.M., Wang, X.Z., Knowledge discovery from process operational data using PCA and fuzzy clustering. Eng. Appl. Artif. Intell. 14 (2001), 607–616.
18. [18] Tong, C.D., Palazoglu, A., Yan, X.F., An adaptive multimode process monitoring strategy based on modeclustering and mode unfolding. J. Process Control 23 (2013), 1497–1503.
19. [19] Zhu, Z.B., Song, Z.H., Palazoglu, A., Process pattern construction and multi-mode monitoring. J. Process Control 22 (2012), 247–262.
20. [20] Tong, C.D., Farra, N.H.E., Palazoglu, A., Yan, X.F., Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology. AIChE J. 60 (2014), 2805–2814.
21. [21] Zhang, S.M., Wang, F.L., Tan, S., Wang, S., Chang, Y.Q., Novel monitoring strategy combining the advantages of the multiple modeling strategy and Gaussian mixture model for multimode processes. Ind. Eng. Chem. Res. 541 (2015), 11866–11880.
22. [22] Song, B., Shi, H.B., Ma, Y.X., Wang, J.P., Multi-subspace principal component analysis with local outlier factor for multimode process monitoring. Ind. Eng. Chem. Res. 53 (2014), 16453–16464.
23. [23] Wang, F.L., Tan, S., Peng, J., Chang, Y.Q., Process monitoring based on mode identification for multi-mode process with transitions. Chemom. Intell. Lab. Syst. 110 (2012), 144–155.
24. [24] Tan, S., Wang, F.L., Peng, J., Chang, Y.Q., Wang, S., Multimode process monitoring based on mode identification. Ind. Eng. Chem. Res. 51 (2012), 374–388.
25. [25] Zhao, S.J., Zhang, J., Xu, Y.M., Monitoring of processes with multiple operation modes through multiple principle component analysis models. Ind. Eng. Chem. Res. 43 (2004), 7025–7035.
26. [26] Ge, Z.Q., Song, Z.H., Multimode process monitoring based on Bayesian method. J. Chemom. 23 (2009), 636–650.
27. [27] Ge, Z.Q., Gao, F.R., Song, Z.H., Two-dimensional Bayesian monitoring method for nonlinear multimode processes. Chem. Eng. Sci. 66 (2011), 5173–5183.
28. [28] Zhao, C.H., Wang, W., Qin, Y., Gao, F.R., Comprehensive subspace decomposition with analysis of between-mode relative changes for multimode process monitorin. Ind. Eng. Chem. Res. 54 (2015), 3154–3166.
29. [29] Zhao, C.H., Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring. AIChE J. 60 (2014), 559–573.
30. [30] Dobos, L., Abonyi, J., Online detection of homogeneous operation ranges by dynamic principal component analysis based time series segmentation. Chem. Eng. Sci. 75 (2012), 96–105.
31. [31] Majid, N.A.A., Taylor, M.P., Chen, J.J., Stam, M.A., Mulder, A., Young, B.R., Aluminium process fault detection by multiway principal component analysis. Control Eng. Pract. 19 (2011), 367–379.
32. [32] Lee, J.M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.B., Nonlinear process monitoring using kernel principal component analysis. Chem. Eng. Sci. 59 (2004), 223–234.
33. [33] Bakshi, B.R., Multiscale PCA with application to multivariate statistical process monitoring. AIChE J. 44 (1998), 1596–1610.
34. [34] Kim, D.S., Lee, I.B., Process monitoring based on probabilistic PCA. Chemom. Intell. Lab. Syst. 67 (2003), 109–123.
35. [35] Qin, S.J., Dunia, R., Determining the number of principal components for best reconstruction. J. Process Control 10 (2000), 245–250.
36. [36] Camacho, J., Ferrer, A., Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: practical aspects. Chemom. Intell. Lab. Syst. 131 (2014), 37–50.
37. [37] Tamura, M., Tsujita, S., A study on the number of principal components and sensitivity of fault detection using PCA. Comput. Chem. Eng. 31 (2007), 1035–1046.
38. [38] Togkalidou, T., Braatz, R.D., Johnson, B.K., Davidson, O., Andrews, A., Experimental design and inferential modeling in pharmaceutical crystallization. AIChE J. 47 (2001), 160–168.
39. [39] Jiang, Q.C., Yan, X.F., Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring. AIChE J. 60 (2014), 949–965.
40. [40] Chiang, L.H., Russell, E.L., Braatz, R.D., Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemom. Intell. Lab. Syst. 50 (2000), 243–252.
41. [41] Lee, J., Kang, B., Kang, S.H., Integrating independent component analysis and local outlier factor for plant-wide process monitoring. J. Process Control 21 (2011), 1011–1021.
42. [42] Zhao, C.H., Wang, W., Efficient faulty variable selection and parsimonious reconstructionmodelling for fault isolation. J. Process Control 38 (2016), 31–41.
43. [43] Zhang, Y.W., Qin, S.J., Improved nonlinear fault detection technique and statistical analysis. AIChE J. 54 (2008), 3204–3220.
44. [44] Zhao, C.H., Gao, F.R., Fault-relevant principal component analysis (FPCA) method for multivariate statistical modeling and process monitoring. Chemom. Intell. Lab. Syst. 133 (2014), 1–16.
45. [45] Lu, N.Y., Yang, Y., Gao, F.R., Wang, F.L., Multirate dynamic inferential modeling for multivariable processes. Chem. Eng. Sci. 59 (2004), 855–864.
46. [46] Zhao, C.H., Sun, Y.X., Comprehensive subspace decomposition and isolation of principal reconstruction directions for online fault diagnosis. J. Process Control 23 (2013), 1515–1527.
47. [47] Ricker, N.L., Optimal steady-state operation of the Tennessee Eastman challenge process. Comput. Chem. Eng. 19 (1995), 949–959.
48. [48] Downs, J.J., Vogel, E.F., A plant-wide industrial process control problem. Comput. Chem. Eng. 17 (1993), 245–255.
49. [49] Yoon, S., MacGregor, J.F., Fault diagnosis with multivariate statistical models part I: using steady state fault signatures. J. Process Control 11 (2001), 387–400.