摘要
数控机床中很多子系统故障数很少,属于小样本的类型,使用传统经典可靠性建模方法进行建模往往会有较大的误差。对于小样本类型的故障数据,首先使用极大似然估计方法估计出各子系统故障间隔时间威布尔分布模型的参数,再采用参数偏差修正方法对各威布尔分布的模型进行修正。最后使用D检验法和误差面积比检验法检验参数偏差修正的效果,检验结果证明了修正后的模型更优。
Many subsystems in CNC machine tool have only a few failures,so they are small samples.In this case,classical reliability modeling method often has large errors.For the failure data of small sample,maximum likelihood estimation method is used to estimate the Weibull distribution model parameters of time between failures of the subsystems.Then,the Weibull distribution model parameters are modified by parameter bias correction method.Finally,the effect of bias correction parametersis tested by the D test and the error area ratio test method.The test results show that the modified models are better.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第8期55-59,共5页
Journal of Chongqing University
基金
国家自然科学基金项目(50875110)
'高档数控机床与基础制造装备'科技重大专项(2010ZX04014-011
关键词
机床
小样本
可靠性
威布尔分布
参数偏差修正
machine tool
small sample
reliability
Weibull distribution
parameter bias correction
