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In Reliability Analysis, coherent systems represent a most important structure. In many situations systems are arranged in a series configuration, meaning that the system's failure is determined by the first component to fail. A problem of fundamental importance is to estimate the survival function parameters for each component, which allows the specification of adequate maintainance policies. However, reliability data for series systems are usually censored, in the sense that one only has information about the first component to fail. In this work, we focus on two components series systems. We discuss and compare, via numerical experiments on simulated datasets, the performances of three estimation methods: Bayesian, Frequetist maximum likelihood and nonparametric Kaplan-Meier estimators. The results of simulation study suggest that maximum likelihood and Bayesian estimators's are roughly equivalent, while Kaplan-Meier underperforms the other two. |
