IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD

Marcin TOMCZYK; Barbara BOROWIK; Mariusz MIKULSKI

doi:10.23743/acs-2018-29

Open full text

pdf

Published: Dec 30, 2018

DOI: https://doi.org/10.23743/acs-2018-29

Issue Vol. 14 No. 4 (2018)

Articles

BOVW FOR CLASSIFICATION IN GEOMETRICS SHAPES
Baldemar ZURITA, Luís LUNA, José HERNÁNDEZ, Federico RAMÍREZ

5-11
MODEL OF A COMPUTER SYSTEM FOR SELECTION OF OPERATING PARAMETERS FOR TRANSPORT VEHICLES IN THE ASPECT OF THEIR DURABILITY
Łukasz WOJCIECHOWSKI, Tadeusz CISOWSKI

12-24
COMPARATIVE ANALYSIS OF THE IMPACT OF DIE DESIGN ON ITS LOAD AND DISTRIBUTION OF STRESS DURING EXTRUSION
Irena NOWOTYŃSKA, Stanisław KUT

25-33
NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE STRENGTH OF TANKS DEDICATED TO HOT UTILITY WATER
Paweł BAŁON, Edward REJMAN, Bartłomiej KIEŁBASA, Janusz SZOSTAK, Robert SMUSZ

34-53
IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD
Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI

54-69
A MODEL FOR ASSESSING THE LEVEL OF AUTOMATION OF A MAINTENANCE DEPARTMENT USING ARTIFICIAL NEURAL NETWORK
Daniel HALIKOWSKI, Justyna PATALAS-MALISZEWSKA, Małgorzata SKRZESZEWSKA

70-80
EVALUATION OF SAP SYSTEM IMPLEMENTATION IN AN ENTERPRISE OF THE AUTOMOTIVE INDUSTRY – CASE STUDY
Monika KULISZ

81-92
REVIEW OF THE DATA MODELING STANDARDS AND DATA MODEL TRANSFORMATION TECHNIQUES
Leszek JASKIERNY

93-108

DOI

https://doi.org/10.23743/acs-2018-29

Authors

Marcin TOMCZYK

tomczykmarcin@poczta.fm

Electrical School No. 1 in Krakow them. Silesian Insurgents, Kamieńskiego 49 Street, 30-644 Kraków, Poland

Barbara BOROWIK

bborowik@pk.edu.pl

Cracow Univeristy of Technology, Faculty of Physics, Mathematics and Computer Science, Institute of Computer Science, Warszawska 24 Street, 31-155 Kraków, P, Poland

Mariusz MIKULSKI

mmikulsk1@poczta.onet.pl

State University of Applied Sciences in Nowy Sącz, Institute of Technology, Zamenhofa 1a Street, 33-300 Nowy Sącz,, Poland

Abstract

In this article a new method of identification of a backlash zone width in a structure of an electromechanical system has been presented. The results of many simulations in a tested model of a complex electromechanical system have been taken while changing a value of a reduced masses inertia moment on a shaft of an induction motor drive. A wavelet analysis of tested signals and analysis of weights that have been obtained during a neural network supervised learning - have been applied in a diagnostic algorithm. The proposed algorithm of detection of backlash zone width, represents effective diagnostic method of a system at changing dynamic conditions, occurring also as a result of mass inertia moment changes.

Keywords:

inertia moment, induction motor, wavelet transformation, backlash zone, neural network weights

References

Annamalai, B., & Swaminathan, S. T. (2016). Diagnostics of faults in induction motor via wavelet packet transform. IOSR Journal of VLSI and Signal Processing (IOSR-JVSP), 01–06.

Chandralekha, R., & Yayanthi, D. (2016). Diagnosis of faults in three phase induction motor using Neuro Fuzzy Logic. Journal of Applied Engineering Research, 11(8), 5735–5740.

Da Costa, C., Kashiwagi, M., & Mathias, M. H. (2015). Rotor failure detection of induction motors by wavelet and Fourier transform in non-stationary condition. Case Studies in Mechanical Systems and Signal Processing, 1, 15–26. https://doi.org/10.1016/j.csmssp.2015.05.001 DOI: https://doi.org/10.1016/j.csmssp.2015.05.001

Douglas, H., Pillay, P., & Ziarani, A. (2003). Detection of broken rotor bars in induction motors using wavelet analysis. In IEEE International Electric Machines and Drives Conference, 2003. IEMDC'03 (pp. 923–928). Madison, USA: IEEE. https://doi.org/10.1109/IEMDC.2003.1210345 DOI: https://doi.org/10.1109/IEMDC.2003.1210345

Kowalski, Cz. (2005). Monitorowanie i diagnostyka uszkodzeń silników indukcyjnych z wykorzystaniem sieci neuronowych. Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej, 57(18), 226.

Orlowska-Kowalska, T., & Szabat, K. (2007). Neural-Network Application for Mechanical Variables Estimation of a Two-Mass Drive System. IEEE Transactions on Industrial Electronics, 54(3), 1352–1364. doi:10.1109/TIE.2007.892637 DOI: https://doi.org/10.1109/TIE.2007.892637

Osowski, S. (1996). Sieci neuronowe – w ujęciu algorytmicznym. Warszawa: WNT.

Sridhar, S., Uma Rao, K., & Jade, S. (2016). Detection and classification of power quality disturbances in the supply to induction motor using wavelet transform and neural networks. Balkan Journal of Electrical & Computer Engineering, 4(1), 37–44. https://doi.org/10.17694/bajece.62699 DOI: https://doi.org/10.17694/bajece.62699

Zając, M. (2009). Metody falkowe w monitoringu i diagnostyce układów elektromechanicznych. Kraków: Wydawnictwo Politechniki Krakowskiej im. Tadeusza Kościuszki.

TOMCZYK, M., BOROWIK, B., & MIKULSKI, M. (2018). IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD. Applied Computer Science, 14(4), 54–69. https://doi.org/10.23743/acs-2018-29

IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD

Issue Vol. 14 No. 4 (2018)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 14 No. 4 (2018)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License