High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

doi:10.1016/j.cjche.2015.03.007

›› 2015, Vol. 23 ›› Issue (9): 1509-1517.DOI: 10.1016/j.cjche.2015.03.007

• 过程系统工程与过程安全 • 上一篇下一篇

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

Ying Tian, Wenli Du, Feng Qian

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2014-09-07 修回日期:2015-01-26 出版日期:2015-09-28 发布日期:2015-10-24
通讯作者: Wenli Du
基金资助:
Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (61333010 21276078), the National Science Fund for Outstanding Young Scholars (61222303), the Fundamental Research Funds for the Central Universities, Shanghai Rising-Star Program (13QH1401200), the Program for New Century Excellent Talents in University (NCET- 10-0885) and Shanghai R&D Platform Construction Program (13DZ2295300).

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

Ying Tian, Wenli Du, Feng Qian

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2014-09-07 Revised:2015-01-26 Online:2015-09-28 Published:2015-10-24
Supported by:
Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (61333010 21276078), the National Science Fund for Outstanding Young Scholars (61222303), the Fundamental Research Funds for the Central Universities, Shanghai Rising-Star Program (13QH1401200), the Program for New Century Excellent Talents in University (NCET- 10-0885) and Shanghai R&D Platform Construction Program (13DZ2295300).

摘要/Abstract

摘要： Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOMdistinguishes various states on the outputmap.Monitoring results of PX oxidation reaction process indicate that the LTSA-SOM can well detect and visualize the fault type.

关键词: Self-organizing map, Local tangent space alignment, Fault diagnosis, Visualization, P-xylene oxidation

Abstract: Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOMdistinguishes various states on the outputmap.Monitoring results of PX oxidation reaction process indicate that the LTSA-SOM can well detect and visualize the fault type.

Key words: Self-organizing map, Local tangent space alignment, Fault diagnosis, Visualization, P-xylene oxidation

Ying Tian, Wenli Du, Feng Qian. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process[J]. , 2015, 23(9): 1509-1517.

参考文献

[1] A. Cincotti, R. Orru, A. Broi, G. Cao, Effect of catalyst concentration and simulation of precipitation processes on liquid-phase catalytic oxidation of p-xylene to terephthalic acid, Chem. Eng. Sci. 52 (21) (1997) 4205-4213.
[2] T. Kohonen, The self-organizing map, Proc. IEEE 78 (9) (1990) 1464-1480.
[3] L. Cser, A.S. Korhonen, P.Mantyla, O. Simula, Data mining in improving the geometric quality parameters of hot rolled strips, Proceedings of the 1999 International Conference on Quality Manufacturing, 1999 (Stellenbosch, South Africa).
[4] H. Saxén, L. Lassus, M. Seppänen, T. Karjalahti, Pattern recognition and classification of blast furnace wall temperatures, Ironmak. Steelmak. 27 (3) (2000) 207-211.
[5] J.S. Van Deventer, D.W. Moolman, C. Aldrich, Visualisation of plant disturbances using self-organising maps, Comput. Chem. Eng. 20 (1996) S1095-S1100.
[6] J. Ahola, E. Alhoniemi, O. Simula, Monitoring industrial processes using the selforganizing map, Soft Computing Methods in Industrial Applications, 1999. SMCia/ 99. Proceedings of the 1999 IEEE Midnight-Sun Workshop on: IEEE, 1999.
[7] Y.S. Ng, R. Srinivasan, Multivariate temporal data analysis using self-organizing maps. 1. Training methodology for effective visualization of multistate operations, Ind. Eng. Chem. Res. 47 (20) (2008) 7744-7757.
[8] Y.S. Ng, R. Srinivasan, Multivariate temporal data analysis using self-organizing maps. 2. Monitoring and diagnosis of multistate operations, Ind. Eng. Chem. Res. 47 (20) (2008) 7758-7771.
[9] F. Corona, M.Mulas, R. Baratti, J.A. Romagnoli, On the topologicalmodeling and analysis of industrial process data using the SOM, Comput. Chem. Eng. 34 (12) (2010) 2022-2032.
[10] Z. Ge, C. Yang, Z. Song, Improved kernel PCA-basedmonitoring approach for nonlinear processes, Chem. Eng. Sci. 64 (9) (2009) 2245-2255.
[11] I. Borg, P. Groenen, Modern Multidimensional Scaling, Springer, NY, 1997.
[12] D. DeMers, G. Cottrell, Non-linear dimensionality reduction, Adv. Neural Inf. Process. Syst. (1993) 580-580.
[13] S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290 (5500) (2000) 2323-2326.
[14] M.E. Tipping, C.M. Bishop, Mixtures of probabilistic principal component analyzers, Neural Comput. 11 (2) (1999) 443-482.
[15] Y. Dai, J. Zhao, Fault diagnosis of batch chemical processes using a dynamic time warping (DTW)-based artificial immune system, Ind. Eng. Chem. Res. 50 (8) (2011) 4534-4544.
[16] K. Ghosh, R. Srinivasan, Immune-system-inspired approach to process monitoring and fault diagnosis, Ind. Eng. Chem. Res. 50 (3) (2010) 1637-1651.
[17] X. Chen, X. Yan, Using improved self-organizing map for fault diagnosis in chemical industry process, Chem. Eng. Res. Des. 90 (12) (2012) 2262-2277.
[18] X. Chen, X. Yan, Fault diagnosis in chemical process based on self-organizingmap integrated with fisher discriminant analysis, Chin. J. Chem. Eng. 21 (4) (2013) 382-387.
[19] Z. Zhang, H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, J. Shanghai Univ. Engl. Ed. 8 (4) (2004) 406-424.
[20] L. Tao, X. Kong,W. Zhong, F. Qian,Modified self-adaptive immune genetic algorithm for optimization of combustion side reaction of p-xylene oxidation, Chin. J. Chem. Eng. 20 (6) (2012) 1047-1052.
[21] B. Xu, R. Qi, W. Zhong, F. Qian, Optimization of p-xylene oxidation reaction process based on self-adaptive multi-objective differential evolution, Chemom. Intell. Lab. Syst. 127 (2013) 55-62.
[22] F. Qian, L. Tao,W. Sun,W. Du, Development of a free radical kinetic model for industrial oxidation of p-xylene based on artificial neural network and adaptive immune genetic algorithm, Ind. Eng. Chem. Res. 51 (8) (2012) 3229-3237.

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

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