Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 3, 2016

Failure Analysis Using Functional Model and Bayesian Network

Mohamed A. Khalil, Arshad Ahmad, Tuan Amran T. Abdullah and Ali Al-shanini

Abstract

A class of functional model known as multilevel flow model (MFM) is used to represent a pilot scale heat exchanger system. MFM is effective in representing chemical process qualitatively through graphical representation, but lacks the ability to quantify the impact of successes or failures of process events, and is not able to quantitatively distinguish between steps in a goal and their contributions towards achieving the main goal. To address this issue, the MFM is converted into its equivalent fault tree (FT) model to accommodate logical sequence of events along with the needed quantifications. The FT model is then converted into Bayesian network (BN) model to facilitate updates of probabilities. Using Hugin 8.1 software, the BN model is simulated to investigate the response of the process when subjected to various faults. The results highlight the capability of the model in detecting process faults and in identifying the associated root causes, thus pointing to the potentials of the proposed strategy in modeling complex chemical processes for higher level functions in plant operations such as facilitating alarm system and fault diagnosis.

References

1. Al-Shanini A, Ahmad A, Khan F. Accident modelling and analysis in process industries. J Loss Prev Process Ind 2014;32:319–34.10.1016/j.jlp.2014.09.016Search in Google Scholar

2. Isermann R. Model-based fault-detection and diagnosis – status and applications. Ann Rev Control 2005;29:71–85.10.1016/j.arcontrol.2004.12.002Search in Google Scholar

3. Venkatasubramanian VV, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Comput Chem Eng 2005;27:293–311.10.1016/S0098-1354(02)00160-6Search in Google Scholar

4. Venkatasubramanian V, Rengaswamy R, Kavuri SN. A review of process fault detection and diagnosis: Part II: qualitative models and search strategies. Comput Chem Eng 2003b;27(3):313–26.10.1016/S0098-1354(02)00161-8Search in Google Scholar

5. Venkatasubramanian V, Rengaswamy R, Kavuri SN, et al. A review of process fault detection and diagnosis: Part III: process history based methods. Comput Chem Eng 2003c;27(3):327–46.10.1016/S0098-1354(02)00162-XSearch in Google Scholar

6. Hu J, Zhang L, Cai Z, Wang Y. An intelligent fault diagnosis system for process plant using a functional HAZOP and DBN integrated methodology. Eng Appl Artif Intell 2015;45:119–35.10.1016/j.engappai.2015.06.010Search in Google Scholar

7. Lind M. (1990). Representing Goals and Functions of Complex Systems. Technical Report 90-D-38, Technical University of Denmark.Search in Google Scholar

8. Lind M. Modeling goals and functions of complex industrial plants. Appl Artif Intell Int J 1994;8(2):259–83.10.1080/08839519408945442Search in Google Scholar

9. Lind M. Plant Modeling for Human Supervisory Control. Trans Inst Measurement Control 1999;21(4/5):171–80.10.1177/014233129902100405Search in Google Scholar

10. Lind M. Modeling Goals and Functions of Control and Safety Systems - theoretical foundations and extensions of MFM. Technical Report NKS-114, Technical University of Denmark, 2005.Search in Google Scholar

11. Rossing NL, Lind M, Jensen N, Jørgensen SB. A Functional HAZOP Methodology. Comput Chem Eng 2010;34:244–25.10.1016/j.compchemeng.2009.06.028Search in Google Scholar

12. Gofuku A, Ohara A, A Systematic Fault Tree Analysis Based on Multi-level Flow Modeling, in Progress of Nuclear Safety for Symbiosis and Sustainability, H. Yoshikawa and Z. Zhang, Editors. 2014, Springer Japan:97–103.10.1007/978-4-431-54610-8_11Search in Google Scholar

13. Ruijters E, Toelinga M. Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Comput Sci Rev 2015;15–16:29–62.10.1016/j.cosrev.2015.03.001Search in Google Scholar

14. Weber P, Medina-Oliva G, Simon C, Iung B. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng Appl Artif Intell 2012;25(4):671–82.10.1016/j.engappai.2010.06.002Search in Google Scholar

15. Liu J, Yang M, Zhang X. An Algorithm for Automatic Generation of Fault Tree from MFM Model, in Zero-Carbon Energy Kyoto 2009. 2010, Springer, 2010:243–7.Search in Google Scholar

16. Li Y-F, Mi J, Huang H-Z, Zhu S-P, Xiao N. Fault tree analysis of train rear-end collision accident considering common cause failure. Maintenance Reliability 2013;15(4):404–9.Search in Google Scholar

17. Khakzad N, Khakzad S, Khan F. Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico. Nat Hazards 2014;74(3):1759–71.10.1007/s11069-014-1271-8Search in Google Scholar

Received: 2016-1-31
Revised: 2016-4-11
Accepted: 2016-4-14
Published Online: 2016-5-3
Published in Print: 2016-12-1

©2016 by De Gruyter