APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES
Michał TOMCZYK
marcin.tomczyk@pk.edu.pl* Cracow Univeristy of Technology, Faculty of Electrical and Computer Engineering, Warszawska 24, 31-155 (Poland)
Anna PLICHTA
Cracow Univeristy of Technology, Faculty of Computer Science and Telecommunications, Chair of Computer Science, Warszawska 24, 31-155 Kraków (Poland)
Mariusz MIKULSKI
State Higher Vocational School in Nowy Sącz, Institute of Engineering,Zamenhofa 1a, 33-300 Nowy Sącz (Poland)
Abstract
This paper presents a method of identifying the width of backlash zone in an electromechanical system generating noises. The system load is a series of rectangular pulses of constant amplitude, generated at equal intervals. The need for identification of the backlash zone is associated with a gradual increase of its width during the drive operation. The study uses wavelet analysis of signals and analysis of neural network weights obtained from the processing without supervised learning. The time-frequency signal representations of accelerations of the mechanical load components were investigated.
Keywords:
nduction motor, wavelet transformation, backlash zone, neural networksReferences
Aktas, M., & Turkmenoglu, V. (2010). Wavelet-based switching faults detection in direct torque control induction motor drives. Science, Measurement & Technology, IET, 4(6), 303–310.
DOI: https://doi.org/10.1049/iet-smt.2009.0121
Google Scholar
Balara, D., Timko, J., Źilkova, J., & Leśo, D. (2017). Neural networks application for mechanical parameters identification of asynchronous motor. Neural Network World, 3, 259–270.
DOI: https://doi.org/10.14311/NNW.2017.27.013
Google Scholar
Chebil, J., Noel, G., Mesbah, M., & Derihe, M. (2009). Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings. Jordan Journal of Mechanical and Industrial Engineering, 3(4), 260–267.
Google Scholar
Doniec, R. (2010). Wykorzystanie metod sztucznej inteligencji do regulacji poziomu insuliny w organiźmie człowieka (doctoral dissertation). Politechnika Śląska, Gliwice.
Google Scholar
Duch, W., Korbicz, J., Rutkowski, L., & Tadeusiewicz, R. (2000). Biocybernetyka i inżynieria biomedyczna 2000. Sieci neuronowe. Tom 6. Warszawa: Akademicka Oficyna Wydawnicza EXIT.
Google Scholar
Duda, J. T. (2007). Pozyskiwanie wzorców diagnostycznych w komputerowych analizach sprawności urządzeń, Diagnostyka procesów i systemów (pp. 1–16). Warszawa: Akademicka Oficyna Wydawnicza EXIT.
Google Scholar
Fuente, M. J., & Saludes, S. (2000). Fault detection and isolation in a non-linear plant via neural networks. IFAC Proceedings Volumes, 33(11), 463–468.
DOI: https://doi.org/10.1016/S1474-6670(17)37402-5
Google Scholar
Granda, D., Aguilar, W. G., Arcos-Aviles, D., & Sotomayor, D., (2017). Broken bar diagnosis for squirrel cage induction motors using frequency analysis based on MCSA and continous wavelet transform. Mathematical and Computational Applications, 22(2), 30. https://doi.org/10.3390/mca22020030
DOI: https://doi.org/10.3390/mca22020030
Google Scholar
Korbicz, J., Kościelny, J. M., & Kowalczuk, Z. (2002). Diagnostyka procesów. Modele. Metody sztucznej inteligencji. Zastosowania. Warszawa: WNT.
Google Scholar
Kowalski, Cz. (2003). Stan obecny i tendencje rozwojowe metod monitorowania i diagnostyki napędów z silnikami indukcyjnymi. Wiadomości Elektrotechniczne, 4, 160–164.
Google Scholar
Łobos, T., Leonowicz, Z., Rezmer, J., & Schegner, P. (2006). High resolution spectrum-estimation methods for signal analysis in power systems. IEEE Trans. Instrum. Measur., 55(1), 219–225.
DOI: https://doi.org/10.1109/TIM.2005.862015
Google Scholar
Osowski, S. (1996). Sieci neuronowe – w ujęciu algorytmicznym. Warszawa: WNT.
Google Scholar
Tadeusiewicz, R. (1993). Sieci neuronowe. Warszawa: Akademicka Oficyna Wydawnicza.
Google Scholar
Wysogląd, B. (2003). Metody diagnozowania łożysk tocznych z zastosowaniem transformacji falkowej. Diagnostyka, 29, 47–52.
Google Scholar
Zając, M. (2009). Metody falkowe w monitoringu i diagnostyce układów elektromechanicznych. Monografia 371. Kraków: Politechnika Krakowska.
Google Scholar
Zhang, J. W., Zhu, N., Yang, L., Yao, Q., & Lu, Q. (2007). A fault diagnosis approach for broken rotor bars based on EMD and envelope analysis. Journal of China University Mining & Technology, 17(2), 205–209.
DOI: https://doi.org/10.1016/S1006-1266(07)60073-X
Google Scholar
Authors
Michał TOMCZYKmarcin.tomczyk@pk.edu.pl
* Cracow Univeristy of Technology, Faculty of Electrical and Computer Engineering, Warszawska 24, 31-155 Poland
Authors
Anna PLICHTACracow Univeristy of Technology, Faculty of Computer Science and Telecommunications, Chair of Computer Science, Warszawska 24, 31-155 Kraków Poland
Authors
Mariusz MIKULSKIState Higher Vocational School in Nowy Sącz, Institute of Engineering,Zamenhofa 1a, 33-300 Nowy Sącz Poland
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