累积法在随机删失数据可靠性评估中的应用

在可靠性数据分析中引入累积法,结合平均秩次法解决随机删失数据下的参数估计问题。以双参数威布尔分布为例,设计了一种对比分析累积法和最小二乘法进行参数估计时估算效果优劣性的蒙特卡洛仿真方法。仿真结果表明,对于不同删失比、不同样本量下的随机删失数据,累积法的计算误差比最小二乘法更小,其结果更加接近真实值。     

应用威布尔过程模型统计分析中的几个问题

对威布尔过程应用中的若干问题作了进一步探讨:如威布尔过程与威布尔分布;具有切断的故障数据处理;威布尔过程的拟合优度检验;可修系统可靠性评估参数;多个同型系统参数一致性检验等.     

车辆悬挂系统扭杆弹簧疲劳寿命的统计分析

本文对车辆悬挂系统扭杆弹簧的寿命作了统计分析,得到服从形状参数m=1.5～2.0的威布尔分布.论证了把可靠度R(t)=0.9的工作寿命作为规范中所规定的额定寿命值的合理性,同时对影响其寿命的若干因素进行了讨论.     

Ⅰ型混合截尾下指数-威布尔分布的统计分析

在定时混合截尾情形下,研究了指数威布尔分布的统计分析问题。利用最大似然估计方法给出了指数-威布尔分布参数的最大似然估计。运用Lindley’s逼近方法计算出了分布参数的Bayes估计。最后,运用Monte-Carlo方法对最大似然估计和Bayes估计结果作了模拟比较,结果表明Bayes估计优于最大似然估计。     

软体油罐储存失效概率分布加速寿命试验研究

随着储存和使用年限的增长,受环境应力的影响,软体油罐各项性能指标均出现不同程度的下降。对软体油罐进行寿命评估对于合理确定油罐的报废周期,开展新型罐体材料研究具有一定的指导意义。以软体油罐模型为试验材料,以气密性测试为失效判别标准,通过加速寿命试验,得到样本在110℃老化温度下的失效数据,并通过威布尔概率图检验法和Van Montfort检验法对失效数据进行检验,得到软体油罐失效概率服从威布尔分布的结论,为软体油罐可靠性寿命评估奠定了理论基础。     

某新型榴弹引信加速寿命试验设计

通过对影响某新型榴弹引信长储失效的主要环境因素（温度和湿度）分析,得出其寿命服从威布尔分布,双环境应力下满足阿伦尼斯模型。用恒定应力加速寿命试验方法计算出双环境应力下威布尔分布的过渡参数、形状参数、尺寸参数等值,从而估算出某新型榴弹引信长储失效规律。     

基于年龄更换策略的威布尔分布备件需求计算方法

备件需求量预测在装备保障工作中发挥着重要作用。预防性维修可以消除装备潜在的故障或避免装备发生故障后所导致的严重后果,从而使装备始终处于期望状态。预防性维修策略下备件需求量建模能够以可预见的可靠性数据为基础进行,其关键问题是最佳更换间隔时间的确定。针对威布尔分布装备备件,采用年龄更换策略,确定最佳更换间隔时间,并在此基础上导出威布尔型备件需求计算模型,为备件需求量的预测提供了一种简单有效的方法。     

基于更新过程的多阶段备件需求模型

正态分布、威布尔分布是常用的备件寿命模型,但是正态分布和威布尔分布的记忆性会导致多阶段需求量预测的相关性.利用更新过程理论,将叠加的更新过程分解和拼接,建立单阶段多项备件需求模型;通过将年龄转移到下个阶段,建立了多阶段不可修备件需求模型;采用加权平均法对多阶段需求模型进行了修正.仿真表明,这种模型能有效预测多阶段需求的波动.     

火箭发动机应储数量的研究

火箭发动机应储数量的确定是其维修保养工作中的一个重要问题。在满足一定精度的前提下,以可靠性评估为中心研究寿命服从逆威布尔分布的火箭发动机的应储数量问题。在具有二项移走的逐步增加I型区间截尾模型下(被随机移走的产品数目服从二项分布),采取不同方案给出决定火箭发动机寿命的关键性参数的可靠性估计,据此确定其应储数量。最后,通过随机模拟方法给出在一定精度指标下火箭发动机的应储数量。     

威布尔分布的战术导弹可靠性试验信息融合方法

针对战术导弹可靠性试验小子样问题,提出了基于威布尔分布,利用小概率原理,针对不同试验环境下试验数据的融合方法。该方法扩大了战术导弹可靠性试验的统计子样,提高了可靠性评定的可信度。     

Weibull型产品的可靠性验证

讨论了Weibull型产品的可靠性验证问题 ,首先分析了Weibull型产品分布参数的无信息验前分布问题 ,利用验前信息可以得到分布参数的验前概率密度函数 ,进而分析了产品的可靠性验证问题 ,顾及了使用方利益和生产方利益。仿真算例表明 ,使用方利益和生产方利益是相互折衷的。     

FRC/钢嵌入式T型接头疲劳试验及可靠性分析

为了研究FRC/钢嵌入式T型连接接头疲劳特性,采用真空成型工艺(VARTM)及缝合技术制作T型连接接头,利用Letry疲劳试验机对接头开展拉-拉疲劳试验。根据疲劳试验结果,研究了T型接头在拉-拉疲劳载荷作用下初始损伤特性及损伤演变规律;基于两参数Weibull分布模型,开展T型连接接头疲劳可靠性分析,拟合得到接头在不同应力水平下的疲劳寿命分布规律,给出接头在不同应力水平下疲劳可靠度函数,以及接头在指定可靠度下应力/寿命双对数曲线关系式(R-lnS-lnN曲线)。结果表明,ln ln[1/R(N)]与lnN呈现良好的线性关系,T型接头疲劳寿命服从Weibull分布;疲劳寿命可靠度双对数方程建立了可靠度、疲劳寿命以及疲劳应力的关系,可指导接头疲劳可靠性设计和工程应用。     

Normal and weibull distributions

Numerous applications of the Weibull distribution in diverse fields of human endeavor are well known today. Nevertheless, it is not uncommon to find applications of the normal distribution in such fields of studies as agriculture, biology, chemistry, engineering, physics, sociology and others. At the present time we have at our disposal many more refined statistical techniques for analyzing the normal rather than the Weibull data. Consequently, it is important for applied statisticians to know if some of their data which can be described by the Weibull distribution can also be described by the normal distribution. The present investigation of the author reveals that the normal distri,bution can be considered to be a good approximation to the Weibull