A Monte-Carlo Technique for Estimating Lower Confidence Limits on System Reliability Using Pass-Fail Data

Several methods for estimating lower s-confidence limits (LCLs) for system reliability were examined using pass-fail data on the components. A new technique was used to obtain limits for selected systems. These limits were compared to those obtained by other methods. The new method was tested to verify its accuracy. Results indicate that the proposed technique is simple to understand, easy to implement, and accurate. One need only supply the component reliabilities, the number of component tests, and the desired level of s-confidence, to obtain, not only an estimated LCL of the system reliability, but also an idea of the accuracy of the estimate. Most of the other techniques are not valid in the case of zero-failures, whereas this method easily accommodates such a situation. This method is not restricted to series systems; it can easily handle parallel configurations.