Abstract
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.
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References
Broch, J.T.: Mechanical Vibration and Shock Measurements. Brüel & Kjaer, Naerum (1984). ISBN 8787355361
Ypma, A.: Learning Methods for Machine Vibration Analysis and Health Monitoring Theses. Delft University of Technology, Delft (2001). ISBN 90-9015310-1
Maluf, D.A., Daneshmend, L.: Application of machine learning for machine monitoring and diagnosis. In: 10th International Florida Artificial Intelligence Research Symposium, pp. 232–236 (1997)
SKF: Vibrodiagnostic Guide, DIF s.r.o., San Diego (1994). CM5003-CZ
Zuth, D.: Analýza vibrací (Vibration analysis). Theses. Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science, Brno (2004)
Zuth, D., Vdoleček, F.: Měření vibrací ve vibrodiagnostice. Automa: časopis pro automatizační techniku. FCC Public, Praha (1994). ISSN 12109592
Vdoleček, F.: Terminology in branch of measurement uncertainties [Terminologie v oboru nejistot měření]. Akustika 16(1), 40–42 (2012). ISSN 1801-9064
ViDiTech \(::\) 2500CV. http://www.viditech.cz/index.php/home-cs/products/online-monitory/2000cv/. Accessed 20 Apr 2017
Acknowledgment
This research was supported by the grant of BUT IGA No. FSI-S-14-2533: “Applied Computer Science and Control”.
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Zuth, D., Marada, T. (2019). Utilization of Machine Learning in Vibrodiagnostics. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_24
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DOI: https://doi.org/10.1007/978-3-319-97888-8_24
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