Abstract
Connecting rod bearing (CRB) is an important component which joins the reciprocating and rotating movements together in the internal combustion engine (ICE). It is very difficult to identify health status of CRB because of variable working process, complicated excitation and distribution sources, and lack of fault samples. Support vector machine (SVM), which has excellent capability in small data case, was introduced to identify the health status of CRB. In this paper, faults of the CRB were simulated in an ICE with the type of EQ6100. Vibration features were extracted from vibration signals acquired from the shell of ICE. And a SVM multi-classifier was designed to identify health status of CRB by using the radial basis kernel function. Experimental results indicated that the presented fault diagnosis method could effectively recognize different conditions of CRB.
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References
Wu, Z.-Y., Yuan, H.-Q.: Research on Fault Diagnosis of Engine by Ant Colony Support Vector Machine. Journal of Vibration and Shock 28(3), 83–86 (2009)
Jia, J., Kong, F., Liu, Y., et al.: Noise Diagnosis Research Based on Wavelet Packet and Fuzzy C-clusters about Connecting Rod Bearing Fault. Transactions of the Chinese Society for Agricultural Machinery 36(6), 87–91 (2005)
Rao, K.S.R., Yahya, M.A.: Neural Networks Applied for Fault Diagnosis of AC Motors. In: Proceedings of International Symposium on Information Technology, vol. 1-4, pp. 2607–2612 (2008)
Sahin, F., Yavuz, M.C., et al.: Fault Diagnosis for Airplane Engines Using Bayesian Networks and Distributed Particle Swarm Optimization. Parallel Computing 33(2), 124–143 (2007)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Vapnik, V., Golowich, S., et al.: Support Vector Method for Function Approximation Regression, Estimation, and Signal Processing. In: Advances in Neural Information Processing Systems, vol. 9, pp. 281–287 (1996)
Liu, Y., You, Z., et al.: A Novel And Quick SVM-Based Multi-Class Classifier. Pattern Recognition 39, 2258–2264 (2006)
Hu, Q., He, Z., et al.: Fault Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVMs Ensemble. Mechanical Systems and Signal Processing 21, 688–705 (2007)
Yuan, S.-F., Chu, F.-L.: Support Vector Machines and Its Applications in Machine Fault Diagnosis. Journal of Vibration and Shock 26, 29–35 (2007)
Yuan, S.-F., Chu, F.-L.: Support Vector Machines-Based Fault Diagnosis for Turbo-pump Rotor. In: Mechanical Systems and Signal Processing, vol. 20, pp. 939–952 (2006)
Yang, Y., Yu, D., et al.: A Fault Diagnosis Approach for Roller Bearing Based on IMF Envelope Spectrum and SVM. Measurement 40, 943–950 (2007)
Chen, K., Li, C.: Machine Condition Monitoring and Fault Diagnosis Technology. Beijing Science and Technology Press, Beijing (1991)
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© 2011 IFIP International Federation for Information Processing
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Liu, Y., He, Q., Zhang, P., Zhu, Z., Kong, F. (2011). Health Status Identification of Connecting Rod Bearing Based on Support Vector Machine. In: Li, D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18369-0_23
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DOI: https://doi.org/10.1007/978-3-642-18369-0_23
Publisher Name: Springer, Berlin, Heidelberg
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