吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 829-836.doi: 10.13229/j.cnki.jdxbgxb201503021

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Parameter estimation of reliability model of machining center based on particle swarm optimization and support vector regression

YANG Zhao-jun1, 2, YANG Chuan-gui1, 2, CHEN Fei1, 2, HAO Qing-bo1, 3, ZHENG Zhi-tong1, 2, WANG Song1, 2   

  1. 1.College of Mechanical Science and Engineering, Jilin University, Changchun 130022, China;
    2.Key Laboratory of CNC Equipment Reliability Technique of Machinery Industry, Jilin University, Changchun 130022, China;
    3.Department of Mechanics, Aviation University of Air Force,Changchun 130022,China
  • Received:2014-03-28 Online:2015-05-01 Published:2015-05-01

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Abstract: Based on the Support Vector Regression (SVR) model, a method is proposed to assess the parameters of the reliability model of machining centers by analyzing censored data. An improved Local best Particle Swarm Optimization algorithm (improved lbest PSO) is developed to tune parameters of the SVR model to keep working efficiently. The improved Lbest PSO, developed from the local best PSO (lbest PSO), introduces a mutation operation and an adaptive inertia fact to improve its ability to search the global optimal solution. Additionally, the Least Square Model (LSM), the Maximum Likelihood Model (MLM), the SVR model selected by the Lbest PSO and the Genetic Algorithm (GA) are employed to compare their estimation performance with the proposed method. Results show that the proposed method is superior to all the other models. Finally, this method is used to estimate the parameters of the reliability model of a type of machining center and obtain its mean time between failures.

Key words: computer numerial control machine tool, machining center reliability, Weibull model, support vector regression, particle swarm optimization

CLC Number: 

  • TG659
[1] 杨兆军,陈传海,陈菲,等. 数控机床可靠性技术的研究进展[J]. 机械工程学报,2013,49(20):130-139.
Yang Zhao-jun, Chen Chuan-hai, Chen Fei, et al. Progress in the research of reliability technology of machine tools[J]. Journal of Mechanical Engineering,2013,49(20):130-139.
[2] Balakrishnan N, Kateri M. On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data[J]. Statistics & Probability Letters,2008,78(17):2971-2975.
[3] Wu H C. Fuzzy reliability estimation using Bayesian approach[J]. Computers & Industrial Engineering,2004,46(3):467-493.
[4] Vapnik V. Statistical Learning Theory[M]. New York: Wiley, 1998.
[5] 杨兆军, 李小兵, 许彬彬,等. 加工中心时间动态可靠性建模[J]. 机械工程学报,2012,48(2):16-22.
Yang Zhao-jun, Li Xiao-bing, Xu Bin-bin, et al. Time dynamic reliability modelling of machining center[J]. Chinese Journal of Mechanical Engineering,2012,48(2):16-22.
[6] Bryan D. The Weibull Analysis Handbook[M]. ASQ Quality Press, 2006.
[7] Smola Alex J, Bernhard S. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3):199-222.
[8] 吴坚, 赵阳, 何睿. 基于支持向量机回归算法的电子机械制动传感器系统故障诊断[J]. 吉林大学学报:工学版, 2013,43(5):1178-1183.
Wu Jian, Zhao Yang, He Rui. Fault detection and diagnosis of EMB sensor system based on SVR[J]. Journal of Jilin University (Engineering and Technology Edition), 2013,43(5): 1178-1183.
[9] Kennedy J, Mendes R. Population structure and particle swarm performance[C]∥Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu HI,USA,2002:1671-1676.
[10] 寇晓丽, 刘三阳. 基于模拟退火的粒子群算法求解约束优化问题[J]. 吉林大学学报:工学版,2007,37(1):136-140.
Kou Xiao-li, Liu San-yang. Particle swarm algorithm based on simulated annealing to solve constrained optimization[J]. Journal of Jilin University(Engineering and Technology Edition),2007,37(1):136-140.
[11] 王凌, 刘波. 微粒群优化与调度算法[M]. 北京:清华大学出版社, 2008.
[12] Engelbrecht A P. Fundamentals of Computational Swarm Intelligence[M]. Hoboken:John Wiley & Sons, 2006.
[13] Lins I D, Moura M C, Zio E, et al. A particle swarm‐optimized support vector machine for reliability prediction[J]. Quality and Reliability Engineering International,2012,28(2):141-158.
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