基于随机维纳过程的产品加速因子分布确定方法  

Distribution Determination Method of Product Acceleration Factor Based on Stochastic Wiener Process

作　　者：魏高乐[1] 陈志军[2] 

机构地区：[1]空军工程大学装备管理与安全工程学院,西安710043 [2]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《科学技术与工程》2015年第29期21-28,共8页Science Technology and Engineering

摘　　要：针对新研产品加速因子难以确定的问题,提出基于随机维纳过程的产品加速因子分布确定方法。首先根据相似产品信息,利用整体极大似然函数(MLE)和Fisher信息矩阵确定相似产品加速因子先验分布;其次根据专家经验信息给出新研产品加速因子先验分布;再其次通过加权融合思想,将相似产品加速因子先验分布和专家经验加速因子先验分布融合,给出新研产品加速因子最终的先验分布;然后根据新研产品内场试验信息,给出Wiener过程的参数估计;最后利用Bayes理论,充分利用产品低层试验信息对加速因子分布进行更新得到后验分布。以某型新研加速度计为实例,验证了所提出的方法适用性和有效性。Aiming at the problem that the acceleration factor of the new product is difficult to determine,the distribution of product acceleration factor based on stochastic Wiener process is proposed. First,according to the similar product information,determine the similar products to accelerate factor prior distribution using the overall maximum likelihood function（ MLE） and the Fisher information matrix; Secondly,according to the expert experience information gives new research products acceleration factor prior distribution; Again followed by weighted fusion theory,fusioning accelerated factor prior distribution of the similar products and acceleration factor prior distribution based on expert experience gives a accelerating factor final prior distribution of new research products; then,according to new research products infield test information,given the Wiener process parameter estimation; Finally,by the Bayes theory,make full use of products low layer test information to update acceleration factor distribution to get acceleration factor posterior distribution. Taking a case study of the accelerometer,the suitable and validity of the proposed method has been proved.

关 键 词：极大似然函数(MLE) Fisher信息矩阵加速因子 WIENER过程 Bayes理论 先验分布 后验分布 

分 类 号：TB114.3[理学—概率论与数理统计]