Stochastic renewal process model for maintenance (case study: thermal electricity generation in Sudan)

  • Authors

    • Mohammedelameen Qurashi Sudan University of Science & Technology, Faculty of Science, Department of Statistics
    • Ahamed Mohamed Abdalla Hamdi
    2016-01-16
    https://doi.org/10.14419/ijasp.v4i1.5667
  • Renewal Process, Homogeneous Poisson Process (HPP), Mean Time between Failures (MTBF), Rate of Occurrence of Failure (ROCOF), Weibull, Maintenance.

  • Abstract

    The renewal process defines as a counting process where the times between the count renewals is a random variables and their distribution is identical. In the electricity generation machines there are spare parts replaced due to damage or expired and replacement process occur repeatedly and the renewal process of here assume that times between replacements are independent random variables and it has identical probability distribution. In this paper, renewal process model has applied on the time of fault for machine in Bahri Thermal Station for electricity generation, which is belong to the National Electricity Authority in Sudan during the period (2011-2015). Through the renewal process model estimation is clear that, the failure time (renewal) for the machines follow Weibull distribution with 2-parameters and when the time trend has been tested it is clear that no trend exist which mean that the renewal process represent Homogeneous Poisson Process (HPP), and the repair rate (renewal) is occurred constantly. In addition, the findings approve that whenever the repair rate (renewal) increase the mean time between failures (MTBF) increases too and this clear in machine no (6).

  • References

    1. [1] Honag Pham (2006), System Software Reliability, the State University of New Jersey, USA, Springer.

      [2] Adamantios Mettas & Wenbiao Zhao (2005), Modelling and Analysis of Repariable System with General Repair, IEEE Annual RELIABILITY and MAINTAINABILITY Symposium.

      [3] Huairui R.Guo, Haitao Liao, Wenbiao Zhao & Adamantios Mettas (2007), an A New Stochastic Model for Systems under General Repair, IEEE Transaction on Reliability, Vol. 56. http://dx.doi.org/10.1109/TR.2006.890895.

      [4] Jan A. Van Casteren (2013), Advanced Stochastic Processes.

      [5] Richard Serfozo (2009), Basics of Applied Stochastic Processes, Springer. http://dx.doi.org/10.1007/978-3-540-89332-5.

      [6] Mario Lefebvre (2007), Applied Stochastic Processes, Springer.

      [7] Van Noortwijk, J.M. & Klatter, H.E. (2004), the use of Lifetime Distributions in Bridge Maintenance and Replacement Modeling, Computers and Structures, Vol. 82. http://dx.doi.org/10.1016/j.compstruc.2004.03.013.

      [8] Rackwitz, R. (2001), Optimizing Systematically Renewed Structures, Reliability Engineering and System Safety, Vol. 73. http://dx.doi.org/10.1016/S0951-8320(01)00050-3.

      [9] Yanez, M., Joglar, F, & Modarres, M.( 2002), Generalized Renewal Process for Analysis of Repairable systems with limited failure experience, Reliability Engineering and System Safety, Vol. 77. http://dx.doi.org/10.1016/S0951-8320(02)00044-3.

      [10] Savits, T. H. (1988), a Cost Relationship between Age and Block Replacement Policies, Journal of Applied Probability, Vol. 25. http://dx.doi.org/10.2307/3214300.

      [11] Bo Bergman (1978), Optimal Replacement under a General Failure Model, Advances in Applied Probability,Vol. 10. http://dx.doi.org/10.2307/1426944.

      [12] Van Der, J.A., Weide, S. & Van Noortwijk, J.M., (2008), Renewal Theory with Exponential and Hyperbolic Discounting. Probability in the Engineering and Informational Sciences, Vol. 22.

      [13] Aven, T. & Gaarder, S. (1987), Optimal Replacement in a Shock Model: Discrete Time, Journal of Applied Probability, Vol. 24. http://dx.doi.org/10.2307/3214082.

      [14] Huairui R. Guo, Haitao (2007), ANew Stochastic Model for Systems Under. General Repairs, IEEE TRANSACTIONS ON RELIABILITY, VOL. 56, NO. 1.

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  • Received date: 2015-12-20

    Accepted date: 2016-01-10

    Published date: 2016-01-16