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CLC number: TM32; TP183

On-line Access: 2013-12-06

Received: 2013-04-09

Revision Accepted: 2013-10-08

Crosschecked: 2013-11-18

Cited: 5

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.12 P.941-952


Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

Author(s):  Ali Uysal, Raif Bayir

Affiliation(s):  Department of Mechatronics Engineering, Faculty of Technology, Karabuk University, Karabük 78050, Turkey

Corresponding email(s):   rbayir@karabuk.edu.tr

Key Words:  Switched reluctance motor, Kohonen neural network, Real-time condition monitoring, Fault detection and diagnosis

Ali Uysal, Raif Bayir. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. Journal of Zhejiang University Science C, 2013, 14(12): 941-952.

@article{title="Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network",
author="Ali Uysal, Raif Bayir",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
%A Ali Uysal
%A Raif Bayir
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 12
%P 941-952
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300085

T1 - Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
A1 - Ali Uysal
A1 - Raif Bayir
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 12
SP - 941
EP - 952
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300085

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bay, Ö.F., Bayir, R., 2005. Kohonen network based fault diagnosis and condition monitoring of pre-engaged starter motors. Int. J. Autom. Technol., 6(4):341-350.

[2]Bayir, R., Bay, Ö.F., 2004. Serial Wound Starter Motor Faults Diagnosis Using Artificial Neural Network. IEEE Int. Conf. on Mechatronics, p.194-199.

[3]Bayir, R., Bay, Ö.F., 2007. Fault diagnosis in starter motors by classification of wavelet analysis results of faulty starter motor’s current signals using fuzzy logic. J. Fac. Eng. Arch. Gazi Univ., 22(2):363-374 (in Turkish).

[4]Chen, H., Lu, S.L., 2013. Fault diagnosis digital method for power transistors in power converters of switched reluctance motors. IEEE Trans. Ind. Electron., 60(2):749-763.

[5]Chow, M.Y., 1997. Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection. World Scientific, Singapore, p.63-78.

[6]Chowdhury, B.H., Wang, K.Y., 1996. Fault Classification Using Kohonen Feature Mapping. Int. Conf. on Intelligent Systems Applications to Power Systems, p.194-198.

[7]Dorrell, D.G., Cossar, C., 2008. A vibration-based condition monitoring system for switched reluctance machine rotor eccentricity detection. IEEE Trans. Magn., 44(9):2204-2214.

[8]Duran, F., 2008. Intelligent Control of Industrial Washing Machine Uses Switched Reluctance Motor. PhD Thesis, Gazi University Informatics Institute, Ankara, Turkey (in Turkish).

[9]Fausett, L., 1994. Fundamentals of Neural Networks. Prentice Hall Inc., USA.

[10]Finley, W.R., Burke, R.R., 1994. Troubleshooting motor problems. IEEE Trans. Ind. Appl., 30(5):1383-1397.

[11]Gao, X.Z., Ovaska, S.J., 2001. Soft computing methods in motor fault diagnosis. Appl. Soft Comput., 1(1):73-81.

[12]Gao, X.Z., Ovaska, S.J., 2002. Genetic algorithm training of Elman neural network in motor fault detection. Neur. Comput. Appl., 11(1):37-44.

[13]Gonçalves, L.F., Bosa, J.L., Balen, T.R., Lubaszewski, M.S., Schneider, E.L., Henriques, R.V., 2011. Fault detection. diagnosis and prediction in electrical valves using self-organizing maps. J. Electron. Test., 27(4):551-564.

[14]Haykin, S., 1999. Neural Networks: a Comprehensive Foundation. Prentice Hall Inc., USA.

[15]Hoffman, A.J., van der Merwe, N.T., 2002. The application of neural networks to vibrational diagnostics for multiple fault conditions. Comput. Stand. Interface., 24(2):139-149.

[16]Isemann, R., 1997. Fault-detection and fault-diagnosis methods an introduction. Control Eng. Pract., 5(5):639-652.

