Interpretation summary for Parametric Distribution Analysis (Arbitrary Censoring)

Use Parametric Distribution Analysis (Arbitrary Censoring) 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

Muffler Data: Single failure mode analysis


A company set the warranty of its new type of mufflers at 50,000 miles. The reliability group wants to assess the reliability of the new type of mufflers and to estimate the proportion of warranty claims they can expect. The measurement of interest is the number of miles driven on a muffler when a warranty claim is made.

The reliability group performs a parametric analysis using a Weibull distribution to describe the data.

Pump Data: Multiple failure mode analysis


The automobile manufacturer is also working with its water pump supplier to improve the overall reliability of the water pumps. To determine how to focus the improvement effort, engineering collected data on how and when water pumps fail.

The engineers perform parametric multiple failure mode analysis using lognormal distributions to model failures of bearings and of gaskets in the pumps.

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