Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project (http://www.maxbeproject.eu/), an EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monitoring systems are integrated into a platform. Also extensive laboratory tests were performed to correlate the in situ measurements to the status of the axle bearing life. With the MAXBE project concept it will be possible: to contribute to detect at an early stage axle bearing failures; to create conditions for the operational and technical integration of axle bearing monitoring and maintenance in different European railway networks; to contribute to the standardization of the requirements for the axle bearing monitoring, diagnosis and maintenance. Demonstration of the developed condition monitoring systems was performed in Portugal in the Northern Railway Line with freight and passenger traffic with a maximum speed of 220 km/h, in Belgium in a tram line and in the UK. Still within the project, a tool for optimal maintenance scheduling and a smart diagnostic tool were developed. This paper presents a synthesis of the most relevant results attained in the project. The successful of the project and the developed solutions have positive impact on the reliability, availability, maintainability and safety of rolling stock and infrastructure with main focus on the axle bearing health.

Memoria en publicidad y mercadeo

La psicología y la publicidad son dos disciplinas que desde hace algunos años se han venido complementando, la publicidad es la disciplina que se ha visto beneficiada con esta alianza, ya que el principal aporte que le hace la psicología a la publicidad es poder determinar aquellos factores o estrategias que generan en el consumidor; atención, asociación entre marca y producto, para luego almacenarse en la memoria y así generar una posible conducta de compra en el individuo. Los medios de comunicación cumplen un papel muy importante, ya que son los instrumentos utilizados para la transmisión de la información publicitaria, dependiendo del medio utilizado ya sea prensa, televisión o radio, las estrategias implementadas para dar a conocer la información deben ir dirigidas al campo perceptivo, que se utiliza para recibir la información y lograr una adecuada asociación y posterior almacenamiento en la memoria. La publicidad y el mercadeo hacen uso de una prueba de evaluación; top of mind, que muestra la jerarquía de recuerdo en la memoria de los individuos ante la mención de una categoría de servicio de un producto.

Memoria de los consumidores expuestos a la publicidad

La psicología y la publicidad son dos disciplinas que se han complementado, la publicidad se ha visto beneficiada con esta alianza, ya que el principal aporte que le hace la psicología a la publicidad es poder determinar aquellos factores o estrategias que generan en el consumidor: atención, asociación entre marca y producto, para luego almacenarse en la memoria y así generar una posible conducta de compra en el individuo. Los medios de comunicación cumplen un papel importante en la transmisión de la información publicitaria, dependiendo del medio utilizado se enfoca la información teniendo en cuenta el campo perceptivo que recibirá la información, para lograr asociación y posterior almacenamiento en la memoria. Para poder medir la jerarquía del recuerdo en la memoria de los individuos ante la mención de una categoría de servicio de un producto, la publicidad y el mercadeo hacen uso de la prueba Top of Mind.

Rol de las instituciones en el perfil emprendedor

En este documento se encontrarán los resultados de una revisión literaria realizada a una variedad de artículos que en conjunto permiten reconocer las temáticas principales que influyen en la creación de empresa. Lo anterior, con el fin de conocer el rol que tienen las instituciones educativas en la formación de estudiantes emprendedores.

Fracture Toughening and Self-Healing of Composite Laminates by Nanofibrous Mats Interleaving

Composite laminates present important advantages compared to conventional monolithic materials, mainly because for equal stiffness and strength they have a weight up to four times lower. However, due to their ply-by-ply nature, they are susceptible to delamination, whose propagation can bring the structure to a rapid catastrophic failure. In this thesis, in order to increase the service life of composite materials, two different approaches were explored: increase the intrinsic resistance of the material or confer to them the capability of self-repair. The delamination has been hindered through interleaving the composite laminates with polymeric nanofibers, which completed the hierarchical reinforcement scale of the composite. The manufacturing process for the integration of the nanofibrous mat in the laminate was optimized, resulting in an enhancement of mode I fracture toughness up to 250%. The effect of the geometrical dimensions of the nano-reinforcement on the architecture of the micro one (UD and woven laminates) was studied on mode I and II. Moreover, different polymeric materials were employed as nanofibrous reinforcement (Nylon 66 and polyvinylidene fluoride). The nano toughening mechanism was studied by micrograph analysis of the crack path and SEM analysis of the fracture surface. The fatigue behavior to the onset of the delamination and the crack growth rate for woven laminates interleaved with Nylon 66 nanofibers was investigated. Furthermore, the impact behavior of GLARE aluminum-glass epoxy laminates, toughened with Nylon 66 nanofibers was investigated. Finally, the possibility of confer to the composite material the capability of self-repair was explored. An extrinsic self-healing-system, based on core-shell nanofibers filled with a two-component epoxy system, was developed by co-electrospinning technique. The healing potential of the nano vascular system has been proved by microscope electron observation of the healing agent release as result of the vessels rupture and the crosslinking reaction was verified by thermal analysis.

