This paper presents additive Weibull reliability model using customer complaints
data and finite element fatigue (FEA) analysis data. Warranty data provides
insight into the underlying customer issues. Reliability engineers prepare a
prediction model based on this data to forecast the failure rate of components.
However, warranty data has certain limitations with respect to prediction
modeling. The warranty period covers only the infant mortality and useful life
zone of a bathtub curve. Thus, predicting with solely warranty data generally
cannot provide results with desired accuracy.
The failure rate of wear-out components is driven by random issues initially and
wear-out or usage-related issues at the end of the lifetime. For accurate
prediction of failure rate, data need to be explored at wear-out zone of a
bathtub curve. Higher cost always limits the testing of components until
failure, but FEA fatigue analysis can provide the failure rate behavior of a
part much beyond the warranty period without physical testing.
In this work, the authors proposed an additive Weibull model, which uses both
warranty and FEA fatigue life data for predicting failure rates. Prediction
model involves two data sets of a part: one with existing warranty claims and
other with fatigue life data. Hazard rate base Weibull estimation has been used
for modeling the warranty data whereas S-N curved-based Weibull parameter
estimation is used for FEA data. To separate Weibull models’ parameters, they
are first estimated and combined to form the proposed mix Weibull model.