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Monte Carlo and SimuMatic

Reliability Demonstration Test Design

Designing Reliability Tests with SimuMatic

One of the applications of SimuMatic is simulating the outcome from a particular test design that is intended to demonstrate a target reliability. You can specify various factors of the design, such as the test duration (for a time-terminated test), number of failures (for a failure-terminated test) and sample size. By running the simulations you can assess whether the planned test design can achieve the reliability target. Depending on the results, you can modify the design by adjusting these factors and repeating the simulation process—in effect, simulating a modified test design—until you arrive at a modified design that is capable of demonstrating the target reliability within the available time and sample size constraints.

Tip: SimuMatic is useful for designing tests where there will be enough failure times observed during the test to analyze the data and fit a life distribution (e.g., two or more observed failures are required for most 2-parameter distributions). To design a test that demonstrates the target reliability with minimal “allowable failures” (e.g., a zero-failure test), use the Reliability Demonstration Test Design tool.

Recommended Settings

Below are some recommended settings for using SimuMatic to design a reliability life test. The options described here can also be used for other types of what-if analyses designed to explore the life estimates that can be obtained from different types of data sets.

On the Settings tab:

If you are solving for the sample size, start with a large number of data points. You can later repeat the simulation with smaller values until you arrive at an acceptable test plan. (In ALTA's Stress-Dependent Monte Carlo utility, use the data sheet on the right side of the window to specify the number of data points for each stress level.)

Make sure an appropriate number of data sets (e.g., 1,000) has been specified in the Number of Data Sets field. Lower numbers may lead to less accurate results.

On the Censoring tab:

Note: A short censoring time may create many data sets with too few failures to estimate an underlying life distribution. These data sets will display "N/A" for the parameters on the SimuMatic folio's data sheet. This can be due to an insufficient sample size or a test termination time that is too short. As a rule of thumb (and for 2-parameter distributions) the combined sample size and test duration should be sufficient to observe three or more failures. In other words, if you have a sample size of 10 then the test duration should be greater than the product's B30 life (i.e., the time at which unreliability = 30%).

Tip: In the Weibull++ SimuMatic utility, you can also choose the Random censoring option if you wish to explore the results that can be obtained from data sets that contain certain types of uncertainty. In this type of analysis, you can use the T1, T2 and DELTA values to estimate whether you'll be able to use the data set to demonstrate a required reliability target and evaluate how much the demonstrated life varies from the intrinsic reliability.

On the Test Design tab:

Select to Calculate test plan results and enter the Target Reliability you wish to demonstrate along with the Lower 1-Sided Confidence Level at which you want to demonstrate it.

Evaluating the Results

After generating the simulated data set, click the Summary (...) icon inside the Additional Results area of the control panel to view a report of the results. The Test Planning Results area of the report displays your test design inputs (reliability requirement, confidence level and sample size) along with the following results:

If the results indicate that you will not be able to demonstrate the target reliability, or if they indicate that the current test plan uses more samples or more test time than necessary to demonstrate the target, you can keep repeating the simulation with gradual adjustments until you arrive at an optimum plan.

Note that because the results are obtained through Monte Carlo simulation, you may arrive at slightly different answers each time you run the analysis.

See ALTA SimuMatic Example for an example of designing a reliability test with SimuMatic.

 

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