{"title":"Fault Detection and Diagnosis of Broken Bar Problem in Induction Motors Base Wavelet Analysis and EMD Method: Case Study of Mobarakeh Steel Company in Iran ","authors":"M. Ahmadi, M. Kafil, H. Ebrahimi","volume":147,"journal":"International Journal of Energy and Power Engineering","pagesStart":154,"pagesEnd":159,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10010155","abstract":"
Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.<\/p>\r\n","references":"[1]\tAustin H. Bonnet; George G. Soukup, \u201cCause and analysis of stator and rotor failures is 3 phase squirrel cage induction motors\u201d IEEE trans-on Industry application vol 28, no. 7, july 2003.pp 921-237.\r\n[2]\tG. Eason, B. Noble, and I.N. Sneddon, \u201cOn certain integrals of Lipschitz-Hankel type involving products of Bessel functions,\u201d Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. \r\n[3]\tYavanda and Bhim Singh \"Identification of Three Phase Induction Motor Incipient Faults \"19-22 Septamber 2004.\r\n[4]\tRicardo Carvalho J. O. \"Dynamic Performance of Induction Motor Under Non Sinousoidal Condition\"2002ieee.\r\n[5]\tA. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, and G. B. Kilman, \u201cQuantitative evaluation of induction motor broken bars by means of electrical signature analysis,\u201d IEEE Trans. Industry Applications, vol. 37, no. 5, pp. 1248-1255, Sep\/Oct 2001.\r\n[6]\tJ. Faiz, and B. M. Ebrahimi, \u201cDetermination of number of rotor broken bars and static eccentricity degree in induction motor under mixed fault,\u201d Electromagnetics, vol. 28, no. 6, pp. 433-449, August 2008.\r\n[7]\tG. Didier, E. Ternisien, O. Caspary, and H. Razik, \u201cFault detection of broken rotor bars in induction motor using a global fault index,\u201d IEEE Trans. Ind. Appl., vol. 42, no. 1, pp. 79-88, Jan.\/Feb. 2006.\r\n[8]\tRiera-Guasp, M, et al., \u201cA General Approach for the Transient Detection of Slip -Dependent Fault Components Based on the Discrete Wavelet Transform,\u201d IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4167-4180, 2008.\r\n[9]\tSiau, J, et al., \u201cBroken Bar Detection in Induction Motors using, New Zealand, 2003.\r\n[10]\tS. M. Shashidhara1\"Tradeoff Analysis of Wavelet Transform Techniques for the Detection of Broken Rotor Bars in Induction Motors\" \u00a9 Research India Publications, Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 8 (2013), pp. 1019-1030.\r\n[11]\tNorden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng,\" The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis\" Published 8 March 1998.DOI: 10.1098\/rspa.1998.0193.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 147, 2019"}