RELIABILITY ASSESSMENT OF A MULTI-STATE SYSTEM USING DISCRETE TIME MARKOV CHAIN

Reliability assessment of a working system is perfunned to identuy the most likely fuLiures and time-to-fuilure so that appropriate actions can be planned to diminish the effucts of the fuilures. Traditional model of reliability assessment assumes every working system would P.ave two states wP.ich are the st.ate du.ring perfectly workiP.g and the complete fuilure state. However, systems that exhibit multi-state behavior rr.ay have 1iPite number of fuilure rate V>il.~h are called multi-state system (MSS). The MSS system will degrade from a perfect working system to certain minor fuilure states befure it complete~' .taiL Hence, new approach is needed to predict tire reliability of such system. This report contains the selected MSS analysis as the new approach to assess the reliabiL;cy of a MSS system and the findings about the selected method which is Discrete-Time Markov Chain (DTMC) analysis. The data was taken from UTP Gas District CooliP.g (GDC) production report fOcusing on the perfunnance of a gas turbine in terms of kW. The perfunnance data was clustered into some perfunnance states and the state transition probabilities were estimated. From tlte estimation, reliability function and distribution parameter were obtained to be used to calculate tlte Mean Time Between Failure (M1BF) of the system. At the end of the project, the reliability of the MSS that predicted using DTMC analysis was compared to the reliability predicted using traditioml method wpjch was the exponential distribution method. The analysis shows that DTMC analysis has better prediction th.an the exponential distdbutjon method. Moreover, by exploiting the state transition probabilities estimation process, the change of operation demand as well as Preventive Maintenance planning could be included in the analysis. Briefly, MSS analysis gave better reliability prediction of the MSS and the behavior of the system could be analyzed