Profile

Lei Xie received a B.S. degree in 2000 and a Ph.D. in 2005 from Zhejiang University, P.R. China. Between 2005 and 2006, he was a postdoctoral researcher at Berlin University of Technology, an Assistant Professor between 2005 and 2008 and is  currently an Associate Professor at the Department of Control Science and Engineering, Zhejiang University. To date, his research activities culminated in over 80 articles that are published in internationally renowned journals and conferences, 3 book chapters and a book in the area of applied multivariate statistics and modeling. His research interests focus on the interdisciplinary area of statistics and
system control theory.

Recent Publications:

1. Xie, L., et al., Fault detection in dynamic systems using the Kullback–Leibler divergence. Control Engineering Practice, 2015. 43: p. 39-48.
2. Sun, P., J. Chen, and L. Xie, Self-active and recursively selective Gaussian process models for nonlinear distributed parameter systems. Chemical Engineering Science, 2015. 123(0): p. 125-136.
3. Luo, L., et al., A Novel Bayesian Robust Model and Its Application for Fault Detection and Automatic Supervision of Nonlinear Process. Industrial & Engineering Chemistry Research, 2015. 54(18): p. 5048-5061.
4. Li, Z., et al., Adaptive KPCA Modeling of Nonlinear Systems. Signal Processing, IEEE Transactions on, 2015. 63(9): p. 2364-2376.
5. Zeng, J., et al., Regression-based analysis of multivariate non-Gaussian datasets for diagnosing abnormal situations in chemical processes. AIChE Journal, 2014. 60(1): p. 148-159.
6. Zeng, J., et al., Detecting abnormal situations using the Kullback–Leibler divergence. Automatica, 2014. 50(11): p. 2777-2786.
7. Yin, F., et al., Data driven model mismatch detection based on statistical band of Markov parameters. Computers & Electrical Engineering, 2014. 40(7): p. 2178-2192.
8. Xie, L., J. Zeng, and C. Gao, Novel Just-In-Time Learning-Based Soft Sensor Utilizing Non-Gaussian Information. Control Systems Technology, IEEE Transactions on, 2014. 22(1): p. 360-368.
9. Li, Z., et al., An error-in-variable projection to latent structure framework for monitoring technical systems with orthogonal signal components. Chemometrics and Intelligent Laboratory Systems, 2014. 133(0): p. 70-83.
10. Guo, Z., et al., Online detection of time-variant oscillations based on improved ITD. Control Engineering Practice, 2014. 32(0): p. 64-72.
11. Guo, Z., et al., Automatic Detection of Nonstationary Multiple Oscillations by an Improved Wavelet Packet Transform. Industrial & Engineering Chemistry Research, 2014. 53(40): p. 15686-15697.
12. Guo, Z., et al., Automatic detection of multiple oscillations by wavelet analysis. Computers & Electrical Engineering, 2014. 40(7): p. 2167-2177.
13. Cai, X., et al., Fast distributed MPC based on active set method. Computers & Chemical Engineering, 2014. 71(0): p. 158-170.
14. Cai, X., et al., ILC strategy for progress improvement of economic performance in industrial model predictive control systems. Journal of Process Control, 2014. 24(12): p. 107-118.
15. Cai, X., et al., Rapid distributed model predictive control design using singular value decomposition for linear systems. Journal of Process Control, 2014. 24(7): p. 1135-1148.
16. Zeng, J., et al., Statistical Process Monitoring Based on the Kernel Mean Discrepancy Test. Industrial & Engineering Chemistry Research, 2013. 52(5): p. 2000-2007.
17. Zakharov, A., et al., An autonomous valve stiction detection system based on data characterization. Control Engineering Practice, 2013. 21(11): p. 1507-1518.
18. Xie, L., X. Lin, and J. Zeng, Shrinking Principal Component Analysis for Enhanced Process Monitoring and Fault Isolation. Industrial & Engineering Chemistry Research, 2013. 52(49): p. 17475-17486.
