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2013 | OriginalPaper | Buchkapitel

70. Study on Fault Classification of Power-Shift Steering Transmission Based on v-Support Vector Machine

verfasst von : Yuan Zhu, Ying-feng Zhang, Ai-yong Du

Erschienen in: The 19th International Conference on Industrial Engineering and Engineering Management

Verlag: Springer Berlin Heidelberg

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Abstract

This paper focused on the condition monitoring problem of the Power-Shift Steering Transmission (PSST). Spectrometric oil analysis is an important way to study the running state of PSST. Because of complicated nonlinear relationship in oil analysis data, a model of PSST’ fault classification based on v- Support Vector Machine (v-SVM) is proposed. The fundamental of v-SVM is researched. The influence of model parameters for performance of v-SVM is analyzed. Experimental results show that, comparing with C-support vector machine and BP neural network, the v-support vector machine has good properties in research of fault classification of PSST.

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Literatur
Zurück zum Zitat Bing L, Peilin Z, Zheng C (2006) The application of combination forecasting based on genetic algorithm in oil spectral analysis. Lubr Eng 30(4):145–146 (in Chinese) Bing L, Peilin Z, Zheng C (2006) The application of combination forecasting based on genetic algorithm in oil spectral analysis. Lubr Eng 30(4):145–146 (in Chinese)
Zurück zum Zitat Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the 5th annual workshop computational learning theory, pp 144–152 Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the 5th annual workshop computational learning theory, pp 144–152
Zurück zum Zitat Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. IEEE proceedings of the 5th annual workshop computational. Learning theory. ACM Press, New York, 1992, pp 144–152 Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. IEEE proceedings of the 5th annual workshop computational. Learning theory. ACM Press, New York, 1992, pp 144–152
Zurück zum Zitat Hongbo F, Yingtang Z, Guoquan R (2006) Study on prediction model of oil spectrum based on support vector machines. Lubr Eng 30(11):148–150 (in Chinese) Hongbo F, Yingtang Z, Guoquan R (2006) Study on prediction model of oil spectrum based on support vector machines. Lubr Eng 30(11):148–150 (in Chinese)
Zurück zum Zitat Hu G-S, Ren G-Y, Jiang J-J (2007) A support vector reduction method for accelerating calculation. Proceedings of the 2007 international conference on wavelet analysis and pattern recognition, Beijing, China, 2–4 Nov 2007, pp 1408–1412 Hu G-S, Ren G-Y, Jiang J-J (2007) A support vector reduction method for accelerating calculation. Proceedings of the 2007 international conference on wavelet analysis and pattern recognition, Beijing, China, 2–4 Nov 2007, pp 1408–1412
Zurück zum Zitat Li P, Xu S (2005) Support vector machine and kernel function characteristic analysis in pattern recognition. Comput Eng Des 26(2):302–304 (in Chinese) Li P, Xu S (2005) Support vector machine and kernel function characteristic analysis in pattern recognition. Comput Eng Des 26(2):302–304 (in Chinese)
Zurück zum Zitat Li Y-C, Fang T-J, Yu E-K (2003) Study of support vector machines for shiort-term load forecasting. Proceedings of the CSEE 23(6):55–59 Li Y-C, Fang T-J, Yu E-K (2003) Study of support vector machines for shiort-term load forecasting. Proceedings of the CSEE 23(6):55–59
Zurück zum Zitat Li H-Y, Wang L-Y, Ma B, Zheng C-S, Chen M (2009) Study on no-load running-in wear of power-shift steering transmission based on oil spectrum analysis. Spectrosc Spectral Anal 29(3):749–751 (in Chinese) Li H-Y, Wang L-Y, Ma B, Zheng C-S, Chen M (2009) Study on no-load running-in wear of power-shift steering transmission based on oil spectrum analysis. Spectrosc Spectral Anal 29(3):749–751 (in Chinese)
Zurück zum Zitat Naiyang D, Yingjie T (2004) A new method of data mining: support vector machine. Science Press, Beijing, pp 78–89 (in Chinese) Naiyang D, Yingjie T (2004) A new method of data mining: support vector machine. Science Press, Beijing, pp 78–89 (in Chinese)
Zurück zum Zitat Pawlak Z (1997) Rough set approach to knowledge-based decision support. Euro J Oper Res 99(1):48–57CrossRef Pawlak Z (1997) Rough set approach to knowledge-based decision support. Euro J Oper Res 99(1):48–57CrossRef
Zurück zum Zitat Schölkopf B, Smola A, Wiliamon R et al (2000) New support vector algorithms. Neural Comput 12(15):1207–1245CrossRef Schölkopf B, Smola A, Wiliamon R et al (2000) New support vector algorithms. Neural Comput 12(15):1207–1245CrossRef
Zurück zum Zitat Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24(6):833–849CrossRef Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24(6):833–849CrossRef
Zurück zum Zitat Vapnik VN (1998) Statistical learning theory. Wiley, New York Vapnik VN (1998) Statistical learning theory. Wiley, New York
Zurück zum Zitat Zhai Y, Han Pu, Wang D-F, Wang G (2003) Risk function based SVM algorithm and its application to a slight malfunction diagnosis. Proceedings of the CSEE 23(9): 198-203 Zhai Y, Han Pu, Wang D-F, Wang G (2003) Risk function based SVM algorithm and its application to a slight malfunction diagnosis. Proceedings of the CSEE 23(9): 198-203
Zurück zum Zitat Zheng C-S, Ma B, Ma Y (2009) Test research on state monitor of PSST by oil spectrometric analysis. Spectrosc Spectral Anal 29(4):1013–1017 (in Chinese) Zheng C-S, Ma B, Ma Y (2009) Test research on state monitor of PSST by oil spectrometric analysis. Spectrosc Spectral Anal 29(4):1013–1017 (in Chinese)
Metadaten
Titel
Study on Fault Classification of Power-Shift Steering Transmission Based on v-Support Vector Machine
verfasst von
Yuan Zhu
Ying-feng Zhang
Ai-yong Du
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-38433-2_70