Research in the field of electric drives and controllers is driven by the need for more efficient, reliable and sustainable solutions in various industries. The integration of sensors and communication technologies into machinery enables real-time monitoring and control of drives, which improves the reliability of the entire plant and enables predictive or proactive maintenance. Remote access to data from monitored drive systems via the Internet increases the efficiency of their operation and enables rapid intervention when needed. Research into the use of 3D printing and additive manufacturing technologies to optimise electric machines and drive components enables rapid prototyping and production of complex components. It also contributes to reducing the energy consumption of drives by optimising motor design and developing advanced control algorithms.

The membership of the research group is multidisciplinary and includes experts from all relevant departments of the Institute of Manufacturing Machines, Systems and Robotics.

Research topics:

• Digital transformation leading to Industry 4.0.

• Creating digital twins of electric drives for simulation, testing, optimization and verification of drive designs.

• Development of machine learning and artificial intelligence algorithms to analyse sensor data to predict and prevent machine failures.

• Development of monitoring systems for the detection of abnormal trends in the behaviour of drives in anticipation of possible machine failures.

• Developing strategies for diagnostic activities, sharing and evaluating monitoring data.

• Advanced analysis of big data sets to identify patterns and trends in measured data.

• Using image recognition in safety applications for machinery.

• Development of monitoring systems for the assessment of the working environment in machinery.

• Using Edge computing to process diagnostic data directly on the production equipment.

• Using AI methods for optimization and adaptive control of drives.

Examples of solved projects:

• Technologies and tools for the digital transformation of engineering

• Electronic counter for hydrogen dispensers

• Ultra high-speed electric active brakes for testing electric vehicle powertrains

• Research and development of a machine tool monitoring unit to support proactive maintenance

• The automatic controls of dedicated technological equipment using machine learning

• Optimization of synchronous machines in order to improve the operating parameters and weight reduction

• National Competence Centre of Mechatronics and Smart Technologies for Mechanical Engineering (TN01000071)

• Development of a universal device for the analysis and cleaning of residential air conditioning systems (MICROCLEAN)

Examples of outputs:

• PRUŠA, R.; HUZLÍK, R.; MAREK, T. Modeling drive of production machine according to output requirements. MM Science Journal, 2023, roč. 2023, č. October, s. 6606-6615. ISSN: 1803-1269.

• HOLUB, M.; ANDRŠ, O.; ŠTĚPÁNEK, V.; KROUPA, J.; HUZLÍK, R.; TŮMA, J.; KOVÁŘ, J.; MARADA, T.; BRADÁČ, F. Experimental study of operational data collection from CNC machine tools for advanced analysis. In 2022 20th International Conference on Mechatronics – Mechatronika (ME). 2023. ISBN: 978-1-6654-1040-3.

• HOLUB, M.; ANDRŠ, O.; ŠTĚPÁNEK, V.; KROUPA, J.; HUZLÍK, R.; TŮMA, J.; KOVÁŘ, J.; MARADA, T.; BRADÁČ, F. Experimental study of operational data collection from CNC machine tools for advanced analysis. In 2022 20th International Conference on Mechatronics – Mechatronika (ME). 2023. ISBN: 978-1-6654-1040-3.

• ZUTH, D.; BLECHA, P.; MARADA, T.; HUZLÍK, R.; TŮMA, J.; MARADOVÁ, K.; FRKAL, V. Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery. Machines, 2021, roč. 9, č. 10, s. 1-19. ISSN: 2075-1702.

• PALOUŠEK, D.; STRECKER, Z.; HUZLÍK, R.; Brno University of Technology: Rotor with structured geometry to improve the parameters of electric rotating machines. Document number 35 254, utility model. (2021).

Contact:
Ing. Rostislav Huzlík, Ph.D.