19. Xie, L., Y. Cong, and A. Horch, An improved valve stiction simulation model based on ISA standard tests. Control Engineering Practice, 2013. 21(10): p. 1359-1368.
20. Gui, C., X. Lei, and C. Jian, Measuring causality by directional symbolic mutual information approach. Chin. Phys. B, 2013. 22(3): p. 38902-038902.
21. Chen, G., et al., Detecting Model–Plant Mismatch of Nonlinear Multivariate Systems Using Mutual Information. Industrial & Engineering Chemistry Research, 2013. 52(5): p. 1927-1938.
22. Zeng, J., et al., A non-Gaussian regression algorithm based on mutual information maximization. Chemometrics and Intelligent Laboratory Systems, 2012. 111(1): p. 1-19.
23. Wang, H., L. Xie, and Z. Song, A Review for Model Plant Mismatch Measures in Process Monitoring. Chinese Journal of Chemical Engineering, 2012. 20(6): p. 1039-1046.
24. Lou, H., et al., Inferential Model for Industrial Polypropylene Melt Index Prediction with Embedded Priori Knowledge and Delay Estimation. Industrial & Engineering Chemistry Research, 2012. 51(25): p. 8510-8525.
25. Liu, Z., Y. Gu, and L. Xie, An improved LQG benchmark for MPC economic performance assessment and optimisation in process industry. Canadian Journal of Chemical Engineering, 2012. 90(6): p. 1434-1441.
26. Krüger, U. and L. Xie, Advances in statistical monitoring of complex multivariate processes : with applications in industrial process control. 2012, Chichester, West Sussex ; Hoboken, N.J.: Wiley.
27. Ge, Z., et al., Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions. AIChE Journal, 2012. 58(8): p. 2357-2372.
28. Gao, X., et al. Detecting model-plant-mismatch of nonlinear multivariate systems using mutual information. in 8th IFAC International Symposium on Advanced Control of Chemical Processes, Kariwala, V., Samavedham, L., Braatz, RD, eds., International Federation of Automatic Control (IFAC), Singapore. 2012.
29. Fu, R., et al., PID control performance assessment using iterative convex programming. Journal of Process Control, 2012. 22(9): p. 1793-1799.< !E et al. Performance assessment of PID controller with time-variant disturbance dynamics. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
31. Zhou, M., et al. Performance assessment of PID controller with time-variant disturbance dynamics. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
32. Zeng, J., et al. Independent component regression based on mutual information maximization. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
33. Zeng, J., et al. Independent component regression based on mutual information maximization. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
34. Liu, Z., et al. MPC economic performance assessment based on equal-grid LQG benchmark. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
35. Liu, Z., et al. MPC economic performance assessment based on equal-grid LQG benchmark. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
36. Fu, R., L. Xie, and Z. Song. Structure restricted (PID) controller performance assessment for multi-stage batch processes with tracking and regulatory requirements. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
37. Fu, R., L. Xie, and Z. Song. Structure restricted (PID) controller performance assessment for multi-stage batch processes with tracking and regulatory requirements. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
38. Ge, Z., et al., Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches. Mechanical Systems and Signal Processing, 2010. 24(8): p. 2972-2984.
39. Feital, T., et al., A unified statistical framework for monitoring multivariate systems with unknown source and error signals. Chemometrics and Intelligent Laboratory Systems, 2010. 104(2): p. 223-232.
40. 张建明, et al., 基于支持向量描述的非高斯过程故障重构与诊断. 化工学报, 2009. 60(1): p. 168-171.
41. 谢磊, et al., 基于NGPP-SVDD的非高斯过程监控及其应用研究. 自动化学报, 2009. 35(1): p. 107-112.
42. Zhang, J. and L. Xie, Particle swarm optimization algorithm for constrained problems. Asia-Pacific Journal of Chemical Engineering, 2009. 4(4): p. 437-442.
43. Xie, L., et al., Non-Gaussian process monitoring based on NGPP-SVDD. Acta Automatica Sinica, 2009. 35(Compendex): p. 107-112.
44. Liu, X.Q., et al., Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometrics and Intelligent Laboratory Systems, 2009. 96(2): p. 132-143.
