A dynamic dashboarding application for fleet monitoring using semantic web of things technologies

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early

Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies

A Survey on the Web of Things

The Web of Things (WoT) paradigm was proposed first in the late 2000s, with the idea of leveraging Web standards to interconnect all types of embedded devices. More than ten years later, the fragmentation of the IoT landscape has dramatically increased as a consequence of the exponential growth of connected devices, making interoperability one of the key issues for most IoT deployments. Contextually, many studies have demonstrated the applicability of Web technologies on IoT scenarios, while the joint efforts from the academia and the industry have led to the proposals of standard specifications for developing WoT systems. Through a systematic review of the literature, we provide a detailed illustration of the WoT paradigm for both researchers and newcomers, by reconstructing the temporal evolution of key concepts and the historical trends, providing an in-depth taxonomy of software architectures and enabling technologies of WoT deployments and, finally, discussing the maturity of WoT vertical markets. Moreover, we identify some future research directions that may open the way to further innovation on WoT systems

FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were detected but require too much human effort to set up. In this paper, we introduce FLAGS, the Fused-AI interpretabLe Anomaly Generation System, and combine both techniques in one methodology to overcome their limitations and optimize them based on limited user feedback. Semantic knowledge is incorporated in a machine learning technique to enhance expressivity. At the same time, feedback about the faults and anomalies that occurred is provided as input to increase adaptiveness using semantic rule mining methods. This new methodology is evaluated on a predictive maintenance case for trains. We show that our method reduces their downtime and provides more insight into frequently occurring problems. (C) 2020 The Authors. Published by Elsevier B.V

A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare

[EN]Knowledge management is one of the key priorities of many organizations. They face di erent challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a di erent context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from di erent sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain

Digitalization of Offshore Wind Farm Systems

Master's thesis in Offshore Technology: Industrial asset managementThis thesis investigates how new digital technologies and digitalization can help further evolve the offshore wind industry using the Industry 4.0 concept as a basis and explores how technologies within this concept can contribute to an offshore wind farm that overcomes some of these challenges. The study focuses on an offshore wind farm from a systems perspective, including respective modules, and where the Industry 4.0 technologies can be applied. Following this is the establishment of a systematic digitalization framework and a proposal on how to cope with increased volumes of data, connectivity, and complexity.publishedVersio

Supply Chain (micro)TMS development

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe rise of technology across many verticals has necessitated the company’s move to digitalization. Despite “XPTO” company a well know player on the retail and success on e‐commerce internal market, they aimed at the strategy of continuous innovation to drive business growth and strengthen their position as a premium brand. They decided to move forward into digitalism inside cloud based solutions to get all the advantages of microservices architecture: optimize logistics and supply chain management, speed up the workflow and maximize service efficiency. An agile organization is not achieved purely by shifting the focus from traditional functional/ technological oriented organizations. The new way to organize teams must reflect all the principles and right segregations of roles, which will be the most immediate and visible disruption and cutover from the traditional way of managing the IT. In this project we aim to use agile framework with development based in house cloud microservice solution for a (micro)TMS solution/system that address the immediate needs imposed by the market in order to use it has competitive advantage

A structured method for the optimization of the existing last mile logistic flows

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn a fast-moving world some business exists due to the interconnectivity between countries. This happens because transports are able to reach the other side of the globe within few days and without being too expensive compensating the lower costs of production and competitive advantages. This is true for well-organized and big supply chains but even them can benefit from integration with disconnected and more complex supply chain as it is the case of e-commerce chains. The transaction of small packages from online shopping required in a totally distinct country of the place of production have very specific characteristics as they are spot flows, hard to predict and to combine with other goods owing to the fact that the destination of flows are different every time and it is not always worth it to dedicate a transport for such a small goods value and in addition most times, logistics have to answer to some challenging marketing requirements meaning they have time windows to fulfil. Last mile is a big part of logistics transports and is one important part of it that can really help companies having better prices and revenues for their transports. Last mile solutions need to be easy to implement and really have to translate in quick gains to logistic companies that are largely reducing their margins to increase competitiveness. In this context, the study aims to investigate and define a method following design Research Methodology hopping to draw some innovative solutions for the problem of last mile. In this respect, the work developed intends to study the solutions already implemented and extract insights on how distribution is made and how to maximize last mile profit through the mature of an algorithm able to reduce inefficiencies in a simple way without having to wiggle too much the structure of businesses as resources of last mile service providers are understood to be scarce as many last mile companies are small sized and running under big logistic players. The solution aims to attain the different marketing requirements exactly as it was defined without having to compromise anything but still being able to make good profit margins and perhaps make room for new opportunities to arise that previously were not profitable

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency