<div dir="ltr">Seminário Conjunto UFSCar/ICMC - 20/09/2017 (quarta-feira) – 10:00 Local: Sala 43 do DEs-UFSCar Palestrante: William Q. Meeker, Department of Statistics, Center for Nondestructive Evaluation, Iowa State University, Ames, Iowa, USA Título: Inference Based on Data from Superpositions of Identical Renewal Processes Resumo: Maintenance data can be used to make inferences about the lifetime distribution of system components. Typically a fleet contains multiple systems. Within each system there is a set of nominally identical replaceable components of particular interest (e.g., two automobile headlights, eight DIMM modules in a computing server, sixteen cylinders in a locomotive engine). For each component replacement event, there is system-level information that a component was replaced, but not information on which particular component was replaced. Thus the observed data is a collection of superpositions of identical renewal processes (SRP), one for each system in the fleet. This paper proposes a procedure for estimating the component lifetime distribution using the aggregated event data from a fleet of systems. We show how to compute the likelihood function for the collection of SRPs and provide suggestions for efficient computations. We compare performance of this incomplete-data ML estimator with the complete-data ML estimator and study the performance of confidence interval methods for estimating quantiles of the lifetime distribution of the component. This joint work with Ye Tian (Facebook), Wei Zhang (Genetech), and Luis Escobar (Louisiana State University). Key words: Maintenance Data; Maximum likelihood; Recurrence data; Reliability; Weibull.</div>