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Sobre

Sobre

Licenciado em Engenharia Mecânica pela FEUP (1982). Master of Science (1984) em Engineering Production and Management e Doctor of Philosophy (1989) em Engineering Production and Management na área da Manutenção e Fiabilidade pela University of Birmingham (Inglaterra). Professor Auxiliar do Departamento de Engenharia Mecânica e Gestão Industrial da FEUP desde 1982, tendo estado entre o ano 2001 até 2011 numa comissão extraordinária de serviço no IPB como Professor Coordenador e onde desempenhei, entre outras, as funções de Coordenador de Departamento de Gestão Industrial e de Coordenador do Programa Erasmus da ESTIG. Tenho colaborado, desde 1990, com várias instituições (Univ. Lusíada, ISEE, Univ. Minho, Univ. Nova, ISQ) onde lecciono disciplinas na área da Gestão das Operações e dos Métodos Quantitativos com especial ênfase na Manutenção e Fiabilidade. Tenho orientado diversos trabalhos de investigação nestas áreas bem como consultoria empresarial.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Armando Leitão
  • Cargo

    Investigador Sénior
  • Desde

    01 fevereiro 2014
006
Publicações

2023

Multiobjective Evolutionary Clustering to Enhance Fault Detection in a PV System

Autores
Yamada, L; Rampazzo, P; Yamada, F; Guimarães, L; Leitão, A; Barbosa, F;

Publicação
Springer Proceedings in Mathematics and Statistics

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Resource definition and allocation for a multi-asset portfolio with heterogeneous degradation

Autores
Dias, L; Leitao, A; Guimaraes, L;

Publicação
RELIABILITY ENGINEERING & SYSTEM SAFETY

Abstract
When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.

2021

An unsupervised approach for fault diagnosis of power transformers

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

Publicação
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.

2013

A flexible decision support tool for maintenance float systems - A simulation approach

Autores
Peito, F; Pereira, G; Leitao, A; Dias, L; Oliveira, JA;

Publicação
12th International Conference on Modeling and Applied Simulation, MAS 2013, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2013

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a way to develop a flexible MFS model, for any number of machines in the workstation, spare machines and maintenance crews, using Arena simulation language. Also in this paper, some of the most common performance measures are identified, calculated and analysed. Nevertheless this paper would concentrate on the two most important performance measures in maintenance systems: system availability and maintenance total cost. As far as these two indicators are concerned, it was then quite clear that they assumed different behaviour patterns, especially when using extreme values for periodic overhauls rates. In this respect, system availability proved to be a more sensitive parameter.

2011

SIMULATION AS A DECISION SUPPORT TOOL IN MAINTENANCE FLOAT SYSTEMS - The Automatic Generation of Simulation Programs

Autores
Peito, F; Pereira, G; Leitao, A; Dias, L;

Publicação
EUROPEAN SIMULATION AND MODELLING CONFERENCE 2011

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors propose a flexible way to develop typical MFS models, for any number of machines in the workstation, spare machines and maintenance crews. Arena simulation language is used to understand a specific MFS, create the corresponding MFS model and analyze most common performance measures.

Teses
supervisionadas

2022

Development of RPA for administrative processes in a cork industry

Autor
Ana Beatriz Ferreira de Almeida

Instituição
UP-FEUP

2022

Improvement and Standardization of a Mizusumashi in a Cork Industry

Autor
Sérgio Tiago Lopes de Oliveira

Instituição
UP-FEUP

2022

Towards Industry 4.0: Visibility over Production and Maintenance in the Cork Industry

Autor
João Nuno Silva Aleixo Cardoso

Instituição
UP-FEUP

2022

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

Autor
Luís Filipe da Silva Magalhães Dias

Instituição
UP-FEUP

2022

Implementation of a Preventive Maintenance System in a Recycling Plant

Autor
Renata Nogueira Silva Moreira

Instituição
UP-FEUP