Abstract
By utilizing the SVM and neural BP network, a method of state diagnosis for rolling bearing is presented. The SVM is used to establish a classifier for the normal and fault state, then two kinds of samples caused by the distinct states are trained to judge whether the rolling bearing is normal or false. If the rolling bearing is in the fault state, all the fault samples were trained by the classifier composed of BP neural network to recognize which fault state it is in, otherwise, the state diagnosis is finished. The final experiment results show that the proposed method can diagnose the fault type more quickly and effectively in the small sample circumstances compared with the one using the BP neural networks solely.
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References
Chang, J., Li, T., Luo, Q.: Fault dignosis of rolling bearing based on time domain parameters. In: Chinese Control and Decision Conference (CCDC),5498857, Xuzhou, China, May 26-28, pp. 2215–2218 (2010)
Han, Q., Wang, H.: Based on rolling bearing failure diagnosis in wavelet analysis. In: International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), Changchun, China, August 24-26, vol. 6, pp. 59–63 (2010)
Liu, L., Wei, L., Song, X., et al.: Fault diagnosis method of rolling bearing based on RBF neural network. Journal of Agricultural Machinery 37(3), 163–165 (2006)
Yang, Z., Peng, T.: Fault diagnosis method of rolling bearing based on Vibration signal analysis and SVM. Journal of Hunan University Of Technology 23(1), 96–99 (2009)
Shi, Z.: Neural networks. Higher Education Press, Beijing (2009)
Vapnik, V.N.: The nature of statistical learning theory[M]. Springer, NewYork (1999)
Ding, F., Shao, J., Zhang, Y., et al.: The application of neural network in fault diagnosis method of rolling bearing. Journal of Vibration Engineering 17, 425–428 (2004)
Sun, L., Yang, S.: Fault diagnosis method of rolling bearing based on SVM “one against one” cluster structure. Journal of Hefei University of Technology 32(1), 4–8 (2009)
Jaehe, Y., Azer, B., Ibrahim, M.: Adaptive reliable multicast. In: IEEE International Conference on Communications, New Orleans, US, July 18-22, vol. 3, pp. 1542–1546 (2000)
Zhang, Z., Li, L., He, Z.: Fault classifier and application based on SVM. Mechanical Science and Technology 23(5), 536–538 (2004)
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© 2011 Springer-Verlag Berlin Heidelberg
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Guan, J., Li, G., Liu, G. (2011). A Method of State Diagnosis for Rolling Bearing Using Support Vector Machine and BP Neural Network. In: Zhu, M. (eds) Electrical Engineering and Control. Lecture Notes in Electrical Engineering, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21765-4_16
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DOI: https://doi.org/10.1007/978-3-642-21765-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21764-7
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