One way of obtaining information about reliability of units is to accelerate their life by testing at higher levels of stress (such as increasing elevated temperatures or voltages). Predicting the lifetime of a unit at normal operating conditions based on data collected at accelerated conditions is a common objective of these tests. Different models of accelerated life testing are used for such extrapolations. Two statistical based models are widely used: parametric models which require a prior specified lifetime distribution, and nonparametric models that relax of the assumption of the life time distribution. The proportional odds model is a nonparametric model in accelerated life testing based on the odds function and show that it gives a more accurate reliability estimates than proportional hazard model. This paper will concentrate on the models of proportional odds nonparametric accelerated life test for reliability prediction.
A sample of 50 randomly selected rural and urban roads of Ramadi district were observed for asphalt pavement Distresses. Three main types of Distresses were considered; rutting, cracks and pavement separation. In addition, different other Distresses types that were observed were grouped in one category named "Other". For each road, information about the age of the pavement was recorded. Kaplan-Meier method was carried out in order to understand the Remain time before pavement deterioration as well as to compare pavement service life with respect to the type of Distress. Results of this research revealed significant differences between pavement service life corresponding to the type of Distress. Pavement service life appeared to last less than 20 months when all the mentioned types of Distresses are occurred on the road
TMF (thermomechanical fatigue) damage model devoted to prediction of the high temperature fatigue lifetime of navel copper alloy (C46400) , was proposed .This model was built on the basis of the stress –number of cycles curve responsible for damage due to interaction of high temperature and fatigue. This obtained prediction compared was very favorably with the cumulative experimental TMF results.