Petreski, Zlatko

Preferred name

Petreski, Zlatko

Official Name

Petreski, Zlatko

Main Affiliation

zlatko.petreski@mf.edu.mk

Now showing 1 - 6 of 6

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Application of similitude laws for experimental investigations of dynamic properties of tall prototype steel structure
(Faculty of Engineering Hunedoara, 2016)
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This paper elaborates the procedures for scaling prototype structure to scale model according to similitude laws keeping the dynamic properties of the original structure. The scaling procedures are very suitable for experiments where the purpose is not only to check the strength of the structure, but also to investigate the performance of other dynamic devices mounted on the primary structure such as dynamic absorbers. In such cases, the use of condensation methods comes to mind in order to reduce the original d.o.f. and make the model structure behave same in the first several modes as the original structure.
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Design of low-cost wireless noise monitoring sensor unit based on IoT concept
(JVE International Ltd., 2021-01-22)
Anachkova, Maja
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Domazetovska, Simona
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Experimental Measurements of System Dynamics Between Two Stages of Wire Drawing Machine
(Walter de Gruyter GmbH, 2015-03-01)
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Shishkovski, Dejan
<jats:title>Abstract</jats:title> <jats:p>In multi-stage wire drawing machines productivity growth can be achieved at higher drawing speeds by preventing wire breakage during the process. One disadvantage of high-speed wire drawing is the requirement imposed by machine dynamics in terms of its stability and reliability during operation. Tensile forces in the wire must maintained by fast synchronization of all capstans speed. In this process, the displacement sensors play the main role in providing the control system with feedback information about the wire condition. In this study, the influences between the sensors and actuator driven capstans have been studied, and tuner roll concept of a wire drawing machine was experimentally investigated. To this aim, measurements were carried out on two drawing stages at different drawing speeds and obtained results were presented. These results clearly show the fast changes of the capstans speed and the angular displacements of the rollers that tighten the wire, which only confirms the high dynamics of the wire drawing machine.</jats:p>
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Modeling and Characterizing of Electrodynamic Shaker ESE 211
(IEEE, 2024-06-11)
Shishkovski, Dejan
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Pecioski, Damjan
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Ignjatovska, Anastasija
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Anachkova, Maja
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The Shaker actuator is a fundamental component in vibration testing and control systems, serving as a versatile tool for inducing controlled vibrations in mechanical structures. Knowing the characteristics performance is essential for selecting the right electrodynamic shaker for a specific testing application. This paper investigates performance parameters of electrodynamic shaker type ESE211 from VEB Schwingungtechink und Akustik, WIB, Dresden, for which there is no available performance data online. Using system identification techniques by measuring dynamic response and various electrical and mechanical parameters, we create an integrated electro mechanical mathematical model and simulation model in MATLAB/SIMULINK. Using this combined modeling technique, where part of the parameters are obtained with static measurements, and part with dynamic measurements, we create a model which gave an excellent result in comparison to the experimental results. The testing range of dynamical response of the shaker was from 1Hz to 100 Hz sinusoidal sweep input signaland payloads specimens from bare table to 975grams.
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A Multimodal Hybrid Piezoelectric–Electromagnetic Vibration Energy Harvester Exploiting the First and Second Resonance Modes for Broadband Low-Frequency Applications
(MDPI AG, 2026-03-27)
Shishkovski, Dejan
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Markovska, Simona Domazetovska
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Pecioski, Damjan
The increasing demand for autonomous wireless sensors in Internet of Things (IoT) ap-plications has intensified research on vibration energy harvesting, particularly in the low-frequency range where ambient vibrations are most prevalent. However, most vibra-tion energy harvesters operate efficiently only at a single resonance mode, resulting in a narrow operational bandwidth and pronounced performance degradation under fre-quency detuning. To address this limitation, this paper proposes a multimodal hybrid pi-ezoelectric–electromagnetic vibration energy harvester that exploits both the first and sec-ond resonance modes of a cantilever-based structure to achieve broadband low-frequency operation. The design is guided by the complementary utilization of strain-dominated and velocity-dominated regions associated with different vibration modes. Numerical model-ing and finite element simulations are employed to investigate the influence of mass dis-tribution, deformation characteristics, and relative velocity on energy conversion perfor-mance. A secondary cantilever carrying the electromagnetic coil is introduced to enhance the relative motion between the coil and the magnetic field, thereby extending the effective operational bandwidth. The experimental results demonstrate increased harvested power, improved energy conversion efficiency, and a significantly broadened effective frequency range compared to conventional single-mode piezoelectric and electromagnetic energy harvesters.
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Fault Diagnosis of Rotating Machinery Using Supervised Machine Learning Algorithms with Integrated Data-Driven and Physics-Informed Feature Sets
(MDPI AG, 2026-03-17)
Ignjatovska, Anastasija Angjusheva
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Shishkovski, Dejan
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Domazetovska Markovska, Simona
This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed fea-ture sets. A dataset acquired under variable load and multiple operating conditions was used for model training. Parallel signal processing techniques were applied to capture fault-related information across multiple frequency bands including time-domain analy-sis, frequency-domain analysis, baseband analysis, and envelope analysis. From the cor-responding signal representations, statistical, spectral, and physics-based features associ-ated with characteristic fault frequencies were extracted and combined into integrated feature sets. The diagnostic performance of models trained using purely data-driven fea-tures was systematically compared with models incorporating integrated data-driven and physics-informed features. Support Vector Machine, Random Forests, Gradient Boosting, and an ensemble classifier were evaluated using accuracy, precision, recall, and F1-score metrics. The proposed framework employs a two-layer classification strategy, where the first layer performs multiclass fault identification, while the second layer evaluates the presence of imbalance as a coexisting fault. In addition, the influence of different feature groups as well as individual measurement axes and their combinations on diagnostic performance were analyzed. Validation using a new dataset measured in laboratory con-ditions confirmed the robustness and generalization capability of the proposed diagnostic framework.

Petreski, Zlatko

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