One-shot device test data analysis using non-parametric and semi-parametric inferential methods and applications

A one-shot device, such as an automobile airbag, electro-explosive unit or munition, is a product that can be used only once. Its actual lifetime is unobservable, rendering the corresponding reliability analysis quite challenging. In this paper, two non-parametric methodologies—maximum likelihood estimation via EM-algorithm and Nelson–Aalen based estimation are developed for identical testing environment on one-shot devices. The EM-algorithm is usually used for unobserved failure times under some specific parametric models. But, here the EM-algorithm is adopted for the number of failures in each time interval in a non-parametric manner. Next, a semi-parametric approach, based on proportional hazards assumption, is developed for nonidentical testing environments, such as under an accelerated life-test. A Monte Carlo simulation study is then carried out for evaluating the performance of the inferential methods developed here. Finally, two data sets are analyzed for illustrative purpose. •Reliability analysis for one-shot devices data.•Non-parametric and semi-parametric models are more robust.•Expectation–Maximization algorithm with iteration having closed expression is more efficient.•Statistical inference for the associated models.