45. Ge, Z.Q., L. Xie, and Z.H. Song, A Novel Statistical-Based Monitoring Approach for Complex Multivariate Processes. Industrial & Engineering Chemistry Research, 2009. 48(10): p. 4892-4898.
46. Ge, Z., et al., Sensor fault identification and isolation for multivariate non-Gaussian processes. Journal of Process Control, 2009. 19(10): p. 1707-1715.
47. Xie, L., et al., Adaptive subspace identification based on fast moving window QR decomposition. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2008. 59(Compendex): p. 1448-1453.
48. Liu, X., et al., Statistical-based monitoring of multivariate non-Gaussian systems. AIChE Journal, 2008. 54(9): p. 2379-2391.
49. Zhang, J., et al., Fault diagnosis based on RS and SVM. Qinghua Daxue Xuebao/Journal of Tsinghua University, 2007. 47(Compendex): p. 1774-1777.
50. Xie, L. and S. Wang, Recursive kernel PCA and its application in adaptive monitoring of nonlinear processes. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2007. 58(Compendex): p. 1776-1782.
51. Xie, L. and J.M. Zhang, Swarm intelligent analysis of independent component and its application in fault detection and diagnosis. Rough Sets and Knowledge Technology, Proceedings, 2006. 4062: p. 742-749.
52. Xie, L., J. Zhang, and S. Wang, Investigation of Dynamic Multivariate Chemical Process Monitoring. Chinese Journal of Chemical Engineering, 2006. 14(Compendex): p. 559-568.
53. Xie, L., J. Zhang, and S. Wang, Sensor fault diagnosis with statistical signal reconstruction approach. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2006. 57(Compendex): p. 2343-2348.
54. Xie, L. and J. Zhang. Load-Shifts optimization of combined-cycle power plant with dynamic particle swarm approach. in International Conference on Sensing, Computing and Automation. 2006. Chongqing, China: Watam Press.
55. Xie, L. and J. Wu, Global optimal ICA and its application in MEG data analysis. Neurocomputing, 2006. 69(16-18): p. 2438-2442.
56. Xie, L., et al., Statistical monitoring of dynamic multivariate processes - Part 1. Modeling autocorrelation and cross-correlation. Industrial & Engineering Chemistry Research, 2006. 45(5): p. 1659-1676.
57. Xie, L., Swarm intelligent tuning of one-class v-SVM parameters, in Rough Sets and Knowledge Technology, J.F.P. G. Wang, A. Skowron, Y. Yao, Editor. 2006, Springer Verlag: Berlin. p. 552-559.
58. Lieftucht, D., et al., Statistical Monitoring of Dynamic Multivariate Processes - Part 2. Identifying Fault Magnitude and Signature. Ind. Eng. Chem. Res., 2006. 45(5): p. 1677-1688.
59. Jiang, L., L. Xie, and S. Wang, Fault diagnosis for batch processes by improved multi-model Fisher discriminant analysis. Chinese Journal of Chemical Engineering, 2006. 14(Compendex): p. 343-348.
60. Zhou, S., L. Xie, and S. Wang, On-line fault diagnosis in industrial processes using variable moving window and hidden Markov model. Chinese Journal of Chemical Engineering, 2005. 13(Compendex): p. 388-395.
61. Xie, L., S. Wang, and J. Zhang, PSO-based robust MPCA and its application to batch process monitoring. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2005. 56(Compendex): p. 492-498.
62. Xie, L., P. Li, and G. Wozny. Chance constrained nonlinear model predictive control. in International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control. 2005. Freudenstadt-Lauterbad, Germany: Springer Verlag.
63. Jiang, L.-Y., et al. Monitoring batch processes using multi-model discriminant partial least squares. in International Conference on Machine Learning and Cybernetics, ICMLC 2005, August 18, 2005 - August 21, 2005. 2005. Guangzhou, China: Institute of Electrical and Electronics Engineers Computer Society.
64. He, N., et al., Fault detection and diagnosis in continuous dynamic multivariable processes using independent component analysis. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2005. 56(Compendex): p. 646-652.
65. Dong, S.-L., S.-Q. Wang, and L. Xie, Robust PLS and its application to batch process monitoring. Kongzhi yu Juece/Control and Decision, 2005. 20(Compendex): p. 823-826.
66. Zhou, S., L. Xie, and S. Wang. A variable moving window approach for on-line fault diagnosis in industrial processes. in WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings, June 15, 2004 - June 19, 2004. 2004. Hangzhou, China: Institute of Electrical and Electronics Engineers Inc.
67. Zhang, Z., S. Wang, and L. Xie. The adaptive predictive function control based on Laguerre model. in WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings, June 15, 2004 - June 19, 2004. 2004. Hangzhou, China: Institute of Electrical and Electronics Engineers Inc.
68. Xie, L., J. Zhang, and S. Wang, Robust statistical batch process monitoring framework and its application. Chinese Journal of Chemical Engineering, 2004. 12(Compendex): p. 682-687.
69. Xie, L., N. He, and S.-Q. Wang, Step-by-step adaptive MPCA applied to an industrial batch process. Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities, 2004. 18(Compendex): p. 643-647.
70. Xie, L., et al., Adaptive predictive functional control based on multiple-model. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2003. 37(Compendex): p. 190-193.
71. Xie, L., et al. Linear pruning techniques for neural networks - Based on projection latent structure. in System Security and Assurance, October 5, 2003 - October 8, 2003. 2003. Washington, DC, United states: Institute of Electrical and Electronics Engineers Inc.
72. Xie, L., J.-M. Zhang, and S.-Q. Wang. Distance between PCs and its Application to Step-by-Step Multi-Way PCA. in ISTM/2003 5th Internatinal Symposium on Test and Measurement, June 1, 2003 - June 5, 2003. 2003. Shenzhen, China: International Academic Publishers.
73. Xie, L., et al. Block recursive MPCA and its application in batch process monitoring. in System Security and Assurance, October 5, 2003 - October 8, 2003. 2003. Washington, DC, United states: Institute of Electrical and Electronics Engineers Inc.
74. Guo, M., et al. Research on an integrated ICA-SVM based framework for fault diagnosis. in System Security and Assurance, October 5, 2003 - October 8, 2003. 2003. Washington, DC, United states: Institute of Electrical and Electronics Engineers Inc.
75. Xie, L., J.-M. Zhang, and S.-Q. Wang. Stability analysis of networked control system. in Proceedings of 2002 International Conference on Machine Learning and Cybernetics, November 4, 2002 - November 5, 2002. 2002. Beijing, China: Institute of Electrical and Electronics Engineers Inc.
Prof. Lei Xie received an engineering degree in automation from Zhejiang University, China, in June 2000,  and the doctoral degree in control sciences & engineering from Zhejiang University under supervision of Prof. Shuqing Wang, in March 2005. During his graduate study, he visited Berlin University of Technology as an exchanging student from 2004 to 2005. From 2005 to 2007, he has been the postdoctor of Berlin University of Technology and Zhejiang Univeristy and his cooperative professors are Prof. Gunter Wonzy and Prof. Pu Li. During the period 2007–2008, he was a visiting researcher of the control group of Queen's University of Belfast, UK.  From 2008 on, Lei Xie is a member of the intitute of Cyber-Systems & Control and was appointed as Associate Professor in July, 2008. 

Prof. Lei Xie's main research interest is the interdisciplinary domain of statistics and system theory. In this field he has published over 20 papers. Current activities focus on the transfer of knowledge about new identification, statistical fault detection & diagnosis and data-driven control performance assement methodologies to industry. Application areas include petrochemical industry, chemical industry, metallurgy industry etc.

Publications:

1. Xie, L., et al., Fault detection in dynamic systems using the Kullback–Leibler divergence. Control Engineering Practice, 2015. 43: p. 39-48.
2. Sun, P., J. Chen, and L. Xie, Self-active and recursively selective Gaussian process models for nonlinear distributed parameter systems. Chemical Engineering Science, 2015. 123(0): p. 125-136.
