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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:
Test different warranty and maintenance strategies.
Perform risk analysis.
Obtain simulation-based confidence bounds.
Analyze probabilistic design models.
Design reliability tests.
Compare different parameter estimation methods.
Evaluate the impact of different censoring schemes.
With Weibull++ and ALTA, you can use Monte Carlo simulation to produce data sets based on various user inputs, such as distribution type, distribution parameters and sample size. To create a data set, the software uses the cdf (cumulative distribution function) of the relevant distribution or model to solve for time given an unreliability value chosen from a uniform random distribution. The process is repeated with new random unreliability values until the desired number of data points is obtained.
The following utilities are available for generating and analyzing simulated data:
The Monte Carlo utility (which comes in a Weibull++ version and an ALTA version) uses Monte Carlo simulation to generate a single data set containing values that are distributed according to a specified life distribution or user-defined model. The data set is then automatically placed in a standard folio, where it can be analyzed like any other data set.
SimuMatic (which also comes in a Weibull++ version and an ALTA version) generates a large number of data sets using Monte Carlo simulation. It then analyzes the group of data sets as a whole. For example, you can use SimuMatic to find the average reliability at a given time for a thousand simulated data sets.
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