Induction Motor Electrical Fault Diagnosis by a Fundamental Frequency Amplitude using Fuzzy Inference System
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2015 by IJRES Journal | ||
| Volume-2 Issue-6 |
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| Year of Publication : 2015 | ||
| Authors : Karim Abdel-Hakam Mohamed, Galal Ali Hassaan, Adel A. Hegazy |
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| DOI : 10.14445/23497157/IJRES-V2I6P104 |
How to Cite?
Karim Abdel-Hakam Mohamed, Galal Ali Hassaan, Adel A. Hegazy, "Induction Motor Electrical Fault Diagnosis by a Fundamental Frequency Amplitude using Fuzzy Inference System," International Journal of Recent Engineering Science, vol. 2, no. 6, pp. 15-24, 2015. Crossref, https://doi.org/10.14445/23497157/IJRES-V2I6P104
Abstract
For reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. This paper presents an optimized induction motor fault identification system using fuzzy logic technique based on vibration signal analysis to investigate the type of AC induction motor failure using MATLAB Simulink. This is accomplished by getting a spectrum values, motor frequencies, and their amplitudes by making a code for each. These are linked to fuzzy inference system for five types of induction motor faults individually to recognize the fault. Fuzzy inference system contains input variables. This is represented by fault frequency features and sidebands in axial, vertical, and horizontal directions, membership function, rules of each fault, and the corresponding output variables. The system is tested by applying a fault simulation with one or more faults.
Keywords
Fuzzy logic, fault diagnosis, vibration analysis, and induction motor faults.
Reference
[1] L. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, ,IEEE Transactions on Systems, and Cybernetics, 1(3), January 1973, 28-44.
[2] G.Guddo, B, Li, and M. Chow, Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision System, IEEE, www4.ncsu.edu, 1998, 1961-1965.
[3] M. Zeraoulia, A.Mamoune, H.Mangel, M. Benbouzid, A simple fuzzy logic approach for induction motors stator condition monitoring,, J. Electrical Systems, 1(1), 2005, 15-25.
[4] X. Liu, L. Ma and J. Mathew, Machinery fault diagnosis based on feature level fuzzy integral data fusion techniques, IEEE International Conference on Industrial Informatics, Singapore, 2006, 857-862.
[5] A. El-Shafei, T. Hassan, and A. Soliman, Y. Zeyada, Neural network and fuzzy logic diagnostics of 1x faults in rotating machinery, Journal of Engineering for Gas Turbines and Power, Barcelona, Spain, July 2007, 703- 710.
[6] M.Iorgulescu, and R. Beloiu, Vibration and current monitoring for fault’s diagnosis of induction motors, Annals of the University of Cariova, Electrical Engineering Series, 2008, 102-107.
[7] R. Kumar, K. Kumar, and K. Ray, Fuzzy logic based fault detection in induction machines using Labview, International Journal of Computer Science and Network Security, 9(9), September 2009, 226-243.
[8] K. Kumar, S. Kumar, and B. Praveena, Soft computing based fault diagnosis, International journal of computer and Electrical Engineering, 2(4), August 2010, 753-760.
[9] V. Do, and U.Chong, Signal model-based fault detection and diagnosis for induction motors using features of vibration signal in two-dimension domain, Journal of Mechanical Engineering, 2011, 655-666.
[10] W. Laala, S. Guedidi, and S. Zouzou, Novel approach for diagnosis and detection of broken bar in induction motor at low slip using fuzzy Logic, dspace.univ-biskra.dz, 2011, 511-516.
[11] K. Bhardwaj, and A. Agarawal, Fault diagnosis of three phase induction motor using fuzzy logic controller and sequence analyzer, MIT International Journal of Electrical and Instrumentation Engineering, 2(2), August 2012, 112- 118.
[12] V. Pandey and P. Choudhary, Induction motor condition monitoring using fuzzy logic, Advance in Electronic and Electric Engineering, 3(6), 2013, 755-764.
[13] S. Shukla, M. Jha, and M. Qureshi, Motor current signature analysis for fault diagnosis and condition monitoring of induction motors using interval type-2 fuzzy logic, International Journal of Innovative Science, Engineering & Technology, 1(5), July 2014, 84-95.
[14] A. Nasiri, and M. Hosseini, Fuzzy-wavelet for detection of eccentricity in induction motors, www.wseas.us, 2005, 4 pages.
[15] C. Scheffer and P. Girdhar, Practical machinery vibration analysis and predictive maintenance (Elsevier, Linacre House, Jordan Hill, Oxford, 2004).
[16] J. E. Berry, Predictive maintenance and vibration signature analysis II(Charlotte, P. C.1997).
[17] A. R. Crawford, The simplified handbook of vibration analysis, Vol. 2 (Computational Systems, Incorporated, 1992).
[18] H. L. Larsen, Fundamentals of fuzzy sets and fuzzy logic, AAUE Computer Science/Course in Fuzzy Logic, Sept. 2005
[19] ISO 10816-1: 1995, Mechanical vibration – Evaluation of machine vibration by measurements on non-rotating partspart 1