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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
Weibull++/ALTA offers the following utilities 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 based on various user inputs, such as distribution type, distribution parameters and sample size. The data set is then automatically placed in an analysis 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. You can use SimuMatic, for example, to find the average reliability at a given time for a thousand simulated data sets.
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