The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the operational efficiency of shipping fleets. The findings of this study support the advancement of PdM methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering collaboration opportunities among them.

Click on the article title to read more.

The aim of this article is to highlight the impact of a periodic power source, such as a tidal turbine, on the operation and sizing of an autonomous hybrid microgrid with photovoltaic panels and storage. The technique of hill climbing (repeated local search) is used to find the optimum combination of Renewable Energy Sources (RES) and storage units with respect to the required capital cost for various load curves and weather conditions. To model the operation of the microgrid devices, analytical and phenomenological models, have been used, which take into account the specifications of actual commercial devices. Six different case studies are presented, with and without a tidal generator, which are based on six different sets of electrical consumption data corresponding to the Euripus campus of the National & Kapodistrian University of Athens (NKUA) in Psachna, Evia, Greece, and respective meteorological and tidal current data from the region. The results show that tidal energy may be used in a RES microgrid, where applicable, to satisfy the base load requirements, leading to a reduction in installed capacities of intermittent RES and storage, accompanied with cost reduction, especially in cases where a high load factor is observed or may be achieved, through demand response mechanisms. Such a hybrid microgrid configuration may be appropriate for regions where low velocity tidal and marine currents exist along with substantial solar and/or wind energy potential, such as the Mediterranean coast line and islands.

A quantitative analysis of multiphase diffusion in Cu‐Zn diffusion couple is presented. The analysis is based in using the concentration profiles provided by electron micro‐beam analyzer. From the dependence of the square of phase thickness from annealing time, the growth constant for each phase in each annealing temperature can be calculated. Knowing the growth constant of γ and ε phases one can calculate the activation energy and the diffusion coefficient of the above mentioned intermetallic phases.

Collaborating scientific groups from Athens (Greece), Baku (Azerbaijan) and Sofia (Bulgaria) have conducted a research work on the possible effects of geomagnetic field disturbances (GMF) and cosmic ray intensity (CRI) variations on human homeostasis, particularly, the cardio‐health state. Electrocardiograms (ECGs) of seven functionally healthy persons were digitally registered at the joint Laboratory of Heliobiology located in the Medical Centre INAM, Baku, on working days and Saturdays. Heart rate values, estimated from ECGs, were analysed in relation to daily values of CRI, as measured by the Neutron Monitor of the University of Athens and daily variations of Dst and Ap geomagnetic indices and some significant results had been revealed in previous studies. Researches were continued by study of additional cardiologic parameters estimated from the same ECG data. In this study digital data of RR interval (the time elapsing between two consecutive R waves in the ECG), namely RRminimum, RRmaximum and RRaverage were analyzed taking into consideration different levels of GMF disturbances (estimated through variations of Dst and Ap indices) and cosmic ray activity (through CRI variations). The data refer to the time period 15 July 2006–31 March 2008. Variations of RR intervals show connection to GMF disturbances and CRI variations. The revealed effects are more pronounced for high levels of geomagnetic activity (when geomagnetic storms occur) and large CRI decreases as well as on the days before and after these variations.