Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

• Autorzy: Glowacz W. , Glowacz Z.

Identyfikatory

DOI

10.1515/aee-2015-0004

• Języki publikacji: EN

Abstrakty

EN

In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils

Słowa kluczowe

EN

DC motor; diagnostic investigations; FFT; Bayes classifier

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2015
• Tom: Vol. 64, nr 1
• Strony: 29--35
• Opis fizyczny: Bibliogr. 23 poz., rys., wz.

Twórcy

autor

Glowacz W.

• AGH University of Science and Technology Department of Automatics and Biomedical Engineering A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Glowacz Z.

• AGH University of Science and Technology Department of Automatics and Biomedical Engineering A. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

• [1] Demenko A., Circuit models of systems with the electromagnetic field (in Polish). Wyd. Politechniki Poznańskiej, Poznań (2004).
• [2] Drozdowski P., Duda A., Fault detection of induction motors due to the effects of magnetic saturation. Zeszyty Problemowe – Maszyny Elektryczne 100(I): 33-38, Katowice, (2013).
• [3] Dudek-Dyduch E, Tadeusiewicz R, Horzyk A., Neural network adaptation process effectiveness dependent of constant training data availability. Neurocomputing 72(13-15): 3138-3149 (2009).
• [4] Dudzikowski I, Ciurys M., Analysis of operation of a car starter with BLDC motor. Przegląd Elektrotechniczny, 86(4): 166-169 (2010).
• [5] Flasiński M., Introduction to Artificial Intelligence (in Polish). PWN, Warszawa (2011).
• [6] Gieras J., Gieras I., Electrical Energy Utilization. Publishing House Adam Marszalek (1998).
• [7] Glinka T., Investigations of electrical machines in the industry (in Polish). Katowice, ed. I (1998), II (2002).
• [8] Głowacz A., Sound recognition of induction motor with the use of discrete Meyer wavelet transform and classifier based on words. Przegląd Elektrotechniczny 89(6): 152-154 (2013).
• [9] Głowacz A., Głowacz A., Głowacz Z., Diagnostics of Direct Current generator based on analysis of monochrome infrared images with the application of cross-sectional image and nearest neighbor classifier with Euclidean distance. Przegląd Elektrotechniczny 88(6): 154-157 (2012).
• [10] Głowacz A., Głowacz W., Głowacz Z., Recognition of armature current of dc motor with application of FFT and Euclidean distance. Zeszyty Problemowe – Maszyny Elektryczne 84: 179-182, Katowice, (2009).
• [11] Głowacz W., Diagnostics of induction motor based on Spectral Analysis of Stator Current with Application of Backpropagation Neural Network. Archives of Metallurgy and Materials 58(2): 561-564 (2013).
• [12] Głowacz Z, Kozik J., Detection of synchronous motor inter-turn faults based on spectral analysis of park's vector. Archives of Metallurgy and Materials 58(1): 19-23 (2013).
• [13] Kowalski Cz. T., Monitoring and fault diagnosis of induction motors using neural networks (in Polish). Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej 57(18), Wrocław (2005).
• [14] Łukaniszyn M., Basis of electromagnetism (in Polish). Oficyna Wydawnicza PO, Opole (2003).
• [15] MathWorks, MATLAB and Simulink for Technical Computing (2014).
• [16] Noga M., Gołębiowski L., Gołębiowski M., Mazur D., Synchronous motor control with internal permanent magnets IPMS taking into account the limitations (in Polish). Zeszyty Problemowe – Maszyny Elektryczne 82: 55-62, Katowice (2009).
• [17] Retana R., Paweletz A., Herzog H., Analysis and Detection of Short Circuits in Fractional Horsepower Commutator Machines, Energy Conversion. IEEE Transactions on 23(2): 484-491 (2008).
• [18] Skomorowski M., Selected aspects of pattern recognition (in Polish). Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków (2013).
• [19] Sobczyk T.J., Frequency analysis of faulty machines – possibilities and limitation. Proceedings of SDEMPED, Cracow, pp. 121-125 (2007).
• [20] Sułowicz M., Borkowski D., Węgiel T., Weinreb K., Specialized diagnostic system for induction motors. Przegląd Elektrotechniczny, 86 (4), pp. 285-291 (2010).
• [21] Szymański Z., Application of the Magnetic Field Distribution in Diagnostic Method of Special Construction Wheel Traction Motors, Studies in Applied Electromagnetics and Mechanics. Advanced Computer Techniques in Applied Electromagnetics 30: 449-456 (2008).
• [22] Zakrzewski K., Tomczuk B., Koteras D., Simulation of forces and 3D field arising during power autotransformer fault due to electric arc in HV winding. IEEE Transactions on Magnetics 38(2): 1153-1156 (2002).
• [23] Zawilak J., Zawilak T., Medium-power synchronous motors excited permanent magnets (in Polish). Zeszyty Problemowe – Maszyny Elektryczne 100(I): 5-8, Katowice (2013).