Reliability planning · Weibull analysis
Weibull Reliability Calculator
Plan or evaluate a zero-failure reliability demonstration using R, C, β, sample size, and test exposure.
GuideRequired test plan
Enter valid target values to calculate the required test plan.
Demonstration result
Enter valid plan values to evaluate the demonstration.
Sample Size Matrix
Same R, C, and β as the current Calculator plan.
| Sample size n | Lives / sample | Total unit-lives | Achieved confidence | Rounded test lives | Confidence at rounded lives |
|---|
Enter valid Calculator plan inputs to generate the matrix.
Rounded test lives are conservatively rounded up to the next 0.1 life. The planned sample size is highlighted in maize. Converted physical exposure uses the conservatively rounded test duration.
Weibull Fit
Estimate β and characteristic life η from complete, uncensored failure-life observations.
Failure-life observations
Positive values in one consistent unit.
| Specimen | Failure life / cycles | Row action |
|---|
Paste a single Excel column into any failure-life cell. Hours, miles, cycles, or another consistent positive exposure unit are acceptable.
Ranked and transformed data
| Specimen | Failure life | Rank | Median rank F | ln(life) | ln[-ln(1-F)] |
|---|
A valid fit will populate the ranked analysis.
Weibull fit
Small failure populations can produce unstable Weibull estimates. Use the fit as an engineering estimate and prefer additional representative failures when available.
Enter at least three complete failure lives to estimate β and η.
Distribution predictions
Point estimates from the fitted β and η.
Reliability at mission life
Enter a life requirement; zero is allowed.Life at target reliability
Use a decimal between 0 and 1.B-life and MTTF values use the current Fit unit. Suspensions, censoring, and parameter confidence bounds are not included.
Complete a valid Weibull fit to calculate life metrics and distribution predictions.
Weibull probability-plot regression
Observed ranks and fitted line.
Fit scope & assumptions
This fit uses complete failure-life observations and Bernard median ranks, Fᵢ = (i − 0.3) / (n + 0.4). The Weibull probability transform is fit by ordinary least squares. The regression slope estimates β and the intercept determines characteristic life η. β and η are point estimates; parameter confidence intervals are not included.
Point-estimate scope: β and η are point estimates from a complete-failure, two-parameter Weibull probability-plot regression. Suspended/right-censored observations and parameter confidence intervals are not included.
- Two-parameter Weibull model; no location parameter
- Complete failures only; no censored or suspended observations
- Independent specimens from a common population
- Consistent exposure units; η is reported in the same unit
- R² is a linearity diagnostic, not a formal goodness-of-fit test
- This is not an MLE or censored-data analysis
Failure-mechanism caution: a single fit is most meaningful when observations represent the same underlying population and dominant failure mechanism. Mixed failure modes can distort β.
η interpretation: at t = η, cumulative failure probability is approximately 63.2% and reliability is approximately 36.8% under the fitted model.