103 research outputs found

    Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

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    As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

    Advanced signal processing methods for condition monitoring

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    Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

    Power Quality in Electrified Transportation Systems

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    "Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    New Transfer Learning Approach Based on a CNN for Fault Diagnosis

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    Induction motors operate in difficult environments in the industry. Monitoring the performance of motors in such circumstances is significant, which can provide a reliable operation system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning model based on a VGG-19 convolutional neural network (CNN) was implemented, which provided a quick and fast training process with higher accuracy. Thermal images with different induction motor conditions were captured with the help of an FLIR camera and applied as inputs to investigate the proposed model. The implementation of this task involved the use of a VGG-19 CNN-based pre-trained network, which provides autonomous features learning based on minimum human intervention. Next, a dense-connected classifier was applied to predict the true class. The experimental results confirmed the robustness and reliability of the developed technique, which was successfully able to classify the induction motor faults, achieving a classification accuracy of 99.8%. The use of a VGG-19 network allowed the attributes to be automatically extracted and associated with the decision-making part. Furthermore, this model was further compared with other applications based on related topics; it successfully proved its superiority and robustness

    EVOLUTION OF THE SUBCONTINENTAL LITHOSPHERE DURING MESOZOIC TETHYAN RIFTING: CONSTRAINTS FROM THE EXTERNAL LIGURIAN MANTLE SECTION (NORTHERN APENNINE, ITALY)

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    Our study is focussed on mantle bodies from the External Ligurian ophiolites, within the Monte Gavi and Monte Sant'Agostino areas. Here, two distinct pyroxenite-bearing mantle sections were recognized, mainly based on their plagioclase-facies evolution. The Monte Gavi mantle section is nearly undeformed and records reactive melt infiltration under plagioclase-facies conditions. This process involved both peridotites (clinopyroxene-poor lherzolites) and enclosed spinel pyroxenite layers, and occurred at 0.7–0.8 GPa. In the Monte Gavi peridotites and pyroxenites, the spinel-facies clinopyroxene was replaced by Ca-rich plagioclase and new orthopyroxene, typically associated with secondary clinopyroxene. The reactive melt migration caused increase of TiO2 contents in relict clinopyroxene and spinel, with the latter also recording a Cr2O3 increase. In the Monte Gavi peridotites and pyroxenites, geothermometers based on slowly diffusing elements (REE and Y) record high temperature conditions (1200-1250 °C) related to the melt infiltration event, followed by subsolidus cooling until ca. 900°C. The Monte Sant'Agostino mantle section is characterized by widespread ductile shearing with no evidence of melt infiltration. The deformation recorded by the Monte Sant'Agostino peridotites (clinopyroxene-rich lherzolites) occurred at 750–800 °C and 0.3–0.6 GPa, leading to protomylonitic to ultramylonitic textures with extreme grain size reduction (10–50 μm). Compared to the peridotites, the enclosed pyroxenite layers gave higher temperature-pressure estimates for the plagioclase-facies re-equilibration (870–930 °C and 0.8–0.9 GPa). We propose that the earlier plagioclase crystallization in the pyroxenites enhanced strain localization and formation of mylonite shear zones in the entire mantle section. We subdivide the subcontinental mantle section from the External Ligurian ophiolites into three distinct domains, developed in response to the rifting evolution that ultimately formed a Middle Jurassic ocean-continent transition: (1) a spinel tectonite domain, characterized by subsolidus static formation of plagioclase, i.e. the Suvero mantle section (Hidas et al., 2020), (2) a plagioclase mylonite domain experiencing melt-absent deformation and (3) a nearly undeformed domain that underwent reactive melt infiltration under plagioclase-facies conditions, exemplified by the the Monte Sant'Agostino and the Monte Gavi mantle sections, respectively. We relate mantle domains (1) and (2) to a rifting-driven uplift in the late Triassic accommodated by large-scale shear zones consisting of anhydrous plagioclase mylonites. Hidas K., Borghini G., Tommasi A., Zanetti A. & Rampone E. 2021. Interplay between melt infiltration and deformation in the deep lithospheric mantle (External Liguride ophiolite, North Italy). Lithos 380-381, 105855

    Development of a multi-objective optimization algorithm based on lichtenberg figures

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    This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

    Impact of Etna’s volcanic emission on major ions and trace elements composition of the atmospheric deposition

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    Mt. Etna, on the eastern coast of Sicily (Italy), is one of the most active volcanoes on the planet and it is widely recognized as a big source of volcanic gases (e.g., CO2 and SO2), halogens, and a lot of trace elements, to the atmosphere in the Mediterranean region. Especially during eruptive periods, Etna’s emissions can be dispersed over long distances and cover wide areas. A group of trace elements has been recently brought to attention for their possible environmental and human health impacts, the Technology-critical elements. The current knowledge about their geochemical cycles is still scarce, nevertheless, recent studies (Brugnone et al., 2020) evidenced a contribution from the volcanic activity for some of them (Te, Tl, and REE). In 2021, in the framework of the research project “Pianeta Dinamico”, by INGV, a network of 10 bulk collectors was implemented to collect, monthly, atmospheric deposition samples. Four of these collectors are located on the flanks of Mt. Etna, other two are in the urban area of Catania and three are in the industrial area of Priolo, all most of the time downwind of the main craters. The last one, close to Cesarò (Nebrodi Regional Park), represents the regional background. The research aims to produce a database on major ions and trace element compositions of the bulk deposition and here we report the values of the main physical-chemical parameters and the deposition fluxes of major ions and trace elements from the first year of research. The pH ranged from 3.1 to 7.7, with a mean value of 5.6, in samples from the Etna area, while it ranged between 5.2 and 7.6, with a mean value of 6.4, in samples from the other study areas. The EC showed values ranging from 5 to 1032 μS cm-1, with a mean value of 65 μS cm-1. The most abundant ions were Cl- and SO42- for anions, Na+ and Ca+ for cations, whose mean deposition fluxes, considering all sampling sites, were 16.6, 6.8, 8.4, and 6.0 mg m-2 d, respectively. The highest deposition fluxes of volcanic refractory elements, such as Al, Fe, and Ti, were measured in the Etna’s sites, with mean values of 948, 464, and 34.3 μg m-2 d-1, respectively, higher than those detected in the other sampling sites, further away from the volcanic source (26.2, 12.4, 0.5 μg m-2 d-1, respectively). The same trend was also observed for volatile elements of prevailing volcanic origin, such as Tl (0.49 μg m-2 d-1), Te (0.07 μg m-2 d-1), As (0.95 μg m-2 d-1), Se (1.92 μg m-2 d-1), and Cd (0.39 μg m-2 d-1). Our preliminary results show that, close to a volcanic area, volcanic emissions must be considered among the major contributors of ions and trace elements to the atmosphere. Their deposition may significantly impact the pedosphere, hydrosphere, and biosphere and directly or indirectly human health
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