Interpretation summary for Nonparametric Distribution Analysis (Right Censoring)

Use nonparametric distribution analysis to obtain estimates that describe the reliability of your product. Use nonparametric methods in the following situations:

When no known distribution adequately fits your data.
When you want to validate or compare the results from a parametric distribution analysis.

Note

Parametric methods provide more precise results and estimate more types of functions.

Depending on which nonparametric method you choose, you can do the following:

Display distribution characteristics such as the median time to failure
Estimate percentiles and survival probabilities
Determine whether survival curves are significantly different
Display survival 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. By analyzing each failure mode individually, you can more easily prioritize your improvement efforts.

Data description

Engine Windings Data: Single failure mode analysis

EngineWindingReliability.MTW

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 nonparametric analysis using both the Kaplan-Meier method and the actuarial method.

Dishwasher Data: Multiple failure mode analysis

DishwasherReliability.MTW

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

The engineers perform a nonparametric multiple failure mode analysis using the Kaplan-Meier method.