Rotor fault detector of the converter-fed induction motor based on RBF neural network

• Autorzy: Kowalski C. T. , Kaminski M.

Identyfikatory

DOI

10.2478/bpasts-2014-0008

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the application of the Radial Basis Function (RBF) networks for the induction motor fault detection. The rotor faults are analysed and fault symptoms are described. Next the main stages of the design methodology of the RBF-based neural detectors are described. These networks are trained and tested using measurement data of the stator current (MCSA). The efficiency of developed RBF-NN detectors is evaluated. Furthermore, influence of neural networks complexity and parameters of the RBF activation function on the quality of data classification is shown. The presented neural detectors are tested with measurement data obtained in the laboratory setup containing the converter-fed induction motor (IM) and changeable rotors with a different degree of damages.

Słowa kluczowe

EN

induction motor; rotor fault; diagnostic symptom; RBF neural network; fault detector

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2014
• Tom: Vol. 62, nr 1
• Strony: 69--76
• Opis fizyczny: Bibliogr. 20 poz., rys., fot., wykr.

Twórcy

autor

Kowalski C. T.

• Institute of Electrical Machines, Drives and Measurements, Wroclaw University of Technology, 19 Smoluchowskiego St., 50-372 Wroclaw, Poland

autor

Kaminski M.

Bibliografia

• [1] W.T. Thomson, “A review of on-line condition monitoring techniques for three-phase squirrel-cage induction motors - past, present and future”, Proc. IEEE Int. Symp. SDEMPED 1, 3-18 (1999).
• [2] C.T. Kowalski and T. Orlowska-Kowalska, “Application of neural networks for the induction motor faults detection”, Mathematics and Computers in Simulation - Trans. IMACS 63 (3-5), 435-448 (2003).
• [3] C.J. Lopez-Toribio, R.J. Patton, and S. Daley, “A mutiplemodel approach to fault-tolerant control using Takagi-Sugeno fuzzy modelling: real application to an induction motor drive system”, Eur. Control Conf., ECC 99 1, CD-ROM (1999).
• [4] K.S. Gaeid and H.W. Ping, “Induction motor fault detection and isolation through unknown input observer”, Scientific Research and Essays 5 (20), 3152-3159 (2010).
• [5] C.J. Lopez-Toribio, R.J. Patton, and S. Daley, “Takagi-Sugeno fuzzy fault-tolerant control of an induction motor”, Neural Computing & Applications 9 (1), 19-28 (2000).
• [6] M. Benbouzid, “A Review of induction motors signature analysis as a medium for faults detection”, IEEE Trans. on Ind. Electronics 47 (5), 984-993 (2000).
• [7] M. Cruz and A.J. Cardoso, “Rotor cage fault diagnosis in threephase induction motors by extended park’s vector approach”, Electric Machines and Power Systems 28 (4), 289-99 (2000).
• [8] C.T. Kowalski and M. Pawlak, “Application of the current space vector method for detection of induction motor faults”, Przegląd Elektrotechniczny 79 (7/8), 771-777 (2004).
• [9] C. Bruzzese, “Analysis and application of particular current signatures for cage monitoring in non-sinusoidally fed motors with high rejection to drive load, inertia, and frequency variations”, IEEE Trans. on Ind. Electronics 55 (12), 4137-4155 (2008).
• [10] B. Akin, U. Orguner, H.A. Toliyat, and M. Rayner, “Low Order PWM inverter harmonics contributions to the inverter-fed induction machine fault diagnosis”, IEEE Trans. on Industrial Electronics 55 (2), 610-619 (2008).
• [11] M.A. Awadallah and M.M. Morcos, “Application of AI tools in fault diagnosis of electrical machines and drives - an overview”, IEEE Trans. on Energy Conversion 18 (2), 245-251 (2003).
• [12] F. Filippetti, G. Frenceschini, C. Tassoni, and P. Vas, “Recent developments of induction motor drives fault diagnosis using AI techniques”, IEEE Trans. on Industrial Electronics 47 (5), 994-1004 (2000).
• [13] C.T. Kowalski and M. Pawlak, “Application of AI methods for rotor faults detection of the induction motor”, Acta Electrotechnica et Informatica 1, 39-41 (2004).
• [14] H. Su and K.T. Chong, “Induction machine condition monitoring using neural network modeling”, IEEE Trans. Industrial Electronics 54 (1), 241-249 (2007).
• [15] A. Sobolewski, “Application of neural classifiers”, PHD Dissertation, Bialystok University of Technology, Białystok, 2008, (in Polish).
• [16] A. Sobolewski, “Neural classifiers of fault symptoms in induction machinery rotor fault diagnosis”, Diagnostics 35, 27-30 (2005).
• [17] M.C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1996.
• [18] M.J.L. Orr, “Recent advances in radial basis function networks”, in Technical Report, Institute for Adaptive and Neural Computation, Univ. of Edinburgh, Edinborough, 1999.
• [19] T. Kanungo, D.M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A.Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation”, IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (7), 881-892 (2002) .
• [20] T. Orlowska-Kowalska and M. Kaminski, “FPGA implementation of the multilayer neural network for speed estimation of the two-mass drive system”, IEEE Trans. on Industrial Informatics 7 (3), 436-445 (2011).