Possibilities of using Industrial Internet of Things (IIOT) in industrial communication.

    Industry 4.0, Vol. 5 (2020), Issue 5, pg(s) 206-209

    Future of modern communication technologies of smart modern production systems is moving to wireless form of data transfer between devices. Thanks to use of smart devices that communicate within the network, we are talking about of interconnection of separate devices – Internet of Things (IoT). In connection with the implementation of standards Industry 4.0 in the manufacturing process together with IoT implementation into production process a new term is created – Industrial Internet of Things (IIoT). However, the implementation of this form of wireless communication brings some problems, mainly in field of safety and stability of data transfers. This paper provides a brief overview of the current state of the use of IIOT in industrial communication and production management and describes relationships to concepts such as cyber-physical systems and Industry 4.0. Part of the article is a proposal of communication scheme suitable for the implementation on model of modular production system (MPS).



    Industry 4.0, Vol. 4 (2019), Issue 6, pg(s) 273-276

    The Industrial Predictive Analytics for Industry 4.0 is a system that predict and prevent machine failures and breakdown by analyzing time-series data (temperature, pressure, vibration etc.) received from sensors embedded in machines and equipment. The system can analyze machine parameters to identify patterns and predict breakdowns before they happen. The core of the proposed system is based on Artificial Neural Network approach (both Deep and Shallow Neural Networks). Artificial Intelligence and Artificial Neural Networks allow analyses the huge amounts of data collected from the manufacturing process and predict what will go wrong, and when. The proposed system works in the paradigm of Industry 4.0 and provides the abilities in the area of predictive maintenance. The Industrial Predictive Analytics for Industry 4.0 also contains a decision-making system and support system that significantly increases the level of maintenance.

  • Risk management in context of Industry 4.0

    Industry 4.0, Vol. 3 (2018), Issue 6, pg(s) 340-342

    Prevention 4.0 as part of the enterprise’s safety culture is developing HSE management system to address new challenges in prevention. Industry 4.0 anticipates new linkages between technology, man, and management systems to apply the most efficient IT systems to ensure the flexibility of the production process so that its output is a product that takes into consideration customer requirements. These changes include the existence of new types of risk due to the change of the position of man from the classical production centers to the area of superstructure activities, programmer, setter, maintainer, security technician for the digitization of production processes. Risk identification is based on defining the hazards and threats of a complex manufacturing system in the context of Safety and Security – Sa&Se, their formulation so that characteristic parameters can be efficiently digitized within the manufacturing process.

  • Model-based approach of a decision processing unit in a smart wood-processing company

    Industry 4.0, Vol. 3 (2018), Issue 6, pg(s) 292-296

    The paper deals with the development of a new type of production planning and control in a wood-processing company. The production is already highly automated and data from the production processes are gathered and stored in a database. The project picks up these technical basements in order to automatically provide intelligent decisions and make the factory even smarter.



    Industry 4.0, Vol. 3 (2018), Issue 2, pg(s) 94-96

    Due to intentions of Ukrainian economy to join European Union there are many threats and opportunities on the way. Ukraine has a number of industries producing goods, products and services needed in Europe. The growth of economic ties with EU makes Ukrainian manufactures stronger and has become a question of key importance during last decade. Meantime, Ukraine remains within rather tense conditions of technological lag but with some promising abilities in agriculture and food industries supplemented with a great potential in area of information technologies. This distinctive combination of abilities make it feasible for Ukraine both to become a strong partner for EU and to do a great leap of industrial progress within “Industry 4.0” concept. Nevertheless, one of crucial problems for Ukraine is to build an innovative infrastructure to meet requirements of intellectual transport system conforming to EU “Industry 4.0”. This research paper is focused on discussion of these new features of transport industry and opportunities for Ukraine to make its transport industry a leading advantage for the whole economy. Those drastic changes in Ukrainian transport would procure an excellence for other industries implementing “Industry 4.0”, the same as to become a test ground and an exclusive source of experience for EU on “Industry 4.0” implementation.


    Industry 4.0’s opportunities and challenges for production engineering and managemnet

    Innovations, Vol. 6 (2018), Issue 1, pg(s) 17-18

    Today Industrial Enterprises are facing the challenge of the 4th Industrial Revolution. Steam Power, Henry Ford’s Assembly Line, and Proliferation of Coal-based energy etc. – each of these developments in the evolution of manufacturing fundamentally changed the way products were manufactured and the way manufacturers moved products from factory to the customers. The present paper discusses the term “Industry 4.0” and its main characteristics, as well. Furthermore, a theoretical framework for evaluation the key technologies and concepts with respect to their impact on the production engineering and management. Also this paper discusses and some given arguments why manufacturers need to make changes in their traditional view of the functioning of the production system in term of “Industry 4.0”.



    Machines. Technologies. Materials., Vol. 11 (2017), Issue 11, pg(s) 519-522

    Industry 4.0 which creates “smart factories” present a recent trend in development. The area represents a merge of cyberphysical systems and Internet of Things, which aims to improve manufacturing technologies. Industry 4.0 strives to boost the algorithms and technologies used in industrial processes during the production processes, process preparations, and products delivery. Our intention is to improve the robotics transport system in factory floor. There are a lot of different research approaches in this area for further improvement. Our approach is to deal with multi-agent systems control, because of the great potential it has in practical applications in industrial robotics. The strive for minimizing the work time and maximizing the efficiency can be satisfied through the usage of multiple coordinated agents to achieve the end goal. The use of Automated Guided Vehicles (AGVs), combined with concepts for task planning of multiple agents broadened during the late 20th century. In this paper, the multi-agent system consists of several mobile robots, in other words platforms, which need to transport materials in a workhouse. The goal of each mobile platform is to carry the specified object to a set position. These appointed goals are not predefined and can be changed according to the needs of the user. Working in a dynamic environment, numerous agents with different tasks to complete can be exposed to many obstacles which may be the cause of accidents. For this reason, a careful path planning is required in such environments. The suggested path planning algorithm for this system is A*. A* is a fast path finder, which can navigate quite well in a planar environment, but it is not favorable for dynamical settings. Therefore, a combination of the A* algorithm with a collision avoidance method is proposed for overcoming these difficulties. By doing this, the A* algorithm is expanded to work in dynamical situations and can assure the convergence of any agent towards their goal. This fusion of both, the path finding algorithm and the collision avoidance method, can aid the cooperation of the agents and improve the efficiency of the system as a whole.