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Maintenance Evaluation and Optimization of a Multi-State System Based on a Dynamic Bayesian Network

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Języki publikacji
EN
Abstrakty
EN
Nowadays, the main challenge in maintenance is to establish a dynamic maintenance strategy to significantly track and improve the performance measures of multi-state systems in terms of production, quality, security and even the environment. This paper presents a quantitative approach based on Dynamic Bayesian Network (DBN) to model and evaluate the maintenance of multi-state system and their functional dependencies. According to transition relationships between the system states modeled by the Markov process, a DBN model is established. The objective is to evaluate the reliability and the availability of the system with taking into account the impact of maintenance strategies (perfect repair and imperfect repair). Using the proposed approach, the dynamic probabilities of system states can be determined and the subsystems contributing to system failure can also be identified. A practical application is demonstrated by a case study of a blower system. Through the result of the diagnostic inference, to improve the performances of the blower, the critical components C, F, W, and P should be given more attention. The results indicate also that the perfect repair strategy can improve significantly the performances of the blower, while the imperfect repair strategy cannot degrade the performances in comparison to the perfect repair strategy. These results show the effectiveness of this approach in the context of a predictive evaluation process and in providing the opportunity to evaluate the impact of the choices made on the future measurement of systems performances. Finally, through diagnostic analysis, intervention management and maintenance planning are managed efficiently and optimally.
Twórcy
  • Transport engineering and environment laboratory, University of Constantine 1, Constantine, Algeria
  • Transport engineering and environment laboratory, University of Constantine 1, Constantine, Algeria
  • University of Reims Champagne Ardenne, Reims, France
Bibliografia
  • Adjerid, S., Aggab, T., and Benazzouz, D. (2012). Performance Evaluation and Optimisation of Industrial System in a Dynamic Maintenance. American Journal of Intelligent Systems, 2, 5, 82–92. DOI: 10.5923/j.ajis.20120205.02.
  • Cai, B., Liu, Y., Zhang, Y., Fan, Q., and Yu, S. (2013). Dynamic Bayesian networks based performance evaluation of subsea blowout preventers in presence of imperfect repair. Expert Systems With Applications, 40, 18, 7544–7554. DOI: 10.1016/j.eswa.2013.07.064.
  • Cai, B., Liu, Y., Liu, Z., Tian, X., Zhang, Y., and Ji, R. (2013). Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations. Risk Analysis, 33, 7, 1293–1311. DOI: 10.1111/j.1539-6924.2012.01918.x.
  • Chang, Y., Li, J., Zhang, C., Chen, G., Li, X., Zhang, S., and Sheng, L. (2019). Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate. Energy Science & Engineering, 7, 1808–1822. DOI: 10.1002/ese3.392.
  • Iung, B., Veron, M., Suhner, M.-C., and Muller, A. (2005). Integration of maintenance strategies into prognosis process to decision-making aid on system operation. CIRP Annals, 54, 1, 5–8. DOI: 10.1016/S0007-8506(07)60037-7.
  • Kohda, T. and Cui, W. (2007). Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network. Reliability Engineering and System Safety, 92, 12, 1716–1723. DOI: 10.1016/j.ress.2006.09.012.
  • Lakehal, A., Nahal, M., and Harouz, R. (2019). Development and application of a decision making tool for fault diagnosis of turbocompressor based on Bayesian network and fault tree. Management and Production Engineering Review, 10, 2, 16–24. DOI: 10.24425/mper.2019.129565.
  • Li, X., Chen, G., Chang, Y., and Xu, C. (2019). Riskbased operation safety analysis during maintenance activities of subsea pipelines. Process Safety and Environmental Protection, 122, 247–262, Feb. DOI: 10.1016/j.psep.2018.12.006.
  • Li, Y.-F. and Peng, R. (2014). Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration. Reliability Engineering and System Safety, 127, 47–57, 2014. DOI: 10.1016/j.ress.2014.03.005.
  • Li, Z., Xu, T., Gu, J., Dong, Q., and Fu, L. (2018). Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network. Royal Society Open Science, 5, 4, 171438. DOI: 10.1098/rsos.171438.
  • Lisnianski, A., Elmakias, D., Laredo, D., and Haim, H.B. (2012). A multi-state Markov model for a short-term reliability analysis of a power generating unit. Reliability Engineering and System Safety, 98, 1, 1–6. DOI: 10.1016/j.ress.2011.10.008.
  • Liu, Y. and Huang, H.-Z. (2010). Optimal replacement policy for multi-state system under imperfect maintenance. IEEE Transactions on Reliability, 59, 3, 483–495. DOI: 10.1109/TR.2010.2051242.
  • Mahadevan, S., Zhang, R., and Smith, N. (2001). Bayesian networks for system reliability reassessment. Structural Safety, 23, 3, 231–251. DOI: 10.1016 /S0167-4730(01)00017-0.
  • Neil, M. and Marquez, D. (2012). Availability modelling of repairable systems using Bayesian networks. Engineering Applications of Artificial Intelligence, 25, 4, 698–704. DOI: 10.1016/j.engappai.2010.06.003.
  • Sheu, S.-H., Chang, C.-C., Chen, Y.-L., and Zhang Z.G. (2015). Optimal preventive maintenance and repair policies for multi-state systems. Reliability Engineering and System Safety, 140, 78–87. DOI: 10.1016/j.ress.2015.03.029.
  • Soro, I.W., Nourelfath, M., and Ait-Kadi, D. (2010). Performance evaluation of multi-state degraded systems with minimal repairs and imperfect preventive maintenance. Reliability Engineering and System Safety, 95, 2, 65–69. DOI: 10.1016/j.ress.2009.08.004.
  • Wang, S.H., Zhang, Y.H., Xu, L.Y., and Liu, H.X. (2017). Optimal condition-based maintenance decision-making method of multi-component system based on simulation. Acta Armament II, 38, 568–575.
  • Weber, P. and Jouffe, L. (2006). Complex system reliability modelling with dynamic object oriented Bayesian networks (DOOBN). Reliability Engineering and System Safety, 91, 2, 149–162. DOI: 10.1016/j.ress. 2005.03.006.
  • Weber, P., Medina-Oliva, G., Simon, C., and Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25, 4, 671–682. DOI: 10.1016/j.engappai.2010.06.002.
  • Wilson, A.G. and Huzurbazar, A.V. (2007). Bayesian networks for multilevel system reliability. Reliability Engineering and System Safety, 92, 10, 1413–1420. DOI: 10.1016/j.ress.2006.09.003.
  • Yuan, W.Z. and Xu, G.Q. (2012). Modelling of a deteriorating system with repair satisfying general distribution. Applied Mathematics and Computation, 218, 11, 6340–6350. DOI: 10.1016/j.amc.2011.12.002.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-5e994eba-0c67-4e07-ab89-5d0f16889bb0
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