Reliable diagnostics using wireless sensor networks

Monitoring activities in industry may require the use of wireless sensor networks, for instance due to difficult access or hostile environment. But it is well known that this type of networks has various limitations like the amount of disposable energy. Indeed, once a sensor node exhausts its resources, it will be dropped from the network, stopping so to forward information about maybe relevant features towards the sink. This will result in broken links and data loss which impacts the diagnostic accuracy at the sink level. It is therefore important to keep the network's monitoring service as long as possible by preserving the energy held by the nodes. As packet transfer consumes the highest amount of energy comparing to other activities in the network, various topologies are usually implemented in wireless sensor networks to increase the network lifetime. In this paper, we emphasize that it is more difficult to perform a good diagnostic when data are gathered by a wireless sensor network instead of a wired one, due to broken links and data loss on the one hand, and deployed network topologies on the other hand. Three strategies are considered to reduce packet transfers: (1) sensor nodes send directly their data to the sink, (2) nodes are divided by clusters, and the cluster heads send the average of their clusters directly to the sink, and (3)averaged data are sent from cluster heads to cluster heads in a hop-by-hop mode, leading to an avalanche of averages. Their impact on the diagnostic accuracy is then evaluated. We show that the use of random forests is relevant for diagnostics when data are aggregated through the network and when sensors stop to transmit their values when their batteries are emptied. This relevance is discussed qualitatively and evaluated numerically by comparing the random forests performance to state-of-the-art PHM approaches, namely: basic bagging of decision trees, support vector machine, multinomial naive Bayes, AdaBoost, and Gradient Boosting. Finally, a way to couple the two best methods, namely the random forests and the gradient boosting, is proposed by finding the best hyperparameters of the former by using the latter

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

The State of the Art of Information Integration in Space Applications

This paper aims to present a comprehensive survey on information integration (II) in space informatics. With an ever-increasing scale and dynamics of complex space systems, II has become essential in dealing with the complexity, changes, dynamics, and uncertainties of space systems. The applications of space II (SII) require addressing some distinctive functional requirements (FRs) of heterogeneity, networking, communication, security, latency, and resilience; while limited works are available to examine recent advances of SII thoroughly. This survey helps to gain the understanding of the state of the art of SII in sense that (1) technical drivers for SII are discussed and classified; (2) existing works in space system development are analyzed in terms of their contributions to space economy, divisions, activities, and missions; (3) enabling space information technologies are explored at aspects of sensing, communication, networking, data analysis, and system integration; (4) the importance of first-time right (FTR) for implementation of a space system is emphasized, the limitations of digital twin (DT-I) as technological enablers are discussed, and a concept digital-triad (DT-II) is introduced as an information platform to overcome these limitations with a list of fundamental design principles; (5) the research challenges and opportunities are discussed to promote SII and advance space informatics in future

State of AI-based monitoring in smart manufacturing and introduction to focused section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area

IoT-teknologian hyödyntäminen sähköverkko-omaisuuden hallinnassa

Objective of this thesis is to define and assess changes in energy sector, which will directly or indirectly affect distribution grid operation and management in Finland, and to determine measurable events or variables, which enable identification and monitoring of the recognized changes. Based on assessment of the upcoming changes, possibilities for utilizing IoT technologies in management and monitoring applications of the identified changes, are assessed. In the assessment of upcoming changes, total of eight subjects were covered and microgeneration, electric vehicles and heat pumps were identified to be the most probable changes to realistically penetrate Finnish energy sector within a time scope of approximately 10 years. However, none of the assessed, changes, were found to have significant and wide-scale effects in terms of performance of Finnish distribution networks. For utilization of IoT technologies in distribution networks one application for operational grid monitoring of power quality problems derived from residential photovoltaic generation, and three cases for IoT based asset health and condition monitoring were assessed. Furthermore, requirements and architecture for data storage and analysis platform of IoT based system were discussed. From the evaluated applications condition monitoring scheme of circuit breakers was determined to be the most promising alternative.Diplomityön tavoitteena on määritellä ja arvioida energiasektoriin vaikuttavien tulevien muutosten suoria tai epäsuoria vaikutuksia jakeluverkon toimintaan ja hallintaan. Havaittujen muutosten vaikutuksista on tarkoitus tunnistaa mitattavia ilmiöitä tai suureita, jotka mahdollistavat muutosten tunnistamisen sekä seurannan. Muutosanalyysiin pohjautuen tavoitteena on tunnistaa ja arvioida mahdollisuuksia IoT-teknologian hyödyntämiseksi havaittujen muutosten aiheuttamien ongelmakohtien tai mahdollisuuksien tunnistamisessa, seurannassa sekä hallinnassa. Energiasektoriin vaikuttavien muutosten analyysissä arvoitiin kokonaisuudessaan kahdeksaa eri aihealuetta ja lopputuloksena pientuotannon, sähköautojen sekä lämpöpumppujen todettiin olevan todennäköisimmät teknologiat, jotka yleistyvät merkittävissä määrin suomalaisessa sähköverkossa seuraavan kymmenen vuoden aikana. Minkään käsitellyn muutoskohdan ei kuitenkaan todettu aiheuttavan laajamittaisia ja merkittäviä ongelmia jakeluverkon toimintaan. IoT-teknologian hyödyntämiseen jakeluverkkotoiminnassa käsiteltiin yhtä verkon käyttöön ja sähkön laatuun liittyvää sovellusta, jonka avulla hajautetun pienaurinkotuotannon vaikutuksia pystytään seuraamaan, sekä lisäksi kolmeen eri verkkokomponenttiin kohdistuvaa jatkuvan kunnon seurannan sovellusta. Tämän lisäksi IoT-järjestelmän toteuttamiseksi vaadittavalle analyysi- ja tietojärjestelmäalustalle määriteltiin rakenteellisia ja toiminnallisia tarpeita. Työssä käsitellyistä IoT-sovelluksista lupaavimmaksi todettiin katkaisijoihin kohdistuva jatkuvan kunnonhallinnan sovellus

Challenges and Barriers of Wireless Charging Technologies for Electric Vehicles

Electric vehicles could be a significant aid in lowering greenhouse gas emissions. Even though extensive study has been done on the features and traits of electric vehicles and the nature of their charging infrastructure, network modeling for electric vehicle manufacturing has been limited and unchanging. The necessity of wireless electric vehicle charging, based on magnetic resonance coupling, drove the primary aims for this review work. Herein, we examined the basic theoretical framework for wireless power transmission systems for EV charging and performed a software-in-the-loop analysis, in addition to carrying out a performance analysis of an EV charging system based on magnetic resonance. This study also covered power pad designs and created workable remedies for the following issues: (i) how power pad positioning affected the function of wireless charging systems and (ii) how to develop strategies to keep power efficiency at its highest level. Moreover, safety features of wireless charging systems, owing to interruption from foreign objects and/or living objects, were analyzed, and solutions were proposed to ensure such systems would operate as safely and optimally as possible

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio