Hierarchical Bayesian Network Modeling Framework for Large-Scale Process Monitoring and Decision Making

IEEE Transactions on Control Systems Technology

Volumn 28, Issue 2, 2020, Pages 671-679

  Chen, Guangjie ^a   Ge, Zhiqiang ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Bayesian network; decision making; fault diagnosis; hierarchical framework; large scale process monitoring

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DATA STRUCTURES; DECISION MAKING; FAILURE ANALYSIS; FAULT DETECTION; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; MONITORING; PROCESS CONTROL; PROCESS MONITORING; TOPOLOGY;

BAYES METHOD; FUNCTIONAL LAYER; HIERARCHICAL BAYESIAN NETWORKS; HIERARCHICAL FRAMEWORK; MONITORING INFORMATION; NETWORK STRUCTURES; TOPOLOGICAL STRUCTURE; WHOLE PROCESS;

BAYESIAN NETWORKS;

EID: 85058151520     PISSN: 10636536     EISSN: 15580865     Source Type: Journal    
DOI: 10.1109/TCST.2018.2882562     Document Type: Article

References (30)

1. Z. Ge, "Review on data-driven modeling and monitoring for plantwide industrial processes," Chemometrics Intell. Lab. Syst., vol. 171, pp. 16-25, Dec. 2017.
2. J. Zhu, Z. Ge, Z. Song, and F. Gao, "Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data," Annu. Rev. Control, vol. 46, pp. 107-133, 2018.
3. Z. Ge and Z. Song, "Distributed PCA model for plant-wide process monitoring," Ind. Eng. Chem. Res., vol. 52, no. 5, pp. 1947-1957, 2013.
4. Z. Ge, "Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes," J. Process Control, vol. 65, pp. 107-117, May 2018.
5. Q. Jiang, B. Huang, S. X. Ding, and X. Yan, "Bayesian fault diagnosis with asynchronous measurements and its application in networked distributed monitoring," IEEE Trans. Ind. Electron., vol. 63, no. 10, pp. 6316-6324, Oct. 2016.
6. Q. Jiang and B. Huang, "Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method," J. Process Control, vol. 46, pp. 75-83, Oct. 2016.
7. J. Zhu, Z. Ge, and Z. Song, "Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data," IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 1877-1885, Aug. 2017.
8. K. Ghosh, M. Ramteke, and R. Srinivasan, "Optimal variable selection for effective statistical process monitoring," Comput. Chem. Eng., vol. 60, pp. 260-276, Jan. 2014.
9. Z. Ge and J. Chen, "Plant-wide industrial process monitoring: A distributed modeling framework," IEEE Trans. Ind. Informat., vol. 12, no. 1, pp. 310-321, Feb. 2016.
10. S. Verron, J. Li, and T. Tiplica, "Fault detection and isolation of faults in a multivariate process with Bayesian network," J. Process Control, vol. 20, no. 8, pp. 902-911, 2010.
11. H. Gharahbagheri, S. A. Imtiaz, and F. Khan, "Root cause diagnosis of process fault using KPCA and Bayesian network," Ind. Eng. Chem. Res., vol. 56, no. 8, pp. 2054-2070, 2017.
12. J. Mori, V. Mahalec, and J. Yu, "Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes," Comput. Chem. Eng., vol. 71, pp. 171-209, Dec. 2014.
13. B. Huang, "Bayesian methods for control loop monitoring and diagnosis," J. Process Control, vol. 18, no. 9, pp. 829-838, 2008.
14. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers," Mach. Learn., vol. 29, nos. 2-3, pp. 131-163, 1997.
15. S. L. Ang, H. C. Ong, and H. C. Low, "Classification using the general Bayesian network," Pertanika J. Sci. Technol., vol. 24, no. 1, pp. 205-211, 2016.
16. M. Askarian, R. Zarghami, F. Jalali-Farahani, and N. Mostoufi, "Fusion of micro-macro data for fault diagnosis of a sweetening unit using Bayesian network," Chem. Eng. Res. Des., vol. 115, pp. 325-334, Nov. 2016.
17. J. Yu and M. M. Rashid, "A novel dynamic Bayesian network-based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis," AIChE J., vol. 59, no. 7, pp. 2348-2365, 2013.
18. J. Mori and J. Yu, "Dynamic Bayesian network based networked process monitoring for fault propagation identification and root cause diagnosis of complex dynamic processes," IFAC Proc. Volumes, vol. 46, no. 32, pp. 678-683, 2013.
19. F. Qi and B. Huang, "Dynamic Bayesian approach for control loop diagnosis with underlying mode dependency," Ind. Eng. Chem. Res., vol. 49, no. 18, pp. 8613-8623, 2010.
20. D. Margaritis, "Learning Bayesian network model structure from data," Ph.D. dissertation, School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, May 2003.
21. D. M. Chickering, "Learning equivalence classes of Bayesian-network structures," J. Mach. Learn. Res., vol. 2, pp. 445-498, Feb. 2002.
22. M. F. Bari, "Bayesian network structure learning," in Proc. 4th Annu. Meeting Asian Assoc. Algorithms Comput. (AAAC), Hsinchu, Taiwan, Apr. 2011.
23. R. Gonzalez, B. Huang, and E. Lau, "Process monitoring using kernel density estimation and Bayesian networking with an industrial case study," ISA Trans., vol. 58, pp. 330-347, Sep. 2015.
24. P. Larranaga, C. M. H. Kuijpers, R. H. Murga, and Y. Yurramendi, "Learning Bayesian network structures by searching for the best ordering with genetic algorithms," IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, vol. 26, no. 4, pp. 487-493, Jul. 1996.
25. D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA, USA: MIT Press, 2009.
26. J. Zhu, Z. Ge, Z. Song, L. Zhou, and G. Chen, "Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach," J. Process Control, vol. 65, pp. 91-106, May 2018.
27. Z. Ge, Z. Song, S. X. Ding, and B. Huang, "Data mining and analytics in the process industry: The role of machine learning," IEEE Access, vol. 5, pp. 20590-20616, 2017.
28. L. Yao and Z. Ge, "Big data quality prediction in the process industry: A distributed parallel modeling framework," J. Process Control, vol. 68, pp. 1-13, Aug. 2018.
29. L. Yao and Z. Ge, "Scalable semi-supervised GMM for big data quality prediction in multimode processes," IEEE Trans. Ind. Electron., to be published, doi: 10.1109/TIE.2018.2856200.
30. L. Yao and Z. Ge, "Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application," IEEE Trans. Ind. Electron., vol. 65, no. 2, pp. 1490-1498, Feb. 2018.