Multi State analysis of process status using multilevel flow modelling and Bayesian network
Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality...
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Online Access: | http://eprints.utm.my/id/eprint/70036/1/MohamedARKhalil2016_MultiStateAnalysisofProcessStatus.pdf http://eprints.utm.my/id/eprint/70036/ http://dx.doi.org/10.11113/jt.v78.9563 |
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my.utm.700362017-11-14T06:23:14Z http://eprints.utm.my/id/eprint/70036/ Multi State analysis of process status using multilevel flow modelling and Bayesian network Khalil, M. A. R. Ahmad, A. Abdullah, T. A. T. Al-Shatri, A. Al-Shanini, A. QA Mathematics Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/70036/1/MohamedARKhalil2016_MultiStateAnalysisofProcessStatus.pdf Khalil, M. A. R. and Ahmad, A. and Abdullah, T. A. T. and Al-Shatri, A. and Al-Shanini, A. (2016) Multi State analysis of process status using multilevel flow modelling and Bayesian network. Jurnal Teknologi, 78 (8-3). pp. 33-41. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v78.9563 DOI:10.11113/jt.v78.9563 |
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QA Mathematics Khalil, M. A. R. Ahmad, A. Abdullah, T. A. T. Al-Shatri, A. Al-Shanini, A. Multi State analysis of process status using multilevel flow modelling and Bayesian network |
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Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method. |
format |
Article |
author |
Khalil, M. A. R. Ahmad, A. Abdullah, T. A. T. Al-Shatri, A. Al-Shanini, A. |
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Khalil, M. A. R. Ahmad, A. Abdullah, T. A. T. Al-Shatri, A. Al-Shanini, A. |
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Khalil, M. A. R. |
title |
Multi State analysis of process status using multilevel flow modelling and Bayesian network |
title_short |
Multi State analysis of process status using multilevel flow modelling and Bayesian network |
title_full |
Multi State analysis of process status using multilevel flow modelling and Bayesian network |
title_fullStr |
Multi State analysis of process status using multilevel flow modelling and Bayesian network |
title_full_unstemmed |
Multi State analysis of process status using multilevel flow modelling and Bayesian network |
title_sort |
multi state analysis of process status using multilevel flow modelling and bayesian network |
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Penerbit UTM Press |
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2016 |
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http://eprints.utm.my/id/eprint/70036/1/MohamedARKhalil2016_MultiStateAnalysisofProcessStatus.pdf http://eprints.utm.my/id/eprint/70036/ http://dx.doi.org/10.11113/jt.v78.9563 |
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