[17]Jiang, H., Penman, J., 1993. Using Kohonen Feature Maps to Monitor the Condition of Synchronous Generators. Workshop on Neural Network Applications and Tools, p.89-94.

[18]Kohonen, T., 2001. Self-Organizing Map (3rd Ed.). Springer Verlag, Germany.

[19]Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J., 1996. Engineering applications of the self-organizing map. Proc. IEEE, 84(10):1358-1384.

[20]Kowalski, C.T., Orlowska-Kowalska, T., 2003. Neural networks application for induction motor faults diagnosis. Math. Comput. Simul., 63(3-5):435-448.

[21]Li, B., Chow, M.Y., Tipsuwan, Y., Hung, J.C., 2000. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron., 47(5):1060-1069.

[22]Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Mag., 4(2):4-22.

[23]Liu, X.Q., Zhang, H.Y., Liu, J., Yang, J., 2000. Fault detection and diagnosis of permanent-magnet dc motor based on parameter estimation and neural network. IEEE Trans. Ind. Electron., 47(5):1021-1030.

[24]Miller, T.J.E., Stephenson, J.M., MacMinn, S.R., Handersot, J.R., 1990. Switched Reluctance Drives. IEEE/IAS Annual Meeting.

[25]Murray, A., Penman, J., 1997. Extracting useful higher order features for condition monitoring using artificial neural networks. IEEE Trans. Signal Process., 45(11):2821-2828.

[26]Nandi, S., Toliyat, H.A., 1999. Condition Monitoring and Fault Diagnosis of Electrical Machines—a Review. 34th IAS Annual Meeting, p.197-204.

[27]Penman, J., Yin, C.M., 1994. Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines. IEE Proc.-Electr. Power Appl., 141(6):317-322.

[28]Ruba, M., Szabó, L., 2009. Fault tolerance study of switched reluctance machines by means of advanced simulation techniques. Pollack Periodica, 4(2):107-116.

[29]Samanta, B., Al-Balushi, K.R., 2003. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process., 17(2):317-328.

[30]Selvaganesan, N., Raja, D., Srinivasan, S., Renganathan, S., 2006. Neural Control and Fault Diagnosis for 6/4 Switched Reluctance Motor. IEEE Int. Conf. on Industrial Technology, p.1741-1746.

[31]Selvaganesan, N., Raja, D., Srinivasan, S., 2007. Fuzzy based fault detection and control for 6/4 switched reluctance motor. Iran. J. Fuzzy Syst., 4(1):37-51.

[32]Torkaman, H., Afjei, E., 2011. Magnetostatic field analysis and diagnosis of mixed eccentricity fault in switched reluctance motor. Electromagnetics, 31(5):368-383.

[33]Torkaman, H., Afjei, E., 2013. Method for eccentricity fault monitoring and diagnosis in switched reluctance machines based on stator voltage signature. IEEE Trans. Magn., 49(2):912-920.

[34]Torkaman, H., Afjei, E., Ravaud, R., Lemarquand, G., 2011. Misalignment fault analysis and diagnosis in switched reluctance motor. Int. J. Appl. Electromagn. Mech., 36(3):253-265.

[35]Torkaman, H., Afjei, E., Yadegari, P., 2012. Static, dynamic, and mixed eccentricity faults diagnosis in switched reluctance motors using transient finite element method and experiments. IEEE Trans. Magn., 48(8):2254-2264.

[36]Ustun, O., 2009. Investigation of flux and inductance measurement methods of the switched reluctance machines. SDU Int. J. Technol. Sci., 1(2):21-33 (in Turkish).

[37]Vas, P., 1993. Parameter Estimation, Condition Monitoring and Diagnosis of Electrical Machines. Oxford University Press, London, UK.

[38]Vas, P., 1999. Artificial Intelligence Based Electrical Machines and Drives. Oxford University Press, New York, USA.

[39]Yang, B.S., Han, T., An, J.L., 2004. ART-KOHONEN neural network for fault diagnosis of rotating machinery. Mech. Syst. Signal Process., 18(3):645-657.

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