Availability analysis of vertical milling centre using Markov approach and Monte Carlo simulations

• Autorzy: Kumar Girish , Mishra Ravi , Khan Tayyab

Identyfikatory

DOI

10.26408/srsp-2023-09

• Konferencja: 17th Summer Safety & Reliability Seminars - SSARS 2023, 9-14 July 2023, Kraków, Poland
• Języki publikacji: EN

Abstrakty

EN

The purpose of this article is to investigate the availability of vertical machining centre using a Markovian technique and Monte Carlo simulation (MSC). Availability is a critical performance metric for industrial systems. Conventional methodologies focus for steady-state availability evaluation of mechanical systems. The research analyses transient availability assessment for four different system configurations. Monte Carlo simulation modelling is used to compare the results and future scope is suggested to use the developed MCS based algorithms/codes for non-exponential (time dependant) failure and repair time distributions. The research also investigates the influence of active and passive redundancy on availability, indicating that for the vertical machining centre, parallel architecture with standby redundancy outperforms active load sharing. The chapter includes a sensitivity study that modifies the repair rates of the ball screw and sub-assembly to make the component selection process easier for engineers. The authors believe that this chapter will be useful to maintenance and practising engineers because it will assist them in making informed decisions about system availability, developing maintenance/replacement policies, and determining the repair level required to achieve the desired system availability.

Słowa kluczowe

EN

availability; reliability; Markov approach; Monte Carlo; redundancy

• Wydawca: Gdynia Maritime University ; Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Safety and Reliability of Systems and Processes
• Rocznik: 2023
• Tom: Summer Safety and Reliability Seminar 2023
• Strony: 117--130
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Kumar Girish

• Delhi Technological, University, India

• https://orcid.org/0000-0002-0448-8918

autor

Mishra Ravi

• Delhi Technological University, Delhi, India

• https://orcid.org/0009-0004-2524-1158

autor

Khan Tayyab

• Delhi Technological University, Delhi, India

• https://orcid.org/0000-0001-6539-7473

Bibliografia

• Ahmed, S.B., Alam, M. & Gupta, D. 1989. Performance modelling and evaluation of flexible manufacturing systems using a semi-Markov approach. International Journal of Computer Integrated Manufacturing 2(5), 275-280.
• Alexander, D.C. 2003. Application of Monte Carlo Simulation to System Reliability Analysis. Texas A&M University, 91-94.
• Attar, A., Raissi, S. & Khalili-Damghani, K. 2017. A simulation based optimization approach for free distributed repairable multi state availability redundancy allocation problems. Journal of Reliability Engineering and System Safety 157, 177-191.
• Billinton, R. & Allan, R. N. 2007. Reliability evaluation of engineering systems: concepts and techniques. 2nd Edition, Springer, New Delhi.
• Borgonovo, E., Marseguerra, M. & Zio E. 2000. A Monte Carlo methodological approach to plant availability modelling with maintenance ageing and obsolescence, Journal of Reliability Engineering and System Safety 67(1), 61-73.
• Cadini, F., Agliardi, G.I. & Zio, E. 2017. A modelling and simulation framework for reliability/availability assessment of a power transmission grid subject to cascading failure under extreme weather conditions. Journal of Applied Energy 185(1), 267-279.
• Çekyay, B. & Özekici, S. 2015. Reliability, MTTF and steady-state availability analysis of systems with exponential lifetimes. Applied Mathematical Modelling 39(1), 284-296.
• Chawla, R. & Kumar, G. 2013. Condition bases maintenance modelling for availability analysis of a repairable mechanical system. International Journal of Innovations in Engineering & Technology 2(2), 371-379.
• Garg, S., Singh, J. & Singh, D.V. 2010. Availability analysis of crank-case manufacturing in a two-wheeler automobile industry. Applied Mathematical Modelling, 34(6), 1672-1683.
• Ge, S., Xu, L., Liu, H. & Zhao, M. 2014. Reliability assessment of active distribution system using Monte Carlo simulation method. Journal of Applied Mathematics 2014(1), 421347.
• Kumar, G., Jain, V. & Gandhi, O.P. 2013. Availability analysis of a repairable mechanical system using analytical semi-Markov approach. Quality Engineering 25(2), 97-107.
• Kumar, G., Jain, V. & Soni, U. 2019. Modeling and simulation of repairable mechanical systems reliability and availability. International Journal of System Assurance Engineering and Management 10, 1221-1233.
• Lin, J.Y. & Donaghey, C.E. 1993. A Monte Carlo simulation to determine minimal cut sets and system reliability. Proceedings of Annual Reliability and Maintainability Symposium, IEEE, Atlanta, GA, USA, 246-249.
• Loganathan, M.K., Kumar, G. & Gandhi, O.P. 2016. Availability evaluation of manufacturing systems using semi-Markov model. International Journal of Computer Integrated Manufacturing 29(7), 720-735.
• Maciejewski, H. & Caban, D. 2008. Estimation of Repairable System Availability Within Fixed Time Horizon, Journal of Reliability Engineering & System Safety 93(1), 100-106.
• Okafor, C.E., Atikpakpa, A. & Okonkwo, U.C. 2016. Availability assessment of steam and gas turbine unit of a thermal power station using Markovian approach. Archives of Current Research International 6(4), 1-17.
• Sharma, R.K. & Kumar, S. 2008 Performance modelling in critical engineering systems using ram analysis. Reliability Engineering & System Safety 93(6), 913-919.
• Sharma, S.P. & Vishwakarma Y. 2014. Application of Markov process in performance analysis of feeding system of sugar industry. Journal of Industrial Mathematics 2014(5), 1-9.
• Varghese, J.P. & Kumar, G. 2014. Availability analysis with opportunistic maintenance of a two component deteriorating system. International Journal of Materials, Mechanics and Manufacturing Manufacturing 2(2), 155-160.

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).