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About

Hugo Ferreira is a researcher with a PhD in Informatics, in the area of artificial intelligence (automated planning and machine learning), FEUP, 2011. He has been involved in data-mining and machine learning projects in the areas of structural health monitoring (acoustic emissions), on-line recommendation (fashion industry), prediction of industrial energy consumption (ornamental stone cutting machines) and industrial maintenance (detection and prognosis). Other areas of interest include applying machine learning to collaborative processes (decision support systems, social computing, human computing and serious games).

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Details

Details

006
Publications

2019

Robust cepstral-based features for anomaly detection in ball bearings

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

Publication
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.

2018

Using metalearning for parameter tuning in neural networks

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

Publication
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

Authors
Ferreira, LL; Albano, M; Silva, J; Martinho, D; Marreiros, G; Di Orio, G; Malo, P; Ferreira, H;

Publication
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.

2012

Environmental modeling with precision navigation using ROAZ autonomous surface vehicle

Authors
Hugo Miguel Ferreira; Carlos Almeida; Alfredo Martins; José Miguel Almeida; André Dias; Guilherme Silva; Eduardo Silva

Publication
IROS 2012 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal

Abstract
The use of robotic vehicles for environmental modeling is discussed. This paper presents diverse results in autonomous marine missions with the ROAZ autonomous surface vehicle. The vehicle can perform autonomous missions while gathering marine data with high inertial and positioning precision. The underwater world is an, economical and environmental, asset that need new tools to study and preserve it. ROAZ is used in marine environment missions since it can sense and monitor the surface and underwater scenarios. Is equipped with a diverse set of sensors, cameras and underwater sonars that generate 3D environmental models. It is used for study the marine life and possible underwater wrecks that can pollute or be a danger to marine navigation. The 3D model and integration of multibeam and sidescan sonars represent a challenge in nowadays. Adding that it is important that robots can explore an area and make decisions based on their surroundings and goals. Regard that, autonomous robotic s

2011

Automatic Plan Generation and Adaptation by Observation: Supporting Complex Human Planning

Authors
Hugo Miguel Ferreira

Publication
PhD Thesisvol.0

Abstract