Logic-based probabilistic network model to detect and track faults in a process system
Process systems are becoming complex due to a higher dependency among operational variables and complex control loops. Principal component analysis (PCA) is widely used to reduce the dimensionality of the complex process systems, while Bayesian networks (BNs) are increasingly employed to model relat...
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John Wiley and Sons Inc.
2020
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my.utp.eprints.231752021-08-19T06:10:06Z Logic-based probabilistic network model to detect and track faults in a process system Tahoon, A.I. Rusli, R. Khan, F. Zainal Abidin, M. Process systems are becoming complex due to a higher dependency among operational variables and complex control loops. Principal component analysis (PCA) is widely used to reduce the dimensionality of the complex process systems, while Bayesian networks (BNs) are increasingly employed to model relationships among the operational variables. This article integrates these two methods (BN and PCA) through a logic-based approach to study the fault conditions of a process system. A distillation pilot plant is used to test the integrated approach. The process monitoring data are analyzed using the PCA to identify the abnormality variables while the BN is developed using data-driven learning. The variable dependency in the BN nodes is learned through maximum likelihood estimation. The results of the proposed approach are compared against the logic-based full BN model. The study observes that the logic-based PCA-BN approach proposed improves the reliability of fault detection. While the logic-based full BN provides a better understanding of fault propagation path through the unit, which helps track and troubleshoot the detected faults. © 2019 American Institute of Chemical Engineers John Wiley and Sons Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075526483&doi=10.1002%2fprs.12110&partnerID=40&md5=93f53c296ce3bee15f2db7ba59c0433e Tahoon, A.I. and Rusli, R. and Khan, F. and Zainal Abidin, M. (2020) Logic-based probabilistic network model to detect and track faults in a process system. Process Safety Progress, 39 (S1). http://eprints.utp.edu.my/23175/ |
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Process systems are becoming complex due to a higher dependency among operational variables and complex control loops. Principal component analysis (PCA) is widely used to reduce the dimensionality of the complex process systems, while Bayesian networks (BNs) are increasingly employed to model relationships among the operational variables. This article integrates these two methods (BN and PCA) through a logic-based approach to study the fault conditions of a process system. A distillation pilot plant is used to test the integrated approach. The process monitoring data are analyzed using the PCA to identify the abnormality variables while the BN is developed using data-driven learning. The variable dependency in the BN nodes is learned through maximum likelihood estimation. The results of the proposed approach are compared against the logic-based full BN model. The study observes that the logic-based PCA-BN approach proposed improves the reliability of fault detection. While the logic-based full BN provides a better understanding of fault propagation path through the unit, which helps track and troubleshoot the detected faults. © 2019 American Institute of Chemical Engineers |
| format |
Article |
| author |
Tahoon, A.I. Rusli, R. Khan, F. Zainal Abidin, M. |
| spellingShingle |
Tahoon, A.I. Rusli, R. Khan, F. Zainal Abidin, M. Logic-based probabilistic network model to detect and track faults in a process system |
| author_facet |
Tahoon, A.I. Rusli, R. Khan, F. Zainal Abidin, M. |
| author_sort |
Tahoon, A.I. |
| title |
Logic-based probabilistic network model to detect and track faults in a process system |
| title_short |
Logic-based probabilistic network model to detect and track faults in a process system |
| title_full |
Logic-based probabilistic network model to detect and track faults in a process system |
| title_fullStr |
Logic-based probabilistic network model to detect and track faults in a process system |
| title_full_unstemmed |
Logic-based probabilistic network model to detect and track faults in a process system |
| title_sort |
logic-based probabilistic network model to detect and track faults in a process system |
| publisher |
John Wiley and Sons Inc. |
| publishDate |
2020 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075526483&doi=10.1002%2fprs.12110&partnerID=40&md5=93f53c296ce3bee15f2db7ba59c0433e http://eprints.utp.edu.my/23175/ |
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