Moving from One Site to Multi-Site Vulnerability Studies: Decision Analysis for Trade-off Between Risks and Benefits

Document Type : Original Research Article

Authors

1 Department of Energy, Politecnico di Milano, Milan, Italy.

2 Department of Mathematics, Politecnico di Milano, Milan, Italy

Abstract

This paper provides a vulnerability study for a gas production site. Decision tree analysis is implemented to provide risk-informed decisions on the maintenance strategies to minimize the unavailability and downtime costs. In this context, the decision analysis assesses the impact on the availability of the plant after having decided to buy some spare parts to mitigate the criticality of some equipment. This will help the decision makers to make an optimal decision before spending their budget on buying the spare parts while they can reliably forecast a maximizedavailability. The decision analysis also will move from onesite approach to the multi-site vulnerability studies. Result concludes a recommendation for the company on deciding the optimum maintenance strategies and provides a list of required spare parts to be bought in advance to reduce the associated risks and costs.

Keywords

Main Subjects

References

[1]   M.H. Faber, Statistics and probability theory: in pursuit of engineering decision support, Vol. 18., London: Springer Science & Business Media, 2012.
[2]   J.-T. Horn and B. J. Leira., "Fatigue reliability assessment of offshore wind turbines with stochastic availability," Reliability Engineering & System Safety, vol. 191, pp. 1-7, 2019.
[3]   H.L.J. Rivera-Gómez, O. Montaño-Arango, E. Hernández-Gress, J. Corona-Armenta and F. Santana-Robles, "Joint production and repair efficiency planning of a multiple deteriorating system," Flexible Services and Manufacturing Journal, vol. 31, no. 2, p. 446–471, 2018.
[4]   P. Pagani, G. Fischer, I. Farquhar, R. Skilton and M. Mittwollen, "A logistical simulation tool to quantitatively evaluate the effect of different maintenance solutions on the total maintenance downtime for fusion reactors," Fusion Engineering and Design, vol. 141, pp. 121-124, 2019.
[5]   R. Manzini, A. Regattieri, H. Pham and E. Ferrari, Maintenance for industrial systems, London: Springer Science & Business Media, 2009.
[6]   J. Moubray, Reliability-centered maintenance, Amsterdam: Industrial Press Inc., 2001.
[7]   R.A. Howard, "Information value theory," IEEE Transactions on systems science and cybernetics, vol. 2, no. 1, pp. 22-26, 1966.
[8]   D. Straub, "Value of information analysis with structural reliability methods," Structural Safety, vol. 49 , pp. 75-85, 2014.
[9]   H. Raiffa and R. Schlaifer, Applied Statistical Decision Theory, Harvard University, 1961.
[10]         M. H. DeGroot, Optimal statistical decisions, Vol. 82: ohn Wiley & Sons, 2005.
[11]         M. H. Faber and R. Rackwitz, "Sustainable decision making in civil engineering," Structural Engineering International, vol. 14, no. 3, pp. 237-242, 2004.
[12]         E. Borgonovo, V. Cappelli, F. Maccheroni and M. Marinacci, "Risk analysis and decision theory: A bridge," European Journal of Operational Research, vol. 264, no. 1, pp. 280-293, 2018.
[13]         B. Adem Esmail and D. Geneletti, "Multi‐criteria decision analysis for nature conservation: A review of 20 years of applications," Methods in Ecology and Evolution, vol. 9, no. 1, pp. 42-53, 2018.
[14]         J. G. Thornton, R. Lilford and N. Johnson, "Decision analysis in medicine," BMJ: British Medical Journal, vol. 304, no. 6834, p. 1099, 1992.
[15]         D. M. Logan, " Decision analysis in engineering-economic modeling," Energy, vol. 15, no. 7, pp. 677-696, 1990.
[16]         S. Thöns and M. H. Faber, "Assessing the value of structural health monitoring ().," ICOSSAR'13 - 11th International conference on structural safety and reliability - Safety, reliability, risk and life-cycle performance of structures and infrastructures (Proceedings), 2013.
[17]         D. Zonta, B. Glisic and S. Adriaenssens, "Value of information: impact of monitoring on decision‐making," Structural Control and Health Monitoring, vol. 21, no. 7, pp. 1043-1056, 2014.
[18]         C. Malings and M. Pozzi, "Value of Information for Spatially Distributed Systems: application to sensor placement," Reliability Engineering & System Safety, vol. 154, pp. 219-233, 2016a.
[19]         C. Malings and M. Pozzi, "Conditional entropy and value of information metrics for optimal sensing in infrastructure systems," Structural Safety, vol. 60, pp. 77-90, 2016b.
[20]         C. Malings and M. Pozzi, "Value-of-information in spatio-temporal systems: Sensor placement and scheduling," Reliability Engineering & System Safety, vol. 172, pp. 45-57, 2018.
[21]         S. R. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE transactions on systems, man, and cybernetics, vol. 21, no. 3, pp. 660-674, 1991.
[22]         D. Straub and A. Der Kiureghian, "Bayesian network enhanced with structural reliability methods: Application," Journal of Engineering Mechanics, vol. 136, no. 10, pp. 1259-1270, 2010.
[23]         J. R. Quinlan, "Induction of decision trees," Machine learning , vol. 1, no. 1, pp. 81-106, 1986.
[24]         L. Rokach and O. Maimon, "Decision trees," in Data mining and knowledge discovery handbook, Boston, MA, Springer, 2005, pp. 165-192.
[25]         E. Zio, An introduction to the basics of reliability and risk analysis. Vol. 13., London: World scientific, 2007.
International Journal of Reliability, Risk and Safety: Theory and Application
Volume 2, Issue 1
June 2019
Pages 15-21

Files

History

  • Receive Date: 19 June 2019
  • Revise Date: 24 July 2019
  • Accept Date: 29 July 2019

Share

How to cite

Statistics