Engineering, Energy, Transport AIC

Valerii HRANIAK – Candidate of Science (Engineering), Associate Professor, Associate Professor of the Department of Power engineering, electrical engineering and electromechanics of Vinnytsia National Agrarian University. (3 Soniachna St., Vinnytsia, Ukraine, 21008, e-mail: titanxp2000@ukr.net https://orcid.org/0000-0001-6604-6157).

Valentyn ROHACH – Recipient of the Third Educational and Scientific Level of the contract form of education with a separation from production, majoring in 133 Industrial Mechanical Engineering of the Vinnytsia National Agrarian University. (Soniachna St., 3, Vinnytsia, Ukraine, 21008, e-mail: valentyn.rohach@gmail.com, https://orcid.org/0009-0006-0914-6956).

Today, in conditions of given the high demands for reliability and uninterrupted operation of equipment, electric machines are used in important and even critical technological processes, where their failure can lead to serious consequences, including production downtime, material losses, or even safety hazards. Diagnostic systems enable the timely detection of potential faults, which helps prevent emergency situations and significantly reduces costs associated with unforeseen repairs.
Moreover, with the advancement of technology, the design of electric machines is becoming increasingly complex, necessitating the implementation of more precise monitoring methods. Such diagnostics not only help prevent emergency situations but also extend the service life of equipment. Additionally, the timely detection of defects helps maintain high energy efficiency in machines, which is crucial for reducing energy costs and minimizing the negative environmental impact.
Integrating diagnostic systems into the overall automated infrastructure allows for real-time monitoring and more accurate forecasting of potential failures. These approaches reduce the risk of accidents, improve the efficiency of production process management, and ensure safety. In the context of the transition to Industry 4.0 and the widespread use of digital technologies, such systems have become an indispensable element for ensuring the stable and safe operation of rotating electric machines, which is critical for modern industrial enterprises. They also allow for a significant reduction in the energy intensity of production processes by extending the operational life of primary production assets.
The work proposes diagnostic indexes for detecting bearing defects in induction motors, theoretically justifying the use of the Hilbert transform for current-based bearing defect diagnostics, and demonstrating that the spectrum of the current envelope waveform should be plotted on a linear scale.

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