Özet:

We have proposed a concept of the modular cyberphysical system for the early diagnosis of industrial and household power equipment based on the application of approaches and standards of Industry 4.0, in particular the concept of the Internet of Things. The main task of the concept and approaches proposed in this paper is the indirect diagnosis and identification of any power equipment whose basic element is the asynchronous motor, in particular the identification of failures and excessive power consumption. In order to resolve the set tasks, it is proposed to use a modular structure of Smart Box diagnosed devices. Specifically, we demonstrate a model of the modular cyberphysical system using a Smart Box device for the early diagnosis of electric equipment, as well as its information flows. This makes it possible to divide all the technological objects at an enterprise into separate structural units, which could form a part of the information cluster. That reduces the reaction time in a cluster system by 30‒35 % compared to a standard one. In addition, the use of a given type of the system makes it possible to reduce the quantity of specialized equipment to the application of similar power equipment. It is proposed to use as a computational core of a Smart Box device the structure a neuro-fuzzy network, which consists of 5 layers. A special feature of this system is the capability to change the number of terms for input variables in order to improve the quality of identification of induction motors. We have chosen, as informative attributes, the characteristic frequencies, which identify an electric motor in the power grid. Specifically, for the systems with small generating capacity, in order to increase the diagnosed induction motors within a cluster, it is advisable to reduce the input set, for example, to 3‒4 CF. The results of our study, in the form of a model of the modular cyberphysical system could be used to build hardware and software modules for the diagnosis of technological and household electrical equipment. In turn, these modules could be combined into an overall global network of IoT. Author Biographies Andreу Kupin, SIHE «Kryvyi Rih National University» Vitaliya Matusevycha str., 11, Kryvyi Rih, Ukraine, 50027 Doctor of Technical Sciences, Professor, Head of Department Department of Computer Systems and Networks

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