Tatjana Barashkova, Tatjana Baraškova

@taltech.ee

TalTech Virumaa College, School of Engineering
ENERGY TECHNOLOGY PROCESSES CONTROL WORKING GROUP

Tatjana Barashkova, Tatjana Baraškova


                  Download Compact PDF  
https://researchid.co/tatjana.baraskova

RESEARCH INTERESTS

Natural Sciences and Engineering; Mechanical Engineering, Automation Technology and Manufacturing Technology; Mechanical engineering, hydraulics, vacuum technology, vibration acoustic engineering ; Multifractal Analysis Signals
12

Scopus Publications

Scopus Publications

  • Comparative Calibration of Vibration Sensors for Structural Health Monitoring Applications
    Karolina Karolina Kudelina, Tatjana Baraškova, Veroonika Shirokova
    Edpe 2025 37th International Conference on Electrical Drives and Power Electronics and 12th Joint Croatia Slovakia Conference, 2025
  • New Opportunities in Real-Time Diagnostics of Induction Machines
    Tatjana Baraškova, Karolina Kudelina, Veroonika Shirokova
    Energies, 2024
    This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.
  • Neuro-Fuzzy Approach for Fault Prediction of Mechanical Bearing Faults Using Vibration Analysis
    Veroonika Shirokova, Karolina Kudelina, Hadi Ashraf Raja, Viktor Rjabtšikov, Tatjana Baraškova, et al.
    IECON Proceedings Industrial Electronics Conference, 2024
    Electrical machines play an important role in modern industry. It has brought significant technological advancements, particularly in integrating information technology with physical devices. This fusion has led to the emergence of smart devices and the Internet of Things, transforming industrial operations. However, despite the popularity of predictive maintenance, there remains a notable gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on mechanical bearing faults. Comparing traditional neural network algorithms with a novel approach integrating fuzzy logic, the study demonstrates that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.98% compared to 89.91%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection.
  • Fault Detecting Accuracy of Mechanical Damages in Rolling Bearings
    Karolina Kudelina, Tatjana Baraškova, Veroonika Shirokova, Toomas Vaimann, Anton Rassõlkin
    Machines, 2022
    Electrical machines are to face different challenging factors during operation, such as high unexpected or excessive loads, unusual properties of the working environment, or intense fluctuations in rotation speed. Therefore, maintenance questions and predicting the accuracy of an equipment’s condition have great importance. This study is based on the theory of vibration reliability. This article introduces the most common faults of bearings in electrical machines and discusses their diagnostic possibilities. Experimental setup, as well as studied bearing failures, are described. The accuracy of conducted experiments is introduced.
  • Experience in an Application of Project-Based Learning to Teaching of Mechanical Engineering and Energy Technology Processes Control
    Aleksei Hõbesaar, Tatjana Baraškova, Veroonika Shirokova
    Advances in Intelligent Systems and Computing, 2021
  • Application of the improved method of grids with the estimation of accuracy
    Dmitri Gornostajev, Gennady Aryassov, Tatjana Barashkova, Sergei Zhigailov
    Procedia Engineering, 2014
  • Estimation of complex derivatives and application for fault diagnosis
    23rd Daaam International Symposium on Intelligent Manufacturing and Automation 2012, 2012
  • Development of the improved method of grids
    Annals of Daaam and Proceedings of the International Daaam Symposium, 2011
  • Methods for estimating the damage factor of materials under the influence of plasma
    Proceedings of the International Conference of Daaam Baltic Industrial Engineering, 2010
  • Generalization of the method of finite differences
    Annals of Daaam and Proceedings of the International Daaam Symposium, 2010
  • Methods for Estimation of the Effect of Correlation at the Measurement of Alternating Voltage
    TATJANA BARASHKOVA
    Advanced Mathematical and Computational Tools in Metrology and Testing Viii, 2009
  • Symbolic solution algorithms of mechanic matrix equations
    Annals of Daaam and Proceedings of the International Daaam Symposium, 2009