3. Luo, L., et al., A Novel Bayesian Robust Model and Its Application for Fault Detection and Automatic Supervision of Nonlinear Process. Industrial & Engineering Chemistry Research, 2015. 54(18): p. 5048-5061.
4. Li, Z., et al., Adaptive KPCA Modeling of Nonlinear Systems. Signal Processing, IEEE Transactions on, 2015. 63(9): p. 2364-2376.
5. Zeng, J., et al., Regression-based analysis of multivariate non-Gaussian datasets for diagnosing abnormal situations in chemical processes. AIChE Journal, 2014. 60(1): p. 148-159.
6. Zeng, J., et al., Detecting abnormal situations using the Kullback–Leibler divergence. Automatica, 2014. 50(11): p. 2777-2786.
7. Yin, F., et al., Data driven model mismatch detection based on statistical band of Markov parameters. Computers & Electrical Engineering, 2014. 40(7): p. 2178-2192.
8. Xie, L., J. Zeng, and C. Gao, Novel Just-In-Time Learning-Based Soft Sensor Utilizing Non-Gaussian Information. Control Systems Technology, IEEE Transactions on, 2014. 22(1): p. 360-368.
9. Li, Z., et al., An error-in-variable projection to latent structure framework for monitoring technical systems with orthogonal signal components. Chemometrics and Intelligent Laboratory Systems, 2014. 133(0): p. 70-83.
10. Guo, Z., et al., Online detection of time-variant oscillations based on improved ITD. Control Engineering Practice, 2014. 32(0): p. 64-72.
11. Guo, Z., et al., Automatic Detection of Nonstationary Multiple Oscillations by an Improved Wavelet Packet Transform. Industrial & Engineering Chemistry Research, 2014. 53(40): p. 15686-15697.
12. Guo, Z., et al., Automatic detection of multiple oscillations by wavelet analysis. Computers & Electrical Engineering, 2014. 40(7): p. 2167-2177.
13. Cai, X., et al., Fast distributed MPC based on active set method. Computers & Chemical Engineering, 2014. 71(0): p. 158-170.
14. Cai, X., et al., ILC strategy for progress improvement of economic performance in industrial model predictive control systems. Journal of Process Control, 2014. 24(12): p. 107-118.
15. Cai, X., et al., Rapid distributed model predictive control design using singular value decomposition for linear systems. Journal of Process Control, 2014. 24(7): p. 1135-1148.
16. Zeng, J., et al., Statistical Process Monitoring Based on the Kernel Mean Discrepancy Test. Industrial & Engineering Chemistry Research, 2013. 52(5): p. 2000-2007.
17. Zakharov, A., et al., An autonomous valve stiction detection system based on data characterization. Control Engineering Practice, 2013. 21(11): p. 1507-1518.
18. Xie, L., X. Lin, and J. Zeng, Shrinking Principal Component Analysis for Enhanced Process Monitoring and Fault Isolation. Industrial & Engineering Chemistry Research, 2013. 52(49): p. 17475-17486.
19. Xie, L., Y. Cong, and A. Horch, An improved valve stiction simulation model based on ISA standard tests. Control Engineering Practice, 2013. 21(10): p. 1359-1368.
20. Gui, C., X. Lei, and C. Jian, Measuring causality by directional symbolic mutual information approach. Chin. Phys. B, 2013. 22(3): p. 38902-038902.
21. Chen, G., et al., Detecting Model–Plant Mismatch of Nonlinear Multivariate Systems Using Mutual Information. Industrial & Engineering Chemistry Research, 2013. 52(5): p. 1927-1938.
22. 刘詟, et al., 基于线性二次型高斯基准的多变量预测控制技术经济性能评估. 控制理论与应用, 2012. 29(12): p. 1530-1536.
23. Zeng, J., et al., A non-Gaussian regression algorithm based on mutual information maximization. Chemometrics and Intelligent Laboratory Systems, 2012. 111(1): p. 1-19.
