MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK

Authors

  • Mohamed A. R. Khalil Center of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
  • Arshad Ahmad Center of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
  • Tuan Amran Tuan Abdullah Center of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
  • Ali Al-Shatri Center of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
  • Ali Al-Shanini Department of Chemical Engineering, Hadhramout University, Mukalla, Yemen

DOI:

https://doi.org/10.11113/jt.v78.9563

Keywords:

Functional modeling, multi–state system, multilevel flow modeling, fault tree analysis, Bayesian network.

Abstract

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.

References

Ericson, C. A. 2005, Hazard Analysis Techniques for System Safety: John Wiley & Sons, Inc., 2005: 183.

Lind, M. 2011a. Nuclear Safety and Simulation. 2: 22.

Lind, M. 2011b. Advances in Safety, Reliability and Risk Management: ESREL 2011. 383.

Gofuku, A., S. Koide, N. Shimada. 2006. SICE-ICASE. International Joint Conference: IEEE, 2006: 497.

Petersen, J. 2000. Proceedings of the cognitive systems engineering in process control (CSEPC 2000). 36.

Larsson, J.E. 2002. Knowledge-Based Systems, 2002: 103.

Mi, J., Y. Li, H.-Z. Huang, Y. Liu, X. Zhang. 2012. Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE). International Conference on: IEEE, 2012: 1117.

Caldarola, L. 1980. Nuclear Engineering and Design. 58: 127.

Yi, R., K. Leixing. 2011. Reliability, Maintainability and Safety (ICRMS), 9th International Conference on, 2011: 920.

Kai, Y. 1990. Reliability Engineering & System Safety. 28: 1.

Bossche, A. 1984. Microelectronics Reliability. 24: 707.

Barlow, R. E., A. S. Wu. 1978. Mathematics of Operations Research. 3: 275.

Dahlstrand, F. 1999. Research and Development in Expert Systems XV. 173.

Khalil, M. A. A., Arshad. Abdullah, Tuan Amran T. Al-shanini, Ali. 2016. Chemical Product and Process Modeling.

Dechter, R. 1998, Learning In Graphical Models: Springer, 1998: 75.

Downloads

Published

2016-08-10

How to Cite

MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK. (2016). Jurnal Teknologi, 78(8-3). https://doi.org/10.11113/jt.v78.9563