748 research outputs found
Sort by
Modelos organizacionales con propósito para la transformación social: estudio de caso de Impact Hub Donostia
Full text linkEl objetivo de esta investigación es identificar y describir los elementos característicos de una organización que contribuya a la transformación social. Buscando entender, para ello, la relación entre el propósito de la organización y su modelo organizativo, así como, entre el propósito de esta y el bienestar de las personas que la constituyen.
Planteada como el estudio de caso de Impact Hub Donostia S. Coop, este trabajo se ha llevado a cabo siguiendo el método inductivo, con el objetivo de construir una narrativa lo más fiel posible a la realidad experimentada por las personas que la constituyen –sin verse condicionada por un marco teórico previo–. En este sentido, se han obtenido seis categorías o características que conforman el modelo organizacional de la cooperativa y se han enmarcado en el contexto teórico extraído deductivamente de los propios resultados empíricos. Concluyendo que la alineación del propósito individual de las personas con el propósito de la organización, así como, la sabiduría colectiva alcanzada a través de un nivel de conciencia grupal, son elementos fundamentales para transitar hacia modelos organizacionales que satisfagan mejor las necesidades de las personas
Evaluation of User Experience in Human–Robot Interaction : A Systematic Literature Review
Full text linkIndustry 4.0 has ushered in a new era of process automation, thus redefining the role of people and altering existing workplaces into unknown formats. The number of robots in the manufacturing industry has been steadily increasing for several decades and in recent years the number and variety of industries using robots have also increased. For robots to become allies in the day-to-day lives of operators, they need to provide positive and fit-for-purpose experiences through smooth and satisfying interactions. In this sense, user experience (UX) serves as the greatest link between persons and robots. Essential to the study of UX is its evaluation. Therefore, the aim of this study is to identify methodologies that evaluate the human–robot interaction (HRI) from a human-centred approach. A systematic literature review has been carried out, in which 24 articles have been identified. Among these, are 15 experimental studies, in addition to theoretical frameworks and tools. The review has provided insight into how evaluations are conducted in HRI. The results show the most evaluated factors and how they are measured considering different types of measurements: qualitative and quantitative, objective and subjective. Research gaps and future directions are correspondingly identified
A pricing model to monetize your industrial data
Full text linkData monetization has become a relevant aspect of the industrial manufacturing. Consequently, this paper proposes a theoretical framework as well as a mathematical model to price industrial data. For this purpose, three characteristics of the data were considered, i.e. 1) quality; 2) entropy and 3) value. Besides, the role of data marketplace’s players was analyzed. In order to validate the economic equation, a case study was carried out by a Spanish manufacturer
A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines
Full text linkIn the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance
Influence of the velocity on quasi-static deflections of industrial articulated robots
Full text linkThis article presents the measurement and analysis of the influence of velocity on the quasi-static deflections of industrial manipulators of three different manufacturers. Quasi-static deflection refers to the deflection of the end effector position of articulated robots during movement at low velocity along a predefined trajectory. Based on earlier reported observations by the authors, there exists a difference in the static and quasi-static deflections considering the same points along a trajectory. This work investigates this difference to assess the applicability of robotic compliance calibration at low velocities. For this assessment, the deflections of three industrial articulated robots were measured at different speeds and loads. Considering the similarity among the robot models used in this investigation, this work also elaborates on the potential influence of the measurement procedure on the measured deflections and its implications for the compliance calibration of articulated robots. For all industrial articulated robots in this investigation, the quasi-static deflections are significantly larger than the static ones but similar in trend. Additionally, the magnitude of the quasi-static deflections presents a proportional relationship to the Cartesian velocity
A review on reinforcement learning for contact-rich robotic manipulation tasks
Full text linkResearch and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain
Influence of cryogenic grinding surface on fatigue performance of carburised 27MnCr5
Full text linkAutomotive transmission components are subjected to cyclic loads and, thus, must have a reliable fatigue performance. Since fatigue cracks nucleate at the surface, it is necessary to guarantee that its surface integrity accomplishes the required specifications. Typically, those components are finished by wet grinding after carburising heat treatment. However, there is an increasing demand to reduce pollutants and hazardous lubricants in the industry, and eco-friendly finishing operations have been highly encouraged. To this end, it is necessary to understand the effect of these novel finishing processes on surface integrity and, consequently, on fatigue behaviour. This study aims to assess the surface integrity and the fatigue performance of cryo-ground surfaces of 27MnCr5 steel, extensively used in fabricating shafts and gears for gearboxes. Fatigue specimens for pure torsion tests were initially case-hardened and afterwards finished using two different cryogenic grinding conditions applying liquid N2 and, as a reference, using the conventional wet grinding process. First, the surface integrity was analysed in terms of texture, residual stresses, microstructure, and microhardness. Second, the batches of specimens were tested under pure torsion fatigue. Surface residual stress relaxation was also measured during fatigue tests. Finally, fracture surfaces were observed to identify crack initiation sites and establish correlations with the surface integrity. Specimens produced by cryogenic and conventional wet grinding did not show microstructural defects or hardness reductions in the carburised layer. All conditions induced compressive residual stresses, and they barely relaxed during fatigue tests. Compressive residual stresses induced by cryogenic grinding were 10–20% lower than those generated by conventional wet grinding. This decrease resulted in a minor reduction of the fatigue resistance (4–6%) compared to the wet grinding. Importantly, this study demonstrates that with a slight geometrical radio correction in the design of the mechanical components (around 2.2%), cryogenic grinding generates pieces with the same fatigue strength as conventional grinding. Therefore, it confirms that cryogenic cooling could be a potential solution to replace pollutant coolant/lubricant fluid in grinding operations
Improving fuzzing assessment methods through the analysis of metrics and experimental conditions
Full text linkFuzzing is nowadays one of the most widely used bug hunting techniques. By automatically generating malformed inputs, fuzzing aims to trigger unwanted behavior on its target. While fuzzing research has matured considerably in the last years, the evaluation and comparison of different fuzzing proposals remain challenging, as no standard set of metrics, data, or experimental conditions exist to allow such observation. This paper aims to fill that gap by proposing a standard set of features to allow such comparison. For that end, it first reviews the existing evaluation methods in the literature and discusses all existing metrics by evaluating seven fuzzers under identical experimental conditions. After examining the obtained results, it recommends a set of practices –particularly on the metrics to be used–, to allow proper comparison between different fuzzing proposals
Data‐Driven Low‐Frequency Oscillation Event Detection Strategy for Railway Electrification Networks
Full text linkLow-frequency oscillations (LFO) occur in railway electrification systems due to the incorporation of new trains with switching converters. As a result, the increased harmonic content can cause catenary stability problems under certain conditions. Most of the research published on this topic to date is focused on modelling the event and analysing it using frequency spectrums. However, in recent years, due to the new technologies linked to Big Data (BD) and data mining (DM), a new opportunity to study and detect LFO events by means of machine-learning (ML) methods has emerged. Trains continuously collect data from the most important catenary variables, which offers new resources for analysing this type of event. Therefore, this article presents the design and implementation of a data-driven LFO event detection strategy for AC railway network scenarios. Compared to previous investigations, a new approach to analyse and detect LFO events, based on field data and ML, is presented. To obtain the most appropriate detection approach for the context of this application, on the one hand, this investigation includes a comparison of machine-learning algorithms (support vector machine, logistic regression, random forest, k-nearest neighbours, naïve Bayes) which have been trained with real field data. On the other hand, an analysis of key parameters and features to optimize event detection is also included. Thus, the most significant result of this work is the high metric values of the solution, reaching values above 97% in accuracy and 93% in F-1 score with the random forest algorithm. In addition, the applicability and training of data-driven methods with real field data are demonstrated. This automatic detection strategy can help with speeding up and improving LFO detection tasks that used to be performed manually. Finally, it is worth mentioning that this research has been structured based on the CRISP-DM methodology, established as the de facto approach for industrial DM projects
Architecture for managing AAS-based business processes
Full text linkIndustries frequently encounter the need to orchestrate services provided by devices as business processes. These industrial business process models need to meet Industry 4.0 (I4.0) specifications to handle unpredictable scenarios in the manufacturing process. Asset Administration Shell (AAS) is considered the cornerstone of interoperability between machines and applications that compose manufacturing systems. AAS facilitates the digitization of physical things (assets) for virtual representation, turning an object into an I4.0 component. This paper investigates the usage of AAS in the context of business process orchestration and a proposal is presented based on those drawbacks. The contributions of this paper are 1) Present an architecture for the management of AAS-based business processes. 2) Introduce an AAS Submodel template that enables the description and registration of the RestServices of an asset. 3) Present a plugin for Camunda Modeler that enables the Service-Discovery mechanism from a chosen AAS repository and maps assets services into BPMN Service-Tasks. And, 4) Outline opportunities for future work between AAS and business process management systems with a primary focus on context-aware capabilities for enhancing the dynamicity of workflows