24. Wang, H., L. Xie, and Z. Song, A Review for Model Plant Mismatch Measures in Process Monitoring. Chinese Journal of Chemical Engineering, 2012. 20(6): p. 1039-1046.
25. Lou, H., et al., Inferential Model for Industrial Polypropylene Melt Index Prediction with Embedded Priori Knowledge and Delay Estimation. Industrial & Engineering Chemistry Research, 2012. 51(25): p. 8510-8525.
26. Liu, Z., Y. Gu, and L. Xie, An improved LQG benchmark for MPC economic performance assessment and optimisation in process industry. Canadian Journal of Chemical Engineering, 2012. 90(6): p. 1434-1441.
27. Krüger, U. and L. Xie, Advances in statistical monitoring of complex multivariate processes : with applications in industrial process control. 2012, Chichester, West Sussex ; Hoboken, N.J.: Wiley.
28. Ge, Z., et al., Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions. AIChE Journal, 2012. 58(8): p. 2357-2372.
29. Gao, X., et al. Detecting model-plant-mismatch of nonlinear multivariate systems using mutual information. in 8th IFAC International Symposium on Advanced Control of Chemical Processes, Kariwala, V., Samavedham, L., Braatz, RD, eds., International Federation of Automatic Control (IFAC), Singapore. 2012.
30. Fu, R., et al., PID control performance assessment using iterative convex programming. Journal of Process Control, 2012. 22(9): p. 1793-1799.
31. 许仙珍, 谢磊, and 王树青, 基于PCA混合模型的多工况过程监控. 化工学报 /CIESC Journal 2011. 62(3): p. 743-752.
32. 陈贵, et al., 基于子空间方法的模型失配检测研究. 化工学报/CIESC Journal, 2011. 62(9): p. 2575-2581.
33. Zhou, M., et al. Performance assessment of PID controller with time-variant disturbance dynamics. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
34. Zhou, M., et al. Performance assessment of PID controller with time-variant disturbance dynamics. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
35. Zeng, J., et al. Independent component regression based on mutual information maximization. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
36. Zeng, J., et al. Independent component regression based on mutual information maximization. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
37. Liu, Z., et al. MPC economic performance assessment based on equal-grid LQG benchmark. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
38. Liu, Z., et al. MPC economic performance assessment based on equal-grid LQG benchmark. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
39. Fu, R., L. Xie, and Z. Song. Structure restricted (PID) controller performance assessment for multi-stage batch processes with tracking and regulatory requirements. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
40. Fu, R., L. Xie, and Z. Song. Structure restricted (PID) controller performance assessment for multi-stage batch processes with tracking and regulatory requirements. in Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on. 2011.
41. 周猛飞, et al., 双层结构PID 控制器性能评估. 计算机与应用化学, 2010. 27(10): p. 1383-1386.
42. 张建明, et al., 支持向量描述性能优化及其在非高斯过程监控中的应用. 化工学报, 2010. 61(8): p. 2072-2077.
43. 张建明, et al., 基于混合PCA模型的多工况过程监控方法. 控制工程, 2010. 17(4): p. 553-556.
44. 韩俊林, et al., 一种执行阀粘滞特性检测方法研究. 化工自动化与仪表, 2010. 37(3): p. 47-51.
45. 韩俊林, et al., 克服阀粘滞特性的控制方法研究. 计算机与应用化学, 2010. 27(1): p. 68-72.
46. 韩俊林, 王树青, and 谢磊, 控制阀粘滞特性的频域检测方法研究. 计算机工程与应用, 2010. 46(33): p. 244-248.
47. Ge, Z., et al., Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches. Mechanical Systems and Signal Processing, 2010. 24(8): p. 2972-2984.
48. Feital, T., et al., A unified statistical framework for monitoring multivariate systems with unknown source and error signals. Chemometrics and Intelligent Laboratory Systems, 2010. 104(2): p. 223-232.
49. 张建明, et al., 基于支持向量描述的非高斯过程故障重构与诊断. 化工学报, 2009. 60(1): p. 168-171.
50. 谢磊, et al., 基于NGPP-SVDD的非高斯过程监控及其应用研究. 自动化学报, 2009. 35(1): p. 107-112.
