Planning an Availability Demonstration Test with Consideration of Confidence Level

The full service life of a technical product or system is usually not completed after an initial failure. With appropriate measures, the system can be returned to a functional state. Availability is an important parameter for evaluating such repairable systems: Failure and repair behaviors are required to determine this availability. These data are usually given as mean value distributions with a certain confidence level. Consequently, the availability value also needs to be expressed with a confidence level. This paper first highlights the bootstrap Monte Carlo simulation (BMCS) for availability demonstration and inference with confidence intervals based on limited failure and repair data. The BMCS enables point-, steady-state and average availability to be determined with a confidence level based on the pure samples or mean value distributions in combination with the corresponding sample size of failure and repair behavior. Furthermore, the method enables individual sample sizes to be used. A sample calculation of a system with Weibull-distributed failure behavior and a sample of repair times is presented. Based on the BMCS, an extended, new procedure is introduced: the “inverse bootstrap Monte Carlo simulation” (IBMCS) to be used for availability demonstration tests with consideration of confidence levels. The IBMCS provides a test plan comprising the required number of failures and repair actions that must be observed to demonstrate a certain availability value. The concept can be applied to each type of availability and can also be applied to the pure samples or distribution functions of failure and repair behavior. It does not require special types of distribution. In other words, for example, a Weibull, a lognormal or an exponential distribution can all be considered as distribution functions of failure and repair behavior. After presenting the IBMCS, a sample calculation will be carried out and the potential of the BMCS and the IBMCS investigated