Existing reliability evaluation methods rely on the availability of accurate component states data. They will become ineffective when the states themselves are uncertain or unknown, which usually happens during the early stages of the development of new systems. In such cases it is important to understand how uncertainties will affect the system reliability measures. Another drawback of current methods studying reliability of Multi-State System (MSS) is that they only considered the systems whose components have several discrete states. For those whose components have continuous states, these methods are not effective either. This paper considered the continuous distribution of components states during the approximation of Multi-State System (MSS) reliability and proposed a method to assess the reliability of this kind of system using Monte-Carlo simulation. This method will also be useful when we have no enough data to know the exact discrete states and related probability, and can only estimate components states distribution types and related parameters. Two examples were employed to illustrate the method. Comparison of the two examples shows that component state uncertainty has significant influence on the assessment of system reliability. Our effort will make the reliability approximation more realistic compared with existing methods.
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