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Sobre

Hugo Ferreira é um investigador doutorado em Informática, na área de inteligência artificial (planeamento automatizado e aprendizagem computacional), FEUP 2011. Tem participado em projetos de extração de dados (data-mining) e de aprendizagem computacional (machine learning) nas áreas de monitorização de integridade estrutural (emissões acústicas), recomendação “on-line” (indústria da moda), a previsão do consumo de energia industrial (máquinas de corte de pedras ornamentais) e a manutenção industrial (detecção e prognóstico). Outras áreas de interesse incluem a aplicação de aprendizagem de máquina em processos colaborativos (sistemas de apoio à decisão, computação social, computação humana e jogos sérios).

Tópicos
de interesse
Detalhes

Detalhes

007
Publicações

2020

Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

Autores
de Sa, CR; Shekar, AK; Ferreira, H; Soares, C;

Publicação
Advances in Intelligent Systems and Computing - 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)

Abstract

2019

Robust cepstral-based features for anomaly detection in ball bearings

Autores
Sousa, R; Antunes, J; Coutinho, F; Silva, E; Santos, J; Ferreira, H;

Publicação
International Journal of Advanced Manufacturing Technology

Abstract
This paper proposes the linear frequency cepstral coefficients as highly discriminative features for anomaly detection in ball bearings using vibration sensor data. These features are based on cepstral analysis and are capable of encoding the patterns of a spectral magnitude profile. Incipient damages on bearings can grow rapidly under normal use resulting in vibration and harsh noise. If left undetected, this damage will worsen, leading to high maintenance costs or even injury. Multiple interferences in an industrial environment contaminate the signal, making it a challenge to correctly identify the bearings’ condition. Many studies have attempted to overcome this issue at the signal level. However, the discriminative capacity of the current vibration signal features is still vulnerable to interference, which motivates this work. In order to demonstrate the benefits of these features, we (1) show that they are computationally efficient and suitable for real-time incremental training; (2) conduct discriminative analysis by evaluating the separability performance and comparing it with the state of the art; and (3) test the robustness of the proposed features under noise interference, which is ideal for use in the harsh operating conditions of industrial machinery. The data was obtained from a laboratory workbench setting that reproduces bearing fault scenarios. Results show that the proposed features are fast, competitive when compared to state-of-the-art features, and resilient to high levels of interference. Despite the higher performance when using the quadratic model, the proposed features remain highly discriminative when used with several other discriminant function. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

2019

Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

Autores
de Sá, CR; Shekar, AK; Ferreira, HM; Soares, C;

Publicação
14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings

Abstract

2018

Using metalearning for parameter tuning in neural networks

Autores
Felix, C; Soares, C; Jorge, A; Ferreira, H;

Publicação
Lecture Notes in Computational Vision and Biomechanics

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters. © 2018, Springer International Publishing AG.

2017

A pilot for proactive maintenance in industry 4.0

Autores
Ferreira, LL; Albano, M; Silva, J; Martinho, D; Marreiros, G; Orio, GD; Maló, P; Ferreira, HM;

Publicação
IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS

Abstract
The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution-embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods- A re already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: The integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system. © 2017 IEEE.