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Vibration-Signature-Based Inter-Turn Short Circuit Identification in a Three-Phase Induction Motor Using Multiple Hidden Layer Back Propagation Neural Networks


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DOI: https://doi.org/10.15866/iree.v13i2.13881

Abstract


Inter-turn short circuits in stator windings area fairly common fault in induction motors. Early detection of this type of fault will greatly assist in sustaining production processes in manufacture. This paper proposes a method to detect inter-turn short circuits in stator windings at an early stage. The proposed method consists of four steps: (1) preprocessing by decomposing the signal into detail and approximation signals using a wavelet transform, (2) converting the first detail signal into a frequency-based signal using fast Fourier transform, (3) calculating the values of statistical features for the signal in time and frequency domains and (4) identifying faults using a back propagation neural network (BPNN). Using BPNN architecture with 3 hidden layers and 75 neurons per layer, the identified recognition rate was96.67% with a mean square error of 1.39×10-5. The validity of the proposed method is excellent based on a receiver operating characteristic analysis, with a precision level of 94.66%.
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Keywords


Back Propagation Neural Network (BPNN); Induction Motor; Inter-Turn Short Circuits; Vibration Signature Analysis

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References


B. Bessam, A. Menacer, M. Boumehraz, and H. Cherif, “A novel method for induction motors stator inter-turn short circuit fault diagnosis based on wavelet energy and neural network,” presented at the International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015 IEEE 10th, Guarda, Portugal, 2015, pp. 143–149.
http://dx.doi.org/10.1109/demped.2015.7303682

S. Grubic, J. M. Aller, Bin Lu, and T. G. Habetler, “A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4127–4136, Dec. 2008.
http://dx.doi.org/10.1109/tie.2008.2004665

R. N. Dash, S. Sahu, C. K. Panigrahi, and B. Subudhi, “Condition monitoring of induction motors: — A review,” presented at the International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016, Paralakhemundi, India, 2016, pp. 2006–2011.
http://dx.doi.org/10.1109/scopes.2016.7955800

P. J. Broniera, W. S. Gongora, A. Goedtel, and W. F. Godoy, “Diagnosis of stator winding inter-turn short circuit in three-phase induction motors by using artificial neural networks,” presented at the Symposium On Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International, Valencia, Spain, 2013, pp. 281–287.
http://dx.doi.org/10.1109/demped.2013.6645729

S. A. Ethni, S. M. Gadoue, and B. Zahawi, “Inter-turn short circuit stator fault identification for induction machines using computational intelligence algorithms,” presented at the IEEE International Conference on Industrial Technology (ICIT), 2015, Seville, Spain, 2015, pp. 757–762.
http://dx.doi.org/10.1109/icit.2015.7125189

H. Abdallah and K. Benatman, “Stator winding inter-turn short-circuit detection in induction motors by parameter identification,” IET Electr. Power Appl., vol. 11, no. 2, pp. 272–288, Feb. 2017.
http://dx.doi.org/10.1049/iet-epa.2016.0432

K. . Rama Rao and M. AriffYahya, “Neural networks applied for fault diagnosis of AC motors,” presented at the International Symposium on Information Technology, 2008. ITSim2008., Kuala Lumpur, Malaysia, 2008, pp. 1–6.
http://dx.doi.org/10.1109/itsim.2008.4631918

N. Lashkari and J. Poshtan, “Detection and discrimination of stator interturn fault and unbalanced supply voltage fault in induction motor using neural network,” presented at the Power Electronics, Drives Systems & Technologies Conference (PEDSTC), 2015 6th, Tehran, Iran, 2015, pp. 275–280.
http://dx.doi.org/10.1109/pedstc.2015.7093287

D. V. Ramana and S. Baskar, “Diverse fault detection techniques of three-phase induction motor — A review,” presented at the International Conference on Emerging Technological Trends (ICETT), Kollam, India, 2016, pp. 1–8.
http://dx.doi.org/10.1109/icett.2016.7873779

A. Bellini, C. Concari, G. Franceschini, C. Tassoni, and A. Toscani, “Vibrations, currents and stray flux signals to asses induction motors rotor conditions,” presented at the IECON 2006 - 32nd Annual Conference of IEEE Industrial Electronics, Paris, France, 2006, pp. 4963–4968.
http://dx.doi.org/10.1109/iecon.2006.347365

M. Tsypkin, “The Origin of the Electromagnetic Vibration of Induction Motors Operating in Modern Industry: Practical Experience—Analysis and Diagnostics,” IEEE Trans. Ind. Appl., vol. 53, no. 2, pp. 1669–1676, Mar. 2017.
http://dx.doi.org/10.1109/tia.2016.2633946

J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramirez-Cortes, R. Morales-Caporal, and I. Cruz-Vega, “Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT,” Shock Vib., vol. 2016, pp. 1–11, 2016.
http://dx.doi.org/10.1155/2016/3530464

P. J. Rodriguez, A. Belahcen, and A. Arkkio, “Signatures of electrical faults in the force distribution and vibration pattern of induction motors,” IEE Proc. - Electr. Power Appl., vol. 153, no. 4, p. 523, 2006.
http://dx.doi.org/10.1049/ip-epa:20050253

J. J. Saucedo-Dorantes, M. Delgado-Prieto, R. A. Osornio-Rios, and R. de Jesus Romero-Troncoso, “Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction,” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 3086–3097, May 2017.
http://dx.doi.org/10.1109/tia.2016.2637307

B. R. Nayana and P. Geethanjali, “Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal,” IEEE Sens. J., vol. 17, no. 17, pp. 5618–5625, Sep. 2017.
http://dx.doi.org/10.1109/jsen.2017.2727638

W. F. Godoy, I. N. da Silva, A. Goedtel, R. H. C. Palacios, G. H. Bazan, and D. Morinigo-Sotelo, “An application of artificial neural networks and PCA for stator fault diagnosis in inverter-fed induction motors,” presented at the International Conference on Electrical Machines (ICEM), 2016 XXII, Lausanne, Switzerland, 2016, pp. 2165–2171.
http://dx.doi.org/10.1109/icelmach.2016.7732822

M. Bouzid, G. Champenois, N. M. Bellaaj, L. Signac, and K. Jelassi, “An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4277–4289, Dec. 2008.
http://dx.doi.org/10.1109/tie.2008.2004667

I. T. Jolliffe, Principal component analysis. New York: Springer, 2002.
http://dx.doi.org/10.1007/978-1-4757-1904-8_7

E. Kilic, O. Ozgonenel, and A. E. Ozdemir, “Fault Identification in Induction Motors with RBF Neural Network Based on Dynamical PCA,” presented at the IEEE International Electric Machines & Drives Conference, 2007. IEMDC ’07., 2007, pp. 830–835.
http://dx.doi.org/10.1109/iemdc.2007.382776

D. Sawitri and T. Taufik, “Fault Detection Using SVM Based Motor Current Signature Analysis for 3-Phase Induction Motors,” in Proceeding of the IASTED, International Symposium Advances in Power and Energy System (APES 2015), Marina del Rey, USA, 2015.
http://dx.doi.org/10.2316/p.2015.831-020

M. Bouzid, G. Champenois, N. M. Bellaaj, L. Signac, and K. Jelassi, “An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4277–4289, Dec. 2008.
http://dx.doi.org/10.1109/tie.2008.2004667


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