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UTL Repository
Master thesis . 2018
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Análise de dados de manutenção : estimação de probabilidade de falhas

Authors: Vitorino, Inês Patrícia Canelas;

Análise de dados de manutenção : estimação de probabilidade de falhas

Abstract

O presente trabalho resulta de uma parceria entre o ISEG e a empresa PSE - Produtos e Serviços de Estatística, Lda., tendo por base a integração num projeto sob a forma de estágio. Baseia-se no desenvolvimento de modelos analíticos para a gestão da manutenção de um cliente da PSE, isto é, na análise e identificação de padrões e comportamentos de um conjunto de ativos de modo a conseguir determinar, de forma antecipada, a necessidade de serviços de manutenção. O estudo e a previsão de ocorrências de manutenção tem uma importância central para a redução de custos, a disponibilidade dos ativos e, consequentemente, a produção. Mais especificamente, o projeto prende-se com a análise de dados de manutenção na área hospitalar. Para desenvolvimento do projeto, foram disponibilizados dados de manutenção relativos ao ano de 2016, nomeadamente dados do inventário dos ativos, custos de manutenção, manutenções corretivas e preventivas que foram realizadas. O projeto foi dividido em duas fases: Preparação e exploração dos dados - com o objetivo de descrever e caracterizar estatisticamente os principais indicadores e potenciais associações na manutenção; Modelização - com o objetivo de criar um modelo que permita conjugar tanto as condições intrínsecas aos equipamentos, como o seu histórico de manutenção e intervenções e as suas condições atuais, por forma a identificar indicadores avançados de possibilidade de falha. Posteriormente haverá a implementação dos resultados, que corresponderá a implementação técnica do modelo preditivo no sistema do cliente.

The present master's thesis is the result of a partnership between ISEG and the company PSE - Produtos e Serviços de Estatística, Lda., and it was developed based on the integration of a six-month internship project. This internship subject is to develop analytical models for the management of the maintenance of one of PSE's customers by analysing and identifying patterns and behaviours of a set of assets in order to determine, in advance, the need for maintenance services. The study and prediction of maintenance needs is crucial to achieve costs reduction, assets availability and, consequently, production. More specifically, the project deals with the analysis of maintenance data in the hospital field. For the development of this project, maintenance data for the year 2016 were made available, namely data on assets inventory, maintenance costs and corrective and preventive maintenance measures that were performed. The project was divided into two parts: Setting and analysation of data - with the aim to describe and determine the main indicators and potencial associations in the maintenance; Modeling - with the aim to create a model that allows the association of the primary condition of the equipment, its maintenance history, past interventions and its current conditions, in order to identify advanced indicators of the chance of failure. Subsequently, the results will be implemented, which will correspond to the technical implementation of the predictive model in the customer system.

Mestrado em Métodos Quantitativos para a Decisão Económica e Empresarial

info:eu-repo/semantics/publishedVersion

Country
Portugal
Keywords

Assets, Faults, Ordens de serviço, Maintenance intervention, Decision trees, Manutenção preventiva, Falhas, Previsão, Preventive maintenance, Intervenções de manutenção, Manutenção corretiva, Corrective maintenance, Árvores de decisão, Ativos, Service orders, Prediction

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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