Interpretation summary for Parametric Distribution Analysis (Right Censoring)

Use a parametric distribution analysis to fit a distribution to your data and to obtain estimates that describe the reliability of your product. Parametric distribution analysis estimates the parameters of the distribution that you select.

Based on the fitted distribution, you can then do the following:
  • Display parameter estimates and distribution characteristics such as mean time to failure (MTTF)
  • Estimate percentiles and survival probabilities
  • Compare the fitted distribution to a historical distribution or compare the distributions of two or more data sets
  • Display probability, survival, cumulative failure, and hazard plots

When your product fails in different ways, use a failure mode analysis to evaluate the impact of each type of failure on the overall reliability. Each failure mode is assumed to be independent and may be modeled by different distributions. By analyzing each failure mode individually, you can more easily prioritize your improvement efforts.

Data description

Engine windings data: Single failure mode analysis

A reliability engineer studies the failure rates of engine windings of turbine assemblies to determine the times at which the windings fail. At high temperatures, the windings might decompose too fast.

The engineer records failure times for the engine windings at 80° C and 100° C. Because some units must be removed from the test before they fail, the data are right censored. The engineer performs a parametric distribution analysis using a lognormal distribution.

Dishwasher data: Multiple failure mode analysis

A household appliance manufacturer wants to improve the reliability of their dishwasher spray arms. To determine how to focus the improvement effort, engineers collect data on how and when spray arms fail.

The engineers perform a parametric analysis with multiple failure modes. They use a Weibull distribution to model spray arm breaks, and a lognormal distribution to model spray arm obstructions.

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