Advanced electrospun nanofibrous mats for hindering delamination, improving damping and for sensing of composite laminates

Carbon Fiber Reinforced Polymers (CFRPs) display high specific mechanical properties, allowing the creation of lightweight components and products by metals replacement. To reach outstanding mechanical performances, the use of stiff thermoset matrices, like epoxy, is preferred. Laminated composites are commonly used for their ease of manipulation during object manufacturing. However, the natural anisotropic structure of laminates makes them vulnerable toward delamination. Moreover, epoxy-based CFRPs are very stiff materials, thus showing low damping capacity, which results in unwanted vibrations and structure-borne noise that may contribute to delamination triggering. Hence, searching for systems able to limit these drawbacks is of primary importance for safety reasons, as well as for economic ones. In this experimental thesis, the production and integration of innovative rubbery nanofibrous mats into CFRP laminates are presented. A smart approach, based on single-needle electrospinning of rubber-containing blends, is proposed for producing dimensionally stable rubbery nanofibers without the need for rubber crosslinking. Nano-modified laminates aim at obtaining structural composites with improved delamination resistance and enhanced damping capacity, without significantly lowering other relevant mechanical properties. The possibility of producing nanofibers nano-reinforced with graphene to be applied for reinforcing composite laminates is also investigated. Moreover, the use of piezoelectric nanofibrous mats in hybrid composite laminates for achieving self-sensing capability is presented too as a different approach to prevent the catastrophic consequences of possible structural laminate failure. Finally, an accurate, systematic, and critical study concerning tensile testing of nonwovens, using electrospun Nylon 66 random nanofibrous mats as a case study, is proposed. Nanofibers diameter and specimen geometry were investigated to thoroughly describe the nanomat tensile behaviour, also considering the polymer thermal properties, and the number of nanofibers crossings as a function of the nanofibers diameter. Stress-strain data were also analysed using a phenomenological data fitting model to interpret the tensile behaviour better.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Valutazione e gestione del rischio per i Beni Culturali: strumenti e metodologie

Il patrimonio culturale sopravvissuto fino ai giorni nostri nonostante calamità naturali ed eventi catastrofici è oggi sempre più in pericolo: gli eventi naturali, accelerati e resi ancora più distruttivi dagli effetti del cambiamento climatico, lo scoppio continuo di nuovi conflitti armati e l’inconsapevolezza con cui gli uomini sfruttano il territorio comportano un aumento dei rischi e dei possibili danni ad un patrimonio che, tuttavia, è di importanza vitale per la crescita dell’umanità. Per evitare che il patrimonio culturale venga disperso o distrutto, è necessario applicare misure di prevenzione e protezione mirate, utilizzando in maniera efficiente gli strumenti disponibili; lo scopo ultimo della prevenzione e della protezione deve essere la resilienza, che va costruita attraverso la conoscenza e l’attenta pianificazione della gestione del patrimonio. Il presente lavoro di ricerca si propone dunque di analizzare i metodi e le strategie utilizzabili per la valutazione e la gestione del rischio applicati ai beni culturali, verificando a quale livello di consapevolezza si è giunti a livello sia nazionale che internazionale, passando in rassegna le tecnologie che permettono di proteggere il patrimonio agevolando il lavoro di mitigazione del rischio e applicando un prototipo di calcolo e analisi del rischio al caso studio del Museo di Nonantola, in provincia di Modena.

Numerical prediction of the lateral displacement capacity of flexural reinforced concrete shear walls

Previous earthquakes showed that shear wall damage could lead to catastrophic failures of the reinforced concrete building. The lateral load capacity of shear walls needs to be estimated to minimize associated losses during catastrophic events; hence it is necessary to develop and validate reliable and stable numerical methods able to converge to reasonable estimations with minimum computational effort. The beam-column 1-D line element with fiber-type cross-section model is a practical option that yields results in agreement with experimental data. However, shortcomings of using this model to predict the local damage response may come from the fact that the model requires fine calibration of material properties to overcome regularization and size effects. To reduce the mesh-dependency of the numerical model, a regularization method based on the concept of post-yield energy is applied in this work to both the concrete and the steel material constitutive laws to predict the nonlinear cyclic response and failure mechanism of concrete shear walls. Different categories of wall specimens known to produce a different response under in plane cyclic loading for their varied geometric and detailing characteristics are considered in this study, namely: 1) scaled wall specimens designed according to the European seismic design code and 2) unique full-scale wall specimens detailed according to the U.S. design code to develop a ductile behavior under cyclic loading. To test the boundaries of application of the proposed method, two full-scale walls with a mixed shear-flexure response and different values of applied axial load are also considered. The results of this study show that the use of regularized constitutive models considerably enhances the response predictions capabilities of the model with regards to global force-drift response and failure mode. The simulations presented in this thesis demonstrate the proposed model to be a valuable tool for researchers and engineers.