Reliability Assessment of Multi-State Repairable System Subject to Minimal Repairs and Constant Demand

Effective maintenance management is essential to reduce the adverse effect of equipment failure to operation. This can be accomplished by accurately predicting the equipment failure such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. This paper presents a model to assess system reliability for a degraded multi-state system based on discrete time Markov process and continuous time Markov process. The selection of which model to use is based on the type of available data. The system degradation was quantified by discrete level of system’s performance rate with system states ranging from perfect functioning state to complete failure. At any point in time, the system can experience random failures from any degraded state upon which general repair will be performed. This research also explored a method of estimating of transition probabilities as well as definition of states for the Markov process by utilizing system performance data and data clustering method. The results proved the applicability of both discrete time Markov chain and continuous time Markov process in assessing the reliability of multi-state systems using the system’s performance data. The results are then utilized to perform equipment replacement analysis due to deterioration based on the expected demand.