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About

First degree in Mechanical Engineering by FEUP (1982). Master of Science (1984) in Engineering Production and Management and Doctor of Philosophy (1989) in Engineering Production and Management in the scientific area of Quality, Reliability and Maintenance by the University of Birmingham (UK). Assistent Professor of the Faculty of Engineering in the Industrial Engineering and Management Department since 1982. Between the years of 2001 until 2011 I was in an Extraordinary Service Commission in IPB (Polytechnic  Institute of Bragança) as Coordenator Professor and where, among others functions, I was the Head of the Industrial Engineering Department and the  Coordenator of the  Erasmus Program of ESTIG. Since 1990 I have been  involved with several institutions (University Lusíada, ISEE, University Minho, University Nova, ISQ) where I teach subjects in the scientific area of Operations Management and Quantitative Methods with particular relevence to Reliability and Maintenance field. I have conducted several research work in these areas as well as business consulting.

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Publications

2021

An unsupervised approach for fault diagnosis of power transformers

Authors
Dias, L; Ribeiro, M; Leitao, A; Guimaraes, L; Carvalho, L; Matos, MA; Bessa, RJ;

Publication
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

Supervised
thesis

2021

Uma Nova Abordagem na Gestão da Manutenção de Equipamentos Fitness Recorrendo a Tecnologias de Identificação

Author
Ana de Assis Duarte

Institution
UP-FEUP

2021

Definição de uma Metodologia de Manutenção para a Verificação de Tolerâncias de Equipamentos numa Unidade de Produção de Pneus

Author
José Pedro Tavares Pedro Bernardo

Institution
UP-FEUP

2021

Melhoria contínua aplicada a uma empresa de Serviços Industriais

Author
Inês da Fonseca Moreira

Institution
UP-FEUP

2021

Implementação de ferramentas de qualidade em processos industriais

Author
Maria Francisca Bernardino dos Santos Monteiro

Institution
UP-FEUP

2021

Leveraging asset management policies with analytics for multi-dependent and heterogeneous multi-asset systems

Author
Luís Filipe da Silva Magalhães Dias

Institution
UP-FEUP