Ing. Daniel Zuth, Ph.D. |
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Education and academic qualification
- 2004, Ing., Faculty of Mechanical Engineering, BUT, branch Automation
- 2009, Ph.D., Faculty of Mechanical Engineering BUT, branch Automation
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Career overview
- 2005-2010, Technical staff , Institute of Automation and Computer Science,
Faculty of Mechanical Engineering, Brno University of Technology
- 2010-present: Assistant professor, Institute of Automation and Computer Science,
Faculty of Mechanical Engineering, Brno University of Technology
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Pedagogic activities
- Automation
- Reliability and Diagnostics
- Technical Measurement
- supervisor of BSC students
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Scientific activities
- Project of electronic sensors for measurement local thermal comfort
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University activities
- 2004-2009: Ph.D. Student, Institute of Automation and Computer Science,
Faculty of Mechanical Engineering, Brno University of Technology
- 2010-present: Assistant professor, Institute of Automation and Computer Science,
Faculty of Mechanical Engineering, Brno University of Technology
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Non-University activities
- 2004 - present: Member of Association of Mechanical Engineering
- 2005 - present: Member of Committee of Association of Mechanical Engineering
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Sum of citations (without self-citations) indexed within ISI Web of Knowledge
0
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Sum of other citations (without self-citations)
0
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Supervised courses:
Publications:
- ZUTH, D.:
Ukázka využití strojového učení ve vibrodiagnostice,
Řézení&údržba průmyslového podniku, pp.14-16, ISSN 1803-4535, Trade Media International s.r.o.
journal article - other
- ZUTH, D.; MARADA, T.:
Comparison of Faults Classification in Vibrodiagnostics from Time and Frequency Domain Data,
Mechatronika 2018, pp.482-487, ISBN 978-80-214-5543-6, (2018)
conference paper
akce: 18th International Conference on Mechatronics – Mechatronika 2018, Brno, 05.12.2018-07.12.2018
- ZUTH, D.; MATYÁŠ, P.; SVOBODOVÁ, S.:
Sběr teplotních údajů při použití jednodeskového počítače,
Automa, Vol.21, (2015), No.1, pp.48-50, ISSN 1210-9592, FCC Public
journal article - other
- ZUTH, D.; VDOLEČEK, F.; ROJKA, A.:
Zásadní vliv snímače na výslednou nejistotu diagnózy,
Automa, Vol.16, (2010), No.12, pp.29-30, ISSN 1210-9592, FCC Public
journal article - other
- ZUTH, D.; VDOLEČEK, F.:
Analýza nejistot ve vibrodiagnostice,
Automa, Vol.16, (2010), No.7, pp.41-45, ISSN 1210-9592, FCC Public
journal article - other
- ZUTH, D.; VDOLEČEK, F.:
Zdroje nejistot ve vibrodiagnostice,
Automa, Vol.16, (2010), No.6, pp.40-42, ISSN 1210-9592, FCC Public
journal article - other
- ZUTH, D.:
Využití snímačů typu umělá kůže, Asociace technických diagnostiků České republiky, o.s.
abstract
List of publications at Portal BUT
- ZUTH, D.:
Ukázka využití strojového učení ve vibrodiagnostice,
Řézení&údržba průmyslového podniku, pp.14-16, ISSN 1803-4535, Trade Media International s.r.o.
journal article - other
The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source are real measured data from the vibrodiagnostic model, this model allows simulation of some types of faults.. The data is then processed and reduced for the use of the Matlab Classification learner app, which creates a model for recognizing faults. The model is ultimately tested on a new sample of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in time domain.
- ZUTH, D.; MARADA, T.:
Comparison of Faults Classification in Vibrodiagnostics from Time and Frequency Domain Data,
Mechatronika 2018, pp.482-487, ISBN 978-80-214-5543-6, (2018)
conference paper
akce: 18th International Conference on Mechatronics – Mechatronika 2018, Brno, 05.12.2018-07.12.2018
The paper deals with the comparison of the success rate of classification models from Matlab Classification Learner app. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the vibrodiagnostics model. Five basic faults are recognized, namely, the static unbalances at two levels, the dynamic unbalances at two levels and the faultless state. The data is then processed and reduced for the use of the Matlab Classification Learner app, which creates a model for recognizing faults. The aim of the paper is to compare the success rate of classification models when the data source is dataset in time or frequency domain.
- ZUTH, D.; MARADA, T.:
Utilization of Machine Learning in Vibrodiagnostics,
Recent Advances in Soft Computing, pp.271-278, ISBN 978-3-319-97887-1, (2018), Springer Verlag
journal article in Scopus
The article deals with possibilities of use machine learning in vibrodiagnostics to determine a fault type of the rotary machine. Sample data are simulated according to the expected vibration velocity waveform signal at a specific fault. Then the data are pre-processed and reduced for using Matlab Classification Learner which creates a model for identifying faults in the new data samples. The model is finally tested on a new sample data. The article serves to verify the possibility of this method for later use on a real machine. In this phase is tested data preprocessing and a suitable classification method.
- ZUTH, D.:
Using HIL Simulation and Genetic Algorithms for Controller Tuning
journal article in Scopus
This paper will deal with the possibilities of using the HIL (Hardware-in-the-loop) simulation in
Matlab for setting the controller by genetic algorithms. The main advantage of using HIL is a possibility of
endless repetition of regulation without straining the real system. This paper will compare several algorithms
for an automatic adjustment of parameters of the real controller. The system is numerically simulated in
MATLAB by Runge-Kutta method (RK4), as a controller was used ARM R microcontroller Cortex R M4. In
addition to send an information about the system, it is also possible to send controller setup parameters from
Matlab and repeat the simulation with dierent parameters. Matlab also evaluates the quality of regulation and
uses algorithms to set the best parameters of the controller. The aim of this paper is to automatically set the
parameters of a real controller without the user interaction.
- VDOLEČEK, F.; ZUTH, D.:
Examples of Uncertainties Analyze in Vibration Diagnostics and Acoustics,
Akustika, Vol.14, (2010), No.14/2010, pp.40-44, ISSN 1801-9064, Akustika -D studio
journal article - other
The measurement in acoustics and vibration diagnostics branches are very conformable reciprocally often. The paper presents short reflections above measurement uncertainties sources on vibration measurement result. More detailed modeling uncertainties analyses were implemented on authors' workplace for vibro-diagnostics system. The article presents some interesting results of these uncertainties analysis and it is completing liberal to papers line from last years of this seminar thus.