Application of Continuous-Time Markov Chain Model for Repairing Machines (Case study: Asalaya Sugar Factory during the Season of 2019)

Abstract

This study aims at dealing with ContinuoustimeMarkov chain Model application on fault time of two machines (Mill troup &Boiler) an important machines in Asalaya Sugar Factory on season (January/2019–December/2019), which affiliated to Sudanese Sugar Company. The study conduces that failure time of machines follows Exponential distribution estimation fault distribution. Failure time represent transition matrix in the ContinuoustimeMarkov chain. The probability of machine in operating state is greater than probability of machine in a fail state. The high probability of the machine in operating state and the mean time of a machine stay an estimated by 4 hours in state (1) (operating state) meanwhile the machine stay in state (0) (fail state) estimated by one hour which indicates the efficiency of the maintenance unit, it is clear that, the probability of available time to repair machines when it fault approximately (0.80), this indicates that the machines has high availability. Through estimating the failure rate and repair rate andthe transition probabilitiesfrom operational state to another , it was found thatfailure rate of the machine(Mill troup) is greater than the failure rate of the machine (Boiler),the probability of both machines in working condition is high ,probability of machines (Mill troup) expose more failure, which requiresmore effort in maintenance than machine (Boiler).The probability of the overall failure rate of the machines is negligible probability for the machines,which isa good indicator as it is unlikely that both machines will fail at the same time. That means maintenance work should take place immediately for the machine that suffers a malfunction.