Knowledge overview

Reliability prediction

Predictions turn data, models and uncertainty into decisions.

Reliability predictions combine test and field data with suitable lifetime models while keeping assumptions and uncertainty visible.

Reliability prediction from test and field data using a lifetime model and three different operating-time profiles

Understand how the data were created

Test, operating and field data are not automatically comparable. Load, use, observation time, censoring and failure definitions shape the result.

Incomplete observations can still provide information when their context is documented.

Select models technically

Weibull, exponential and other distributions describe different failure behaviours. Selection must fit both data and mechanism.

Stress–life models are particularly sensitive because extrapolation can become invalid when the failure mechanism changes.

A defensible prognosis follows three steps: understand the data in context, select a technically appropriate model and present the extrapolation together with its uncertainty.

Three-stage diagram from data context through a reliability model to a prognosis with known data, a future event and an uncertainty band.
Data context, model and prognosis form a traceable chain; the uncertainty band shows the increasing spread beyond the known data range.

Uncertainty is part of the result

A prediction is a statement with statistical and technical uncertainty, not a certainty.

Confidence bounds, sensitivities and documented assumptions are therefore as important as the point estimate.