FAILURE-BASED MAINTENANCE PLANNING USING BAYESIAN NETWORKS: A CASE STUDY HYDRAULIC TURBINE

The assessment of existing infrastructures in the energy sector is of great economic importance for the world. The extension of the power generation life of hydroelectric power plants depends on logical decisions regarding the maintenance and renewal of the equipment. For this purpose, a Bayesian network (BN) has been applied to evaluate the failures in the hydraulic turbine to calculate the failure of the turbine. Forty-six nodes have been identified that will affect the operation of the system. Preventive measures have been established for failures with the highest posterior probability. By creating four different cases, failure probabilities and the change of the main fault have been calculated. How much savings could be made in each case is determined with the maintenance. This proposed framework will be guided in determining the maintenance strategies for hydroelectric power plant operators.

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[1] M. Topcu and C. T. Tugcu, “The impact of renewable energy consumption on income inequality: Evidence from developed countries,” Renew. Energy, vol. 151, pp. 1134–1140, 2020.
[2] “International Hydropower, A. Hydropower Status Report: Sector Trends and Insights; IHA: London, UK, 2018.”
[3] L. Krishnasamy, F. Khan, and M. Haddara, “Development of a risk-based maintenance (RBM) strategy for a power-generating plant,” J. Loss Prev. Process Ind., vol. 18, no. 2, pp. 69–81, 2005.
[4] B. Xu et al., “Priority analysis for risk factors of equipment in a hydraulic turbine generator unit,” J. Loss Prev. Process Ind., vol. 58, pp. 1–7, 2019.
[5] L. A. Teran et al., “Failure analysis of a run-of-the-river hydroelectric power plant,” Eng. Fail. Anal., vol. 68, pp. 87–100, 2016.
[6] B. A. Akash, R. Mamlook, and M. S. Mohsen, “Multi-criteria selection of electric power plants using analytical hierarchy process,” Electric Power Syst. Res., vol. 52, no. 1, pp. 29–35, 1999.
[7] S. Ahmad and R. M. Tahar, “Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia,” Renew. Energy, vol. 63, pp. 458–466, 2014.
[8] V. Selak et al., “Effect of fixed dose combination treatment on adherence and risk factor control among patients at high risk of cardiovascular disease: randomised controlled trial in primary care,” BMJ, vol. 348, no. may27 11, p. g3318, 2014.
[9] W. Tang, Z. Li, and Y. Tu, “Sustainability risk evaluation for large-scale hydropower projects with hybrid uncertainty,” Sustainability, vol. 10, no. 2, p. 138, 2018.
[10] E. Vassoney, A. Mammoliti Mochet, and C. Comoglio, “Use of multicriteria analysis (MCA) for sustainable hydropower planning and management,” J. Environ. Manage., vol. 196, pp. 48–55, 2017.
[11] A. Sarkar and D. K. Behera, “Development of risk based maintenance strategy for gas turbine power system,” International Journal of Advanced Research in Engineering and Apllied Sciences, vol. 1, no. 2, pp. 20–38, 2012.
[12] A. W. Dawotola, T. B. Trafalis, Z. Mustaffa, P. H. A. J. M. van Gelder, and J. K. Vrijling, “Risk-based maintenance of a cross-country petroleum pipeline system,” J. pipeline syst. eng. pract., vol. 4, no. 3, pp. 141–148, 2013.
[13] L. T. Ostrom and C. A. Wilhelmsen, Risk assessment: tools, techniques, and their applications. USA: John Wiley & Sons, 2019.
[14] J. Ren, I. Jenkinson, J. Wang, D. L. Xu, and J. B. Yang, “An offshore risk analysis method using fuzzy Bayesian network,” J. Offshore Mech. Arct. Eng. Trans. ASME, vol. 131, no. 4, p. 041101, 2009.
[15] A. El-Awady, K. Ponnambalam, T. Bennett, A. Zielinski, and A. Verzobio, “Bayesian Network approach for failure prediction of Mountain Chute dam and generating station,” in Sustainable and Safe Dams Around the World, CRC Press, 2019, pp. 2518–2530.
[16] N. Khakzad, F. Khan, and P. Amyotte, “Quantitative risk analysis of offshore drilling operations: A Bayesian approach,” Saf. Sci., vol. 57, pp. 108–117, 2013.
[17] Y. Chang, G. Chen, X. Wu, J. Ye, B. Chen, and L. Xu, “Failure probability analysis for emergency disconnect of deepwater drilling riser using Bayesian network,” J. Loss Prev. Process Ind., vol. 51, pp. 42–53, 2018.
[18] A. S. Cofıno, R. Cano, C. Sordo, and J. M. Gutierrez, “Bayesian networks for probabilistic weather prediction,” in 15th Eureopean Conference on Artificial Intelligence (ECAI), AmsterdamNetherlands, 2002.
[19] U. Petry, Y. Hundecha, M. Pahlow, and A. Schumann, “Generation of severe flood scenarios by stochastic rainfall in combination with a rainfall runoff model”, in Proceedings of the 4th International Symposium on Flood Defense, 2008, pp. 6–8.
[20] S. Hellman, A. McGovern, and M. Xue, “Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction,” in 2012 Conference on Intelligent Data Understanding, 2012.
[21] L. Garrote, M. Molina, and L. Mediero, “Probabilistic forecasts using Bayesian networks calibrated with deterministic rainfall-runoff models,” in NATO Science Series, Dordrecht: Springer Netherlands, 2007, pp. 173–183.
[22] X. Zhang, H. Zhao, Y. Xie, and Z. Yin, “Bayesian network model for fault diagnosis of hydropower equipment,” Journal-Northeastern University Natural Science, vol. 27, no. 3, 2006.
[23] S. Q. Wang, M. F. Dulaimi, and M. Y. Aguria, “Risk management framework for construction projects in developing countries,” Constr. Manage. Econ., vol. 22, no. 3, pp. 237–252, 2004.
[24] M. Yucesan, M. Gul, and E. Celik, “A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method,” Complex intell. syst., vol. 7, no. 3, pp. 1547–1564, 2021.
[25] M. Yucesan, M. Gul, and A. F. Guneri, “A Bayesian network-based approach for failure analysis in weapon industry,” J. Therm. Eng., pp. 222–229, 2021.
[26] B. Cai, H. Liu, and M. Xie, “A real-time fault diagnosis methodology of complex systems using objectoriented Bayesian networks”, Mech. Syst. Signal Process., vol. 80, pp. 31–44, 2016.
[27] H. J. P. Marvin et al., “Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment”, Nanotoxicology, vol. 11, no. 1, pp. 123–133, 2017.
[28] M. Yucesan and G. Kahraman, “Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP”, Energy Policy, vol. 126, pp. 343–351, 2019.
[29] “MSBNx.” [Online]. Available: https://www.microsoft.com/en-us/download/details.aspx?id=52299.
[30] G. Kou, Y. Peng, and G. Wang, “Evaluation of clustering algorithms for financial risk analysis using MCDM methods”, Inf. Sci. (Ny), vol. 275, pp. 1–12, 2014.
[31] S. A. Jozi, M. T. Shoshtary, and A. R. K. Zadeh, “Environmental risk assessment of dams in construction phase using a multi-criteria decision-making (MCDM) method”, Hum. Ecol. Risk Assess., vol. 21, no. 1, pp. 1–16, 2015.
[32] J. Rezaei, “Best-worst multi-criteria decision-making method”, Omega, vol. 53, pp. 49–57, 2015.
[33] E. Celik, M. Yucesan, and M. Gul, “Green supplier selection for textile industry: a case study using BWM-TODIM integration under interval type-2 fuzzy sets,” Environ. Sci. Pollut. Res. Int., vol. 28, no. 45, pp. 64793–64817, 2021.
[34] M. Gul, M. Yucesan, and E. Celik, “A modified risk prioritization approach using best–worst method”, in Industrial Ecology and Environmental Management, Cham: Springer International Publishing, 2021, pp. 53–74.