The main objective of life testing is to obtain information concerning failure. This information should then be used in order to quantify reliability, improve product reliability, and to determine whether safety and reliability goals are being met. The amount of time available for testing directly at use conditions, that is, with practical test times and realistic (relatively) small test sample sizes, could be considerably less than the
component’s expected lifetime. To overcome such a problem, there is the life-testing alternative aimed at forcing components to fail by testing them at much higher than the intended application conditions. By doing this, we will get failure data that can be fitted to life distribution models. To go from the failure rate obtained at high stress to what a product or service is likely to experience at much lower stress, under use conditions, we will need additional modeling. These models are known as acceleration models. The accelerated life testing concept is such that a component, operating under predetermined (correct) levels
of increased stress, will have exactly the same failure mechanism as observed when used at normal stress levels. For example, if the time of testing is measured in cycles, then the time squeezing may only require increasing the number of cycles per unit of time. In this study, we will develop an accelerated life-testing model in which the underlying sampling distribution is the three-parameter Weibull model. We will be assuming a linear acceleration condition. An example will illustrate the application of the proposed accelerated life-testing model.
Periodical Preventive Maintenance Schedule for Minimum Cost Using Geometric Process Model in Accelerated life Testing With Exponentiated Weibull Failure Data