APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES

Michał TOMCZYK; Anna PLICHTA; Mariusz MIKULSKI

doi:10.23743/acs-2019-32

Open full text

pdf

Published: Dec 30, 2019

DOI: https://doi.org/10.23743/acs-2019-32

Issue Vol. 15 No. 4 (2019)

Articles

A DECLARATIVE APPROACH TO SHOP ORDERS OPTIMIZATION
Jarosław WIKAREK, Paweł SITEK, Mieczysław JAGODZIŃSKI

5-15
NUMERICAL MODELLING OF RESINS USED IN STEREOLITOGRAPHY RAPID PROTOTYPING
Danuta MIEDZIŃSKA, Ewelina MAŁEK, Arkadiusz POPŁAWSKI

16-26
CUSTOMIZING AUDIO FADES WITH A VIEW TO REAL-TIME PROCESSING
Lucian LUPŞA-TĂTARU

27-36
DEVELOPMENT OF INTEGRATED MANAGEMENT INFORMATION SYSTEMS IN THE CONTEXT OF INDUSTRY 4.0
Bartosz CIEŚLA, Grzegorz GUNIA

37-48
THE SPECTROPHOTOMETRIC ANALYSIS OF ANTIOXIDANT PROPERTIES OF SELECTED HERBS IN VISION-PRO™ UV-VIS
Karolina FERYSIUK, Karolina M. WÓJCIAK

49-62
ENHANCING APPROACH USING HYBRID PAILLER AND RSA FOR INFORMATION SECURITY IN BIGDATA
Shadan Mohammed Jihad ABDALWAHID, Raghad Zuhair YOUSIF, Shahab Wahhab KAREEM

63-74
AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING
Saheed ADEWUYI, Segun AINA, Aderonke LAWAL, Adeniran OLUWARANTI, Moses UZUNUIGBE

75-92
APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES
Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI

93-108

DOI

https://doi.org/10.23743/acs-2019-32

Authors

Michał TOMCZYK

marcin.tomczyk@pk.edu.pl

* Cracow Univeristy of Technology, Faculty of Electrical and Computer Engineering, Warszawska 24, 31-155, Poland

Anna PLICHTA

aplichta@pk.edu.pl

Cracow Univeristy of Technology, Faculty of Computer Science and Telecommunications, Chair of Computer Science, Warszawska 24, 31-155 Kraków, Poland

Mariusz MIKULSKI

mmikulsk1@poczta.onet.pl

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 networks

References

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

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

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.

Doniec, R. (2010). Wykorzystanie metod sztucznej inteligencji do regulacji poziomu insuliny w organiźmie człowieka (doctoral dissertation). Politechnika Śląska, Gliwice.

Duch, W., Korbicz, J., Rutkowski, L., & Tadeusiewicz, R. (2000). Biocybernetyka i inżynieria biomedyczna 2000. Sieci neuronowe. Tom 6. Warszawa: Akademicka Oficyna Wydawnicza EXIT.

Duda, J. T. (2007). Pozyskiwanie wzorców diagnostycznych w komputerowych analizach sprawności urządzeń, Diagnostyka procesów i systemów (pp. 1–16). Warszawa: Akademicka Oficyna Wydawnicza EXIT.

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

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

Korbicz, J., Kościelny, J. M., & Kowalczuk, Z. (2002). Diagnostyka procesów. Modele. Metody sztucznej inteligencji. Zastosowania. Warszawa: WNT.

Kowalski, Cz. (2003). Stan obecny i tendencje rozwojowe metod monitorowania i diagnostyki napędów z silnikami indukcyjnymi. Wiadomości Elektrotechniczne, 4, 160–164.

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

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

Tadeusiewicz, R. (1993). Sieci neuronowe. Warszawa: Akademicka Oficyna Wydawnicza.

Wysogląd, B. (2003). Metody diagnozowania łożysk tocznych z zastosowaniem transformacji falkowej. Diagnostyka, 29, 47–52.

Zając, M. (2009). Metody falkowe w monitoringu i diagnostyce układów elektromechanicznych. Monografia 371. Kraków: Politechnika Krakowska.

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

TOMCZYK, M., PLICHTA, A., & MIKULSKI, M. (2019). APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES. Applied Computer Science, 15(4), 93–108. https://doi.org/10.23743/acs-2019-32

APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES

Issue Vol. 15 No. 4 (2019)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 15 No. 4 (2019)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License