摘要
针对加工中心可靠性模型,提出了一种基于支持向量回归模型的参数估计方法,并利用改进的局部最优粒子群优化算法对支持向量回归模型的参数进行优化,以提高其对可靠性模型参数的估计精度。与标准的局部最优粒子群优化算法比较,改进的局部最优粒子群优化算法引入了变异操作和自适应调节惯性因子,提高了算法的全局最优解搜索能力。将提出的方法与最小二乘法、最大似然估计法、局部最优粒子群优化算法优化的支持向量回归模型和遗传算法优化的支持向量回归模型进行了对比试验。试验结果表明:该方法的参数估计精度高于最小二乘法、最大似然估计法、局部最优粒子群优化算法优化的支持向量回归模型和遗传算法优化的支持向量回归模型。最后,将该方法用于估计实际加工中心可靠性模型的参数,得出了相应的平均故障间隔时间的评估数值。
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.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2015年第3期829-836,共8页
Journal of Jilin University:Engineering and Technology Edition
基金
吉林省科技发展计划项目(20130302009GX)
吉林大学研究生创新基金项目(2014053)
关键词
数控机床
加工中心可靠性
Weibull模型
支持向量回归
粒子群优化
computer numerial control machine tool
machining center reliability
Weibull model
support vector regression
particle swarm optimization
