Autor(es)

Brito, eder S. ; Tomazella, Vera L. D. ; Ferreira, Paulo H. ; Neto, Francisco Louzada ; Gonzatto, Oilson A.

• Tipo de documento: Artigo Científico
• Fonte: QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL; v. 40, n. 7, p. 25-pg., 2024-06-29.

Resumo

Imperfect repairs (IRs) are widely applicable in reliability engineering since most equipment is not completely replaced after failure. In this sense, it is necessary to develop methodologies that can describe failure processes and predict the reliability of systems under this type of repair. One of the challenges in this context is to establish reliability models for multiple repairable systems considering unobserved heterogeneity associated with systems failure times and their failure intensity after performing IRs. Thus, in this work, frailty models are proposed to identify unobserved heterogeneity in these failure processes. In this context, we consider the arithmetic reduction of age (ARA) and arithmetic reduction of intensity (ARI) classes of IR models, with constant repair efficiency and a power-law process distribution to model failure times and a univariate Gamma distributed frailty by all systems failure times. Classical inferential methods are used to estimate the parameters and reliability predictors of systems under IRs. An extensive simulation study is carried out under different scenarios to investigate the suitability of the models and the asymptotic consistency and efficiency properties of the maximum likelihood estimators. Finally, we illustrate the practical relevance of the proposed models on two real data sets. (AU)

• Processo FAPESP: 21/04449-9 - Modelagem e análise da confiabilidade de sistemas complexos reparáveis
• Beneficiário: Vera Lucia Damasceno Tomazella
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs