IDENTIFICATION OF THE MASS INERTIA MOMENT IN AN ELECTROMECHANICAL SYSTEM BASED ON WAVELET–NEURAL METHOD
Marcin TOMCZYK
tomczykmarcin@poczta.fmElectrical School No. 1 in Krakow, Kamieńskiego 49, 30-644 Kraków (Poland)
Barbara BOROWIK
Cracow University of Technology, Warszawska 24, 31-155 Kraków (Poland)
Bohdan BOROWIK
The University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biała (Poland)
Abstract
This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simulations the method of identifying an inertia moment of reduced masses on shaft of induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients analysis of testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electromechanical system has been verified for its correctness during the backlash zone width monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effective diagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.
Keywords:
induction motor, wavelet transformation, backlash zone, neural networksReferences
Doniec, R. (2010). Wykorzystanie metod sztucznej inteligencji do regulacji poziomu insuliny w organizmie człowieka (doctoral dissertation). Wydawnictwo Politechniki Śląskiej, Gliwice.
Google Scholar
Duda, J. T. (2007). Pozyskiwanie wzorców diagnostycznych w komputerowych analizach sprawności urządzeń. In J. Korbicz, K. Patan, & M. Kowal (Eds.), Diagnostyka procesów i systemów (pp. 1–16). Warszawa: Akademicka Oficyna Wydawnicza EXIT.
Google Scholar
Farronato, L., Monti A., Ponci, F., Ferrero, A., Cristaldi, L., & Lazzaroni, M. (2005). Virtual system Fault Models for Training Fuzzy-Wavelet Identifiers in Electrical Drive Diagnosis: an Experimental Validation. In IMTC 2005 Proceedings of the IEEE. Instrumentation and Measurement Technology Conference (pp. 2310–2315). Ottawa: IEEE. https://doi.org/10.1109/IMTC.2005.1604589
DOI: https://doi.org/10.1109/IMTC.2005.1604589
Google Scholar
Ishkova, I., & Vitek, O. (2016). Detection and Classification of faults in induction motor by means of motor current signature analysis and stray flux monitoring. Przegląd Elektrotechniczny, 92(4), 166–170. https://doi.org/10.15199/48.2016.04.36
DOI: https://doi.org/10.15199/48.2016.04.36
Google Scholar
Korbicz, J. (2002). Diagnostyka procesów. Modele. Metody sztucznej inteligencji. Zastosowania. Warszawa: WNT.
Google Scholar
Kowalski, Cz. (2006). Zastosowanie analizy falkowej w diagnostyce silników indukcyjnych. Przegląd Elektrotechniczny, 82(1), 21–26.
Google Scholar
Rusiecki, A. (2007). Algorytmy uczenia sieci neuronowych odporne na błędy w danych (doctoral dissertation). Politechnika Wrocławska, Wrocław.
Google Scholar
Wolkiewicz, M., & Kowalski, Cz. (2015). Diagnostyka uszkodzeń uzwojeń stojana silnika indukcyjnego z wykorzystaniem dyskretnej transformaty falkowej obwiedni prądu stojana. Maszyny elektryczne: zeszyty problemowe, 3(107), 13–18.
Google Scholar
Yayakumar, K., Thangavel, S., & Elango, M. K. (2015). Backpropagation Algorithm for Bearing Fault Detection of Induction Motor Drive Using Wavelet Packet Decomposition. International Journal of Applied Engineering Research, 10(10), 26191–26208.
Google Scholar
Authors
Marcin TOMCZYKtomczykmarcin@poczta.fm
Electrical School No. 1 in Krakow, Kamieńskiego 49, 30-644 Kraków Poland
Authors
Barbara BOROWIKCracow University of Technology, Warszawska 24, 31-155 Kraków Poland
Authors
Bohdan BOROWIKThe University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biała Poland
Statistics
Abstract views: 137PDF downloads: 58
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, 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: Vol. 14 No. 4 (2018)
- Sebastian CYGAN, Barbara BOROWIK, Bohdan BOROWIK, STREET LIGHTS INTELLIGENT SYSTEM, BASED ON THE INTERNET OF THINGS CONCEPT , Applied Computer Science: Vol. 14 No. 1 (2018)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
Similar Articles
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, 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: Vol. 14 No. 4 (2018)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA, INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS , Applied Computer Science: Vol. 13 No. 1 (2017)
- Wulan Dewi, Wiranto Herry Utomo, PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 1 (2021)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Lukas BAUER, Leon STÜTZ, Markus KLEY, BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 17 No. 4 (2021)
- Grzegorz KŁOSOWSKI, Tomasz KLEPKA, Agnieszka NOWACKA, NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
You may also start an advanced similarity search for this article.
