An adaptive artificial immune system for fault classification
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- 作者：Ilhan Aydin (1) iaydin@firat.edu.tr
Mehmet Karakose (1) mkarakose@firat.edu.tr
Erhan Akin (1) eakin@firat.edu.tr - 关键词：Artificial immune system – ; Clonal selection – ; Fault diagnosis – ; Fuzzy K ; NN – ; Classification
- 刊名：Journal of Intelligent Manufacturing
- 出版年：2012
- 出版时间：October 2012
- 年：2012
- 卷：23
- 期：5
- 页码：1489-1499
- 全文大小：822.2 KB
- 参考文献：1. Aydin, I., et al. (2009). A multi-objective artificial immune algorithm for parameter optimization in support vector machine, Applied Soft Computing Journal. doi:10.1016/j.asoc.2009.11.003.
2. Aydin, I., Karakose, M., & Akin, E. (2008). Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods. In Proceedings of IEEE computational intelligence for measurement system and application, Istanbul (pp. 93–98) 14–16 July.
3. Ayhan B., Chow M. Y., Song M. H. (2006) Multiple discriminant analysis and neural network-based monolith and partition fault- detection schemes for broken rotor bar in induction motors. IEEE Transactions on Industrial Electronics 53(4): 1298–1308
4. Bellini A., Filipetti F., Tassoni C., Capolino G. A. (2008) Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics 55(12): 4109–4126
5. Benbouzid M. E. H. (2000) A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics 47(5): 984–993
6. Branco P. J. C., Dente J. A., Mendes R. V. (2003) Using immunology principles for fault detection. IEEE Transactions on Industrial Electronics 50(2): 362–373
7. Briz F., Degner M. W., Garcia P., Bragado D. (2008) Broken rotor bar detection in line-fed induction machines using complex wavelet analysis of startup transients. IEEE Transactions on Industry Applications 44(3): 760–768
8. Calis H., Cakir A. (2008) Experimental study for sensorless broken bar detection in induction motors. Energy Conversion and Management 49(4): 854–862
9. Chen K. Y., Lim C. P., Lai W. K. (2005) Application of a neural fuzzy system with rule extraction to fault detection and diagnosis. Intelligent Manufacturing 16: 679–691
10. Cizek V. (1970) Discrete Hilbert transform. IEEE Transactions on Audio Electroacoustic 18: 340–343
11. da Silva A. M., Povinelli R. J., Demerdash N. A. O. (2008) Induction machine broken bar and stator short-circuit fault diagnostics based on three phase stator current envelopes. IEEE Transactions on Industrial Electronics 55(3): 1310–1318
12. Dasgupta D. (2006) Advances in artificial immune systems. IEEE Computational Intelligence Magazine 1(4): 40–49
13. Daviu J. A., Rodriguez P. J., Guasp M. R., Arkkio A., Folch J. R., Perez R. B. (2009) Transient detection of eccentricity-related components in induction motors through the Hilbert–Huang Transform. Energy Conversion and Management 50(7): 1810–1820
14. de Castro L. N., Zuben F. J. V. (2002) Learning and optimization using the clonal selection principles. IEEE Transactions on Evolutionary Computation 6(3): 239–251
15. Duda R. O., Hart P. E. (1973) Pattern classification and scene analysis. Wiley, New York
16. Haji, M., & Toliyat, H. A. (2001). Pattern recognition- a technique for broken rotor fault detection ‘eccentricity and broken bar fault’. In: Proceedings of IEEE thirty six industry applications conference (pp. 1572–1578). Chicago, IL, USA
17. Hao X., Cai-xin S. (2007) Artificial immune network classification algorithm for fault diagnosis of power transformer. IEEE Transactions on Power Delivery 22(2): 930–935
18. Jimenez G. A., Munoz A. O., Mermoud M. A. D. (2007) Fault detection in induction motors using Hilbert and Wavelet transforms. Electrical Engineering 89(3): 205–220
19. Jolliffe I. T. (1986) Principal component analysis. Springer, New York
20. Keller J. M., Gray M. R., Givens J. A. (1985) A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems Man and Cybernetics 15: 580–585
21. Kliman G. B., Koegl R. A. (1988) Noninvasive detection of broken bars in operating induction motors. IEEE Transactions on Energy Conversion 3(4): 873–879
22. Kliman G. B., Stein J. (1992) Methods of motor current signature analysis. Electric Machines and Power Systems 20(5): 463–474
23. Leung K., Cheong F., Cheong C. (2007) Generating compact classifier systems using a simple artificial immune system. IEEE Transactions on Systems, Man and Cybernetics-Part B 37(5): 1344–1356
24. Li R., Sopon P., He D. (2009) Fault features extraction for bearing prognostics. Intelligent Manufacturing. doi:10.1007/s10845-009-0353-z
25. Martins J. F., Pires V. F., Pires A. J. (2007) Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault. IEEE Transactions on Industrial Electronics 54(1): 259–264
26. Onel I. Y., Aycicek E., Senol I. (2009) An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform. Intelligent Manufacturing 20(2): 243–247
27. Panadero R. P., Sanchez M. P., Guasp M. R., Folch J. R., Perez E. H., Cruz J. P. (2009) Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Transactions on Energy Conversion 24(2009): 52–59
28. Pires D. F., Pires V. F., Martins J. F., Pires A. J. (2009) Rotor cage fault diagnosis in three-phase induction motors based on a current and virtual flux approach. Energy Conversion Management 50(4): 1026–1032
29. Supangat R., Ertugrul N., Soong W. L., Gray D. A., Hansen C., Grieger J. (2006) Detection of broken rotor bars in induction motor using starting-current analysis and effects of loading. IEE Proceedings of Electric Power Applications 153: 848–855
30. Thomson W. T., Fenger M. (2001) Current signature analysis to detect induction motor faults. IEEE Industrial Applications Magazine 7: 26–34
31. Wang C. C., Too G. P. J. (2002) Rotating machine fault detection based on HOS and artificial neural networks. Intelligent Manufacturing 13: 283–293
32. Watkins, A. B., & Bogges, L. C. (2002). A resource limited artificial immune classifier. In Proceedings of evolutionary Computation (CEC ‘02) (pp. 926–931). 12–17 May, Honolulu, USA.
33. Zarei J., Poshtan J. (2007) Bearing fault detection using wavelet packet transform of induction motor currents. Journal of Tribology 40(5): 763–769
34. Zhong Y., Zhang L., Huang B., Li P. (2006) An unsupervised artificial immune classifier for multi/hyper spectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 44(2): 420–431 - 作者单位：1. Computer Engineering Department, Firat University, 23119 Elazig, Turkey
- ISSN：1572-8145
文摘
Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37 kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.