Diagnostics of Induction Motor Based on Spectral Analysis of Stator Current with Application of Backpropagation Neural Network

Open access

Nowadays PC computers make possible the signal characteristics by calculation. The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction motor by applying spectral analysis and backpropagation neural network. Software to recognize the current of induction motor was implemented. System of current recognition is based on a study of the frequency spectrum of stator current. The studies were conducted for two conditions of induction motor. The results of the numerical experiments are presented and discussed in the paper. The researches show that the system can be useful for protection of the engines and metallurgical equipment.

[1] J. Kurek, S. Osowski, Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor, Neural Computing & Applications 19, 4, 557-564 (2010).

[2] K. Oprzedkiewicz, Acontrollability problem foralinear, time-invariant, system with uncertainty of state and control, Przeglad Elektrotechniczny 87, 3, 286-292 (2011).

[3] W. Orlewski, A. Siwek, Hydroelectric power plant using dump industrial water, Rynek Energii, 6, 87-91 (2010).

[4] A. Głowacz, Z. G łowacz, Diagnostics of DCmachine based on analysis of acoustic signals with application of MFCCand clasifier based on words, Archives of Metallurgy and Materials 57, 1, 179-183 (2012).

[5] A. Głowacz, Z. G łowacz, Diagnostics of induction motor based on analysis of acoustic signals with application of FFTand classifier based on words, Archives of Metallurgy and Materials 55, 3, 707-712 (2010).

[6] Z. Głowacz, A. G łowacz, Simulation language for analysis of discrete-continuous electrical systems (SESL2), Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control, Innsbruck, Austria, 94-99 (2007).

[7] Z. Głowacz, A. Zdrojewski, Diagnostics of commutator DCmotor using spectral analysis method, Przeglad Elektrotechniczny 85, 1, 147-150 (2009).

[8] A. Głowacz, A. Głowacz, P. Korohoda, Recognition of Color Thermograms of Synchronous Motor with the Application of Image Cross-Section and Linear Perceptron Classifier, Przeglad Elektrotechniczny 88, 10a, 87-89 (2012).

[9] Z. Głowacz, J. Kozik, Feature selection of the armature windings short circuit fault in synchronous motor using genetic algorithm and the Mahalanobis distance, Przeglad Elektrotechniczny 88, 2, 204-207 (2012).

[10] A. Głowacz, A. Głowacz, Z. G łowacz, Diagnostics of Direct Current generator based on analysis of monochrome infrared images with the application of cross-sectional image and nearest neighborclassifier with Euclidean distance, Przeglad Elektrotechniczny 88, 6, 154-157 (2012).

[11] H. Gorecki, M. Zaczyk, Extremal dynamic errors in linear dynamic systems, Bulletin of the Polish Academy of Sciences-Technical Sciences 58, 1, 99-105 (2010).

[12] A. Głowacz, Diagnostics of DCmachine based on sound recognition with application of LPCand GSDM, Przeglad Elektrotechniczny 86, 6, 243-246 (2010).

[13] http://www.lomont.org

[14] A. Głowacz, Z. Mikrut, P. Pawlik, Video Detection Algorithm Using an Optical Flow Calculation Method, 5th International Conference on Multimedia Communications, Services and Security, Krakow, Poland,2012, Communications in Computer and Information Science 287, 118-129.

[15] J. Kwasniewski, Application of the Wavelet Analysis to Inspectioof Compact Ropes Usinga High-Efficiency Device, Archives of Mining Sciences 58, 1, 159-164 (2013).

[16] K. D’Obryn, J. Hydzik -Wisniewska, Selected Aspects of Numerical Modelling of the Salt Rock Mass: The Case of the ”Wieliczka” Salt Mine, Archives of Mining Sciences 58, 1, 73-88 (2013).

[17] J. Sattarvand, C. Niemann- Delius, A New Metaheuristic Algorithm for Long-Term Open-Pit Production Planning, Archives of Mining Sciences 58, 1, 107-118 (2013).

[18] A.A. Bazzazi, M. Esmaeili, Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System, Archives of Mining Sciences 57, 4, 933-943 (2012).

[19] T. Benovic, I. Miljanovic, S. Vujic, Fuzzy Model of Autogenous Suspension Coal Cleaning, Archives of Mining Sciences 57, 4, 843-860 (2012).

[20] S.A. Aalized, F. Rashidinejad, Prediction of penetration rate of rotary-percussive drilling using artificial neural networks -acase study, Archives of Mining Sciences 57, 3, 715-728 (2012).

[21] M. Monjezi, Z. Ahmadi, M. Khandelwal, Application of neural networks for the prediction of rock fragmentation in Chadormalu iron mine, Archives of Mining Sciences 57, 3, 787-798 (2012).

[22] R. Smulski, Comparative Analysis of Selected Models of Water Coning in Gas Reservoirs, Archives of Mining Sciences 57, 2, 451-470 (2012).

[23] P. Strzelczyk, I. Wochlik, R. Tadeusiewicz, A. Izworski, J. Bulka, Telemedical System in Evaluation of Auditory Brainsteam Responses and Support of Diagnosis, 2nd Asian Conference on Intelligent Information and Database Systems (ACIIDS), Lecture Notes in Artificial Intelligence, Hue City, Vietnam, 2010, 21-28.

[24] E. Dudek-Dyduch, R. Tadeusiewicz, A. Horzyk, Neural network adaptation process effectiveness dependent of constant training data availability, Neurocomputing 72, 13-15, 3138-3149 (2009).

Archives of Metallurgy and Materials

The Journal of Institute of Metallurgy and Materials Science and Commitee on Metallurgy of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 0.571
5-year IMPACT FACTOR: 0.776

CiteScore 2016: 0.85

SCImago Journal Rank (SJR) 2016: 0.347
Source Normalized Impact per Paper (SNIP) 2016: 0.740

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 99 99 9
PDF Downloads 30 30 8