Monte Carlo and SimuMatic

In life data analysis and accelerated life testing analysis, the reliability engineer will typically select a model to fit data obtained from testing or usage in the field. However, in some situations, it is useful to generate simulated data sets containing values that are distributed according to a specified life distribution or model. For example, simulated data could be used to:

When using reliability growth analysis, the engineer will typically fit a reliability growth model to actual data obtained from developmental testing or fielded repairable systems operating in the field. However, in some situations, it may be useful to generate simulated data sets containing values that are distributed according to a specified set of parameters. For example, simulated data could be used to:

You can use Monte Carlo simulation to produce data sets based on various user inputs, such as data type, the beta and lambda parameters of the Crow-AMSAA (NHPP) model and sample size. The software will randomly generate input variables that follow a specified probability distribution. In the case of reliability growth and repairable system data analysis, the goal is to generate failure times for systems that are assumed to have specific characteristics. Therefore, the inter-arrival times of the failures will follow a non-homogeneous Poisson process with a Weibull failure intensity (as specified in the Crow-AMSAA model).

Weibull++ offers the following utilities for generating and analyzing simulated data:

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