Small sample reliability growth modeling using a grey systems model
Small sample reliability growth modeling using a grey systems model
复制标题
DOI：
10.1080/08982112.2017.1318920
复制
发表时间：
2017-04
影响因子：
2
通讯作者：
T. P. Talafuse;E. Pohl
中科院分区：
文献类型：
--
作者：
T. P. Talafuse;E. Pohl
摘要：在进行系统级开发测试时，时间和费用通常保证了故障数据的小样本量。在发现故障时，可以实施重新设计和/或校正动作以提高系统可靠性。目前用于估计可靠性增长的方法，即Crow（AMSAA）增长模型，规定当处理小样本时，参数估计具有很大的不确定性。为了处理有限的故障数据，我们建议使用一个修改后的GM（1，1）模型来预测系统的可靠性增长参数，并探讨如何参数估计的影响，系统的故障遵循多威布尔分布。采用蒙特-卡罗模拟方法绘制系统可靠性响应面，并将模拟结果与修正GM（1，1）模型和AMSAA增长模型的精度进行比较。结果表明，在小样本和多失效模式的情况下，修正GM（1，1）模型比AMSAA模型对增长模型参数的预测精度更高。
ABSTRACT When performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. For purposes of handling limited failure data, we propose the use of a modified GM(1,1) model to predict system reliability growth parameters and investigate how parameter estimates are affected by systems whose failures follow a poly-Weibull distribution. Monte-Carlo simulation is used to map the response surface of system reliability, and results are used to compare the accuracy of the modified GM(1,1) model to that of the AMSAA growth model. It is shown that with small sample sizes and multiple failure modes, the modified GM(1,1) model is more accurate than the AMSAA model for prediction of growth model parameters.