ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

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

Prognostics of Ball Bearings in Cooling Fans

Ball bearings have been used to support rotating shafts in machines such as wind turbines, aircraft engines, and desktop computer fans. There has been extensive research in the areas of condition monitoring, diagnostics, and prognostics of ball bearings. As the identification of ball bearing defects by inspection interrupts the operation of rotating machines and can be costly, the assessment of the health of ball bearings relies on the use of condition monitoring techniques. Fault detection and life prediction methods have been developed to improve condition-based maintenance and product qualification. However, intermittent and catastrophic system failures due to bearing problems still occur resulting in loss of life and increase of maintenance and warranty costs. Inaccurate life prediction of ball bearings is of concern to industry. This research focuses on prognostics of ball bearings based on vibration and acoustic emission analysis to provide early warning of failure and predict life in advance. The failure mechanisms of ball bearings in cooling fans are identified and failure precursors associated with the defects are determined. A prognostic method based on Bayesian Monte Carlo method and sequential probability ratio test is developed to predict time-to-failure of ball bearings in advance. A benchmark study is presented to demonstrate the application of the developed prognostic method to desktop computer fans. The prognostic method developed in this research can be extended as a general method to predict life of a component or system

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

Soft Sensor-based Servo Press Monitoring

The force that a servo press exerts forming a workpiece is one the most important magnitudes in any metal forming operation. The process force, along with the characteristics of the die, is what shapes the workpiece. When the process force is greater than the maximum force for which the servo press was designed, the servo press integrity can be damaged. Therefore, the knowledge of the process force is of great interest for both, press manufacturers and users. As such, the metal forming sector is seeking systems that can monitor the process force and the operation of the servo press to analyse process’s performance and predict future deviations in the forming operation. Servo press users want to guarantee the quality of the formed parts and reduce facility downtimes due to malfunctions of the press. This dissertation addressed the monitoring of the process force and the dynamic performance of a servo press based on a model based statistical signal processing algorithm known as the dual particle filter (dPF). Initially both, the developed model of a servo press and the proposed dPF, have been experimentally evaluated and validated in a reduced scale test bench. The test bench has been designed and manufactured based on a design methodology that allows to replicate the kinematic and dynamic behaviour of different servo press facilities in the same test bench. The experimental validation has been also carried out in an industrial servo press under three different metal forming processes. The estimation results have proved the ability of the dPF to track the process force throughout the evaluated processes, obtaining a deviation lower than 5% with respect to the measured force signals at the maximum force position. The dPF algorithm has been accelerated by means of a field programmable gate array (FPGA) to achieve a real time estimation.Serbo prentsa batek pieza gordin bat eraldatzeko egindako prozesuko indarra edozein konformatu eragiketako magnitude garrantzitsuenetarikoa da. Prozesuko indarra da, trokelaren ezaugarriekin batera, pieza gordina eraldatzen duena. Prozesuko indarra prentsak diseinuaren arabera jasan dezakeena baino handiagoa bada, prentsak kalteak izan ditzake bere osotasunean. Beraz, prozesuko indarraren ezagutza interes handikoa da, prentsa egileentzat zein erabiltzaileentzat. Hori dela eta, metal eraldatzearen sektoreak prozesuko indarra eta prentsa beraren funtzionamendua monitoriza ditzaketen sistemen bila diardute, prentsaren jarduera aztertu eta eraldatzeko operazioetan etorkizunean izan daitezkeen desbideraketak aurreikusteko. Prentsa erabiltzaileek fabrikatutako piezen kalitatea bermatzea eta funtzionamendu akatsengatiko prentsaren geldialdiak murriztea bilatzen dute. Tesi honek servo prentsa baten prozesuko indarra eta portarea dinamikoaren monitorizazioa jorratzen ditu, dual particle filter (dPF) izeneko modeloetan oinarritutako seinalaren prozesamendu estadistikoko algoritmo baten bitartez. Lehenik eta behin, garatutako servo prentsaren modeloa eta proposatutako dPFa eskalatutako entsegutarako banku batean ebaluatu eta balioztatu dira. Eskalatutako entsegutarako bankua serbo prentsa desberdinen portaera zinematiko eta dinamikoa erreplikatzea ahalbidetzen duen metodologia baten bitartez diseinatu eta gauzatu da. Esperimentu bidezko balioztatzea serbo prentsa industrial batean ere gauzatu da hiru konformatuko prozesu desberdinetan. Estimazio emaitzek dPFak prozesuko indarrari jarraitzeko duen ahalmena forgatu dute, neurtutako indarrarekiko %5ekoa baino txikiagoko desbideraketa lortuz indar maximoa egiten den puntuan. dPF algoritmoa field programmable gate array (FPGA) baten bitartez azeleratu da, denbora errealeko estimazioa lortzeko.La fuerza que una servo prensa ejerce conformando una pieza es la magnitud más importante en cualquier operación de conformado. La fuerza aplicada, junto a las características del troquel, es la magnitud que da forma a la pieza. Cuando la fuerza de proceso es más grande que la fuerza máxima para la que fue diseñada la servo prensa, la integridad de ésta puede verse afectada. Por lo tanto, el conocimiento de la fuerza de proceso es de gr´an interés tanto para los fabricantes de prensas como para los usuarios de las mismas. Así pues, el sector del conformado está buscando sistemas capaces de monitorizar la fuerza de proceso y el funcionamiento de la servo prensa para analizar el proceso y predecir futuras desviaciones de las operaciones de conformado. Los usuarios de las servo prensas quieren garantizar la calidad de las piezas fabricadas y reducir las paradas de las servo prensas debidas al mal funcionamiento de las mismas. Esta tesis aborda la monitorización de la fuerza de proceso y el comportamiento dinámico de una servo prensa mediante un algoritmo de tratamiento estadístico de la señal conocido como el dual Particle Filter (dPF). Inicialmente, tanto el modelo desarrollado como el dPF propuesto han sido evaluados y validados experimentalmente en un banco de ensayos de escala reducida. El banco de ensayos ha sido diseñado y fabricado mediante una metodología de diseño que permite replicar el comportamiento cinem´atico y din´amico de distintas servo prensas en el mismo banco. La validación experimental también se ha llevado a cabo en una servo prensa industrial mediante tres procesos de conformado distintos. Los resultados de estimación han provado la habilidad del dPF para seguir la fuerza de proceso en los procesos evaluados, obteniendo una desviación menor que un 5% con respecto a las señales medidas en el punto donde se da la fuerza máxima. El algoritmo dPF ha sido acelerado mediante un filed programmable gate array (FPGA) para lograr estimaciones en tiempo real

New trends in electrical vehicle powertrains

The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft

Motion capture technology in industrial applications: A systematic review

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications