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

In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

[1]  Biao Huang,et al.  BAYESIAN METHODS FOR CONTROL LOOP MONITORING AND DIAGNOSIS , 2008 .

[2]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[3]  Jie Yu,et al.  A novel dynamic bayesian network‐based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis , 2013 .

[4]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[5]  Kaushik Ghosh,et al.  Optimal variable selection for effective statistical process monitoring , 2014, Comput. Chem. Eng..

[6]  Zhi-huan Song,et al.  Distributed PCA Model for Plant-Wide Process Monitoring , 2013 .

[7]  Biao Huang,et al.  Dynamic Bayesian Approach for Control Loop Diagnosis with Underlying Mode Dependency , 2010 .

[8]  Steven X. Ding,et al.  Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring , 2016, IEEE Transactions on Industrial Electronics.

[9]  Zhiqiang Ge,et al.  Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach , 2017 .

[10]  Zhiqiang Ge,et al.  Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes , 2017 .

[11]  Faisal Khan,et al.  Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network , 2017 .

[12]  Pedro Larrañaga,et al.  Learning Bayesian network structures by searching for the best ordering with genetic algorithms , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Zhiqiang Ge,et al.  Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application , 2018, IEEE Transactions on Industrial Electronics.

[14]  David Maxwell Chickering,et al.  Learning Equivalence Classes of Bayesian Network Structures , 1996, UAI.

[15]  Zhiqiang Ge,et al.  Big data quality prediction in the process industry: A distributed parallel modeling framework , 2018, Journal of Process Control.

[16]  Jing Li,et al.  Fault detection and isolation of faults in a multivariate process with Bayesian network , 2010 .

[17]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[18]  Jie Yu,et al.  Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes , 2014, Comput. Chem. Eng..

[19]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[20]  Md. Faizul Bari,et al.  Bayesian Network Structure Learning , 2010 .

[21]  Biao Huang,et al.  Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method , 2016 .

[22]  Zhiqiang Ge,et al.  Scalable Semisupervised GMM for Big Data Quality Prediction in Multimode Processes , 2019, IEEE Transactions on Industrial Electronics.

[23]  Zhiqiang Ge,et al.  Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data , 2017, IEEE Transactions on Industrial Informatics.

[24]  Zhiqiang Ge,et al.  Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data , 2018, Annu. Rev. Control..

[25]  Jie Yu,et al.  Dynamic Bayesian Network Based Networked Process Monitoring for Fault Propagation Identification and Root Cause Diagnosis of Complex Dynamic Processes , 2013 .

[26]  Navid Mostoufi,et al.  Fusion of micro-macro data for fault diagnosis of a sweetening unit using Bayesian network , 2016 .

[27]  Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework , 2016, IEEE Transactions on Industrial Informatics.

[28]  D. Margaritis.  Learning Bayesian Network Model Structure from Data , 2003 .

[29]  Biao Huang,et al.  Process monitoring using kernel density estimation and Bayesian networking with an industrial case study. , 2015, ISA transactions.