Process Monitoring Based on Multivariate Causality Analysis and Probability Inference
System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex systems. This paper presents a new modeling an...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8263608/ |
| _version_ | 1819304075554979840 |
|---|---|
| author | Xiaolu Chen Jing Wang Jinglin Zhou |
| author_facet | Xiaolu Chen Jing Wang Jinglin Zhou |
| author_sort | Xiaolu Chen |
| collection | DOAJ |
| description | System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex systems. This paper presents a new modeling and monitoring framework to avoid the traditional Bayesian network disadvantage. A multivariate causal analysis method is proposed to establish a compact system structure. Combined with network parameter learning, we constructed a corresponding multivariate alarm predict graph model, in which the qualitative and quantitative relationships among the process variables are revealed distinctly. Then this model is used to accurately predict the future possible alarm events via the probability inference. Similarly, it also can be used to detect faults and find the source of the fault. The effectiveness of the proposed method is verified in public data sets and the Tenessee Eastman process. Simulation results show that the established causal relationship is completely consistent with the actual mechanism, and the alarm state of the critical variable is accurately predicted. |
| first_indexed | 2024-12-24T06:59:30Z |
| format | Article |
| id | doaj.art-0fe63458403d469bb0b371bbdba46132 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T06:59:30Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0fe63458403d469bb0b371bbdba461322022-12-21T17:09:38ZengIEEEIEEE Access2169-35362018-01-0166360636910.1109/ACCESS.2018.27955358263608Process Monitoring Based on Multivariate Causality Analysis and Probability InferenceXiaolu Chen0Jing Wang1https://orcid.org/0000-0002-6847-8452Jinglin Zhou2College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaSystem security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex systems. This paper presents a new modeling and monitoring framework to avoid the traditional Bayesian network disadvantage. A multivariate causal analysis method is proposed to establish a compact system structure. Combined with network parameter learning, we constructed a corresponding multivariate alarm predict graph model, in which the qualitative and quantitative relationships among the process variables are revealed distinctly. Then this model is used to accurately predict the future possible alarm events via the probability inference. Similarly, it also can be used to detect faults and find the source of the fault. The effectiveness of the proposed method is verified in public data sets and the Tenessee Eastman process. Simulation results show that the established causal relationship is completely consistent with the actual mechanism, and the alarm state of the critical variable is accurately predicted.https://ieeexplore.ieee.org/document/8263608/Alarm predictionmultivariate causality analysisprocess monitoring modelingparameter learning |
| spellingShingle | Xiaolu Chen Jing Wang Jinglin Zhou Process Monitoring Based on Multivariate Causality Analysis and Probability Inference IEEE Access Alarm prediction multivariate causality analysis process monitoring modeling parameter learning |
| title | Process Monitoring Based on Multivariate Causality Analysis and Probability Inference |
| title_full | Process Monitoring Based on Multivariate Causality Analysis and Probability Inference |
| title_fullStr | Process Monitoring Based on Multivariate Causality Analysis and Probability Inference |
| title_full_unstemmed | Process Monitoring Based on Multivariate Causality Analysis and Probability Inference |
| title_short | Process Monitoring Based on Multivariate Causality Analysis and Probability Inference |
| title_sort | process monitoring based on multivariate causality analysis and probability inference |
| topic | Alarm prediction multivariate causality analysis process monitoring modeling parameter learning |
| url | https://ieeexplore.ieee.org/document/8263608/ |
| work_keys_str_mv | AT xiaoluchen processmonitoringbasedonmultivariatecausalityanalysisandprobabilityinference AT jingwang processmonitoringbasedonmultivariatecausalityanalysisandprobabilityinference AT jinglinzhou processmonitoringbasedonmultivariatecausalityanalysisandprobabilityinference |