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Centre of Technology and Systems
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A Review of the Power Converter Interfaces for Switched Reluctance Machines
Publication . Pires, Vitor Fernão; Pires, Armando José; Cordeiro, Armando; Foito, Daniel; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; MDPI - Multidisciplinary Digital Publishing Institute
The use of power electronic converters is essential for the operation of Switched Reluctance Machines (SRMs). Many topologies and structures have been developed over the last years considering several specific applications for this kind of machine, improving the control strategies, performance range, fault-tolerant operation, among other aspects. Thus, due to the great importance of power electronic converters in such applications, this paper is focused on a detailed review of main structures and topologies for SRM drives. The proposed study is not limited to the classic two-level power converters topologies dedicated to the SRMs; it also presents a review about recent approaches, such as multilevel topologies and based on impedance source network. Moreover, this review is also focused on a new class of topologies associated to these machines, namely the ones with fault-tolerant capability. This new category of topologies has been a topic of research in recent years, being currently considered an area of great interest for future research work. An analysis, taking into consideration the main features of each structure and topology, was addressed in this review. A classification and comparison of the several structures and topologies for each kind of converter, considering modularity, boost capability, number of necessary switches and phases, integration in the machine design, control complexity, available voltage levels and fault-tolerant capability to different failure modes, is also presented. In this way, this review also includes a description of the presented solutions taking into consideration the reliability of the SRM drive.
An indium-oxide electrode with discontinuous Au layers for plasmonic devices
Publication . Vygranenko, Yuri; Lavareda, Guilherme; André, Vânia; Brogueira, Pedro; Amaral, Ana; Fernandes, Miguel; Fantoni, Alessandro; Vieira, M.; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DCM - Departamento de Ciência dos Materiais
In this contribution we report on a low cost plasmonic electrode for light-sensing applications. The electrode combines a conducting nonstoichiometric indium oxide (InOx) layer with an ultrathin (∼5 nm) discontinuous Au layer. The InOx and Au layers were deposited on glass substrates by plasma enhanced reactive thermal evaporation and thermal evaporation, respectively. Several device configurations with one or two Au layer(s) sandwiched between InOx layers were fabricated and characterized. The morphological and structural properties of both Au and InOx layers were analyzed using AFM and XRD techniques. In particular, the effect of thermal annealing (673 K, 15 min) on the surface morphology of Au layers grown on bare glass and InOx-coated substrate was investigated. It has been also found that the oxide film grown above an underlying nanostructured Au layer is amorphous, while a reference InOx film on glass is nanocrystalline with a smooth surface. The electrical properties of InOx grown on the Au surface are worsened due to Au-induced structural disorder. The observed difference in transmission spectra of the glass/InOx/Au and glass/Au/InOx structures indicates the difference in the morphology of the metal layer. Thus, the optical and morphological properties of the InOxelectrode can be varied in a wide range by incorporating several Au layers.
A hybrid deep learning-based approach for rolling bearing fault prognostics
Publication . Neto, Domício; Petrella, Lorena; Henriques, Jorge; Gil, Paulo; Cardoso, Alberto; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DEE - Departamento de Engenharia Electrotécnica e de Computadores; CTS - Centro de Tecnologia e Sistemas
Predictive Maintenance (PdM) has the potential to revolutionize the industry by providing advanced techniques to assess the condition of an industrial system and yield key information that can help optimize maintenance planning and prevent unexpected faults and breakdowns. Nevertheless, PdM is far from being universally applied and it is still the subject of increasing research. Thus, developing new approaches has great relevance to help PdM become a practical reality for the industry. PdM can also bring benefits in terms of sustainability, by reducing human and material resources waste, which is one of the main objectives of Circular Manufacturing initiatives. In this context, rolling bearings are one of the most studied components, as most industrial systems with rotating mechanisms contain bearings, which are prone to a number of faults caused by natural and unnatural wear. In this work, an hybrid Deep Learning (DL) approach is proposed, combining a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network to predict Remaining Useful Life (RUL) using rolling bearing vibration data preprocessed with the Short-Time Fourier Transform (STFT). This model was trained and validated using the PRONOSTIA public dataset, which is a popular benchmark for rolling bearing prognostics. The obtained results are satisfactory, providing RUL estimates close to the true values in most test cases, proving the competitiveness of the approach and its potential.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UID/EEA/00066/2019