Identification of a Backlash Zone in an Electromechanical System Containing Changes of a Mass Inertia Moment Based on a Wavelet–Neural Method

• Autorzy: Tomczyk Marcin , Borowik Barbara , Mikulski Mariusz

Identyfikatory

DOI

10.23743/acs-2018-29

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

PL

moment bezwładności; silnik indukcyjny; transformacja falkowa; strefa luzu; sieci neuronowe

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 14, no 4
• Strony: 54--69
• Opis fizyczny: Bibliogr. 9 poz., fig., tab.

Twórcy

autor

Tomczyk Marcin

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

autor

Borowik Barbara

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

autor

Mikulski Mariusz

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

Bibliografia

• [1] 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.
• [2] 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.
• [3] 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 Me-chanical Systems and Signal Processing, 1, 15–26. doi:10.1016/j.csmssp.2015.05.001
• [4] 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. doi:10.1109/IEMDC.2003.1210345
• [5] 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.
• [6] 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
• [7] Osowski, S. (1996). Sieci neuronowe – w ujęciu algorytmicznym. Warszawa: WNT.
• [8] 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. doi:10.17694/bajece.62699
• [9] Zając, M. (2009). Metody falkowe w monitoringu i diagnostyce układów elektromechanicznych. Kraków: Wydawnictwo Politechniki Krakowskiej im. Tadeusza Kościuszki.