51. Zhang, J. and L. Xie, Particle swarm optimization algorithm for constrained problems. Asia-Pacific Journal of Chemical Engineering, 2009. 4(4): p. 437-442.
52. Xie, L., et al., Non-Gaussian process monitoring based on NGPP-SVDD. Acta Automatica Sinica, 2009. 35(Compendex): p. 107-112.
53. Liu, X.Q., et al., Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometrics and Intelligent Laboratory Systems, 2009. 96(2): p. 132-143.
54. Ge, Z.Q., L. Xie, and Z.H. Song, A Novel Statistical-Based Monitoring Approach for Complex Multivariate Processes. Industrial & Engineering Chemistry Research, 2009. 48(10): p. 4892-4898.
55. Ge, Z., et al., Sensor fault identification and isolation for multivariate non-Gaussian processes. Journal of Process Control, 2009. 19(10): p. 1707-1715.
56. Xie, L., et al., Adaptive subspace identification based on fast moving window QR decomposition. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2008. 59(Compendex): p. 1448-1453.
57. Liu, X., et al., Statistical-based monitoring of multivariate non-Gaussian systems. AIChE Journal, 2008. 54(9): p. 2379-2391.
58. Zhang, J., et al., Fault diagnosis based on RS and SVM. Qinghua Daxue Xuebao/Journal of Tsinghua University, 2007. 47(Compendex): p. 1774-1777.
59. Xie, L. and S. Wang, Recursive kernel PCA and its application in adaptive monitoring of nonlinear processes. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2007. 58(Compendex): p. 1776-1782.
60. Xie, L. and J.M. Zhang, Swarm intelligent analysis of independent component and its application in fault detection and diagnosis. Rough Sets and Knowledge Technology, Proceedings, 2006. 4062: p. 742-749.
61. Xie, L., J. Zhang, and S. Wang, Investigation of Dynamic Multivariate Chemical Process Monitoring. Chinese Journal of Chemical Engineering, 2006. 14(Compendex): p. 559-568.
62. Xie, L., J. Zhang, and S. Wang, Sensor fault diagnosis with statistical signal reconstruction approach. Huagong Xuebao/Journal of Chemical Industry and Engineering (China), 2006. 57(Compendex): p. 2343-2348.
63. Xie, L. and J. Zhang. Load-Shifts optimization of combined-cycle power plant with dynamic particle swarm approach. in International Conference on Sensing, Computing and Automation. 2006. Chongqing, China: Watam Press.
64. Xie, L. and J. Wu, Global optimal ICA and its application in MEG data analysis. Neurocomputing, 2006. 69(16-18): p. 2438-2442.
65. Xie, L., et al., Statistical monitoring of dynamic multivariate processes - Part 1. Modeling autocorrelation and cross-correlation. Industrial & Engineering Chemistry Research, 2006. 45(5): p. 1659-1676.
66. Xie, L., Swarm intelligent tuning of one-class v-SVM parameters, in Rough Sets and Knowledge Technology, J.F.P. G. Wang, A. Skowron, Y. Yao, Editor. 2006, Springer Verlag: Berlin. p. 552-559.
67. Lieftucht, D., et al., Statistical Monitoring of Dynamic Multivariate Processes - Part 2. Identifying Fault Magnitude and Signature. Ind. Eng. Chem. Res., 2006. 45(5): p. 1677-1688.
68. Jiang, L., L. Xie, and S. Wang, Fault diagnosis for batch processes by improved multi-model Fisher discriminant analysis. Chinese Journal of Chemical Engineering, 2006. 14(Compendex): p. 343-348.
69. Zhou, S., L. Xie, and S. Wang, On-line fault diagnosis in industrial processes using variable moving window and hidden Markov model. Chinese Journal of Chemical Engineering, 2005. 13(Compendex): p. 388-395.
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Research work

Statistical Fault Detection & Diagnosis; Control Performance Assessment; System Identification; Process Control;

Contact

Phone:
E-mail:leix(at)csc.zju.edu.cn