Application of Least Square-Support Vector Machines in Reliability Analysis of NC Machine Tools

Abstract:

• Periodical: Advanced Materials Research (Volumes 181-182)
• Pages: 166-171
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.181-182.166
• Online since: January 2011
• Authors: Zhi Ming Wang
• Keywords: Bayesian Inference, Failure Time Prediction, Grid-Searching, Least Squares Support Vector Machines (Ls-SVM), Leave-Out-One (LOO) Cross Validation

References

[1] L. Zhang, Z. Wang and S. Zhao: Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimising method. Mechanical Systems and Signal Processing, Vol. 21 (2007), pp.856-865.

DOI: 10.1016/j.ymssp.2005.09.013

[2] S. Kanmani, V. R. Uthariaraj and V. Sankaranarayanan, et al: Object-oriented software fault prediction using neural networks. Information and Software Technology, Vol. 49 (2007), pp.483-492.

DOI: 10.1016/j.infsof.2006.07.005

[3] Y. Zhang, G. Shen and Y. Jia, et al.: Research on the Disciplinarian and Reliability of Failure Distribution of CNC Lathe. Transactions of the Chineses Society for Agricultural Machinery, Vol. 37 (2006), pp.156-159. (in Chinese).

[4] Y. Liang: Combining neural networks and genetic algorithms for predicting the reliability of repairable systems. International Journal of Quality & Reliability Management, Vol. 25 (2008), pp.201-210.

DOI: 10.1108/02656710810846943

[5] V. Vapnik: Statistical learning theory (Wiley, New York 1998).

[6] J. A. K. Suykens, V. T. Gestel, J. D. Brabanter, et al.: Least squares support vector machines. (World Scientific, Singapore 2002).

DOI: 10.1142/5089

[7] H. Rong, G. Zhang and W. Jin: Selection of Kernel Functions and Parameters for Support Vector Machines in System Identification. Journal of systems imulation, Vol. 8 (2006), pp.3204-3208. (in Chinese).

[8] X. Wang and Z. Li: Identifying the Parameters of the kernel Function in Support Vector Machines Based on the Grid-Search Method. Periodical of Ocean University of China, Vol. 35 (2005), pp.859-862. (in Chinese).

[9] G. C. Cawley and N. L. C. Talbot: Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks, Vol. 17 (2004), pp.1467-1475.

DOI: 10.1016/j.neunet.2004.07.002