This dissertation introduces a general nonparametric or "distribution-free" model to
analyze the lifetime of components under accelerated life testing. Unlike the accelerated
failure time (AFT) models, the proposed model shares the advantage of being
"distribution-free" with the proportional hazard (PH) mod~l and overcomes the deficiency
of the PH model not allowing survival curves corresponding to different values of a
covariate to cross.
The extended hazard regression (EHR) model has been successfully used in
analyzing the survival time data of non-homogeneous populations in. the medical field. It
is a general model which encompasses both the PH and the AFT models as special cases.
We investigate the EHR model and find it also appears to be flexible and useful in the
In this research, we extend and modify the EHR model using the partial likelihood
function to analyze failure data with time dependent covariates. The new model can be
easily adopted to create an accelerated life testing model with different types of stress
loading. For example, stress loading in accelerated life testing can be a step function,cyclic, or a linear function .with time. These types of stress loading reduces the testing time and increases the number of failures of components under test.
The proposed new EHR model with time dependent covariates which incorporates multiple stress loadings requires further verification. Therefore, we conduct an accelerated life test in the laboratory by subjecting components to time dependent stresses and we compare the reliability estimation based on the developed model with that obtained from the experimental results. The combination of the theoretical development of the accelerated life testing model verified by laboratory experiments offers a unique perspective to reliability model building and verification.