Modelling the failure risk for water supply networks with interval-censored data

[EN] In reliability, sometimes some failures are not observed at the exact moment of the occurrence. In that case it can be more convenient to approximate them by a time interval. In this study, we have used a generalized non-linear model developed for interval-censored data to treat the life time o...

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Detalles Bibliográficos
Autores: García Mora, María Belén|||0000-0002-4112-5411, Debón, A.|||0000-0002-5116-289X, Santamaria Navarro, Cristina|||0000-0002-8124-2831, Carrión García, Andrés|||0000-0002-0953-2500
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/99988
Acceso en línea:https://riunet.upv.es/handle/10251/99988
Access Level:acceso abierto
Palabra clave:Interval-censored data
Reliability analysis
Generalized non-linear model
MATEMATICA APLICADA
ESTADISTICA E INVESTIGACION OPERATIVA
Descripción
Sumario:[EN] In reliability, sometimes some failures are not observed at the exact moment of the occurrence. In that case it can be more convenient to approximate them by a time interval. In this study, we have used a generalized non-linear model developed for interval-censored data to treat the life time of a pipe from its time of installation until its failure. The aim of this analysis was to identify those network characteristics that may affect the risk of failure and we make an exhaustive validation of this analysis. The results indicated that certain characteristics of the network negatively affected the risk of failure of the pipe: an increase in the length and pressure of the pipes, a small diameter, some materials used in the manufacture of pipes and the traffic on the street where the pipes are located. Once the model has been correctly fitted to our data, we also provided simple tables that will allow companies to easily calculate the pipe's probability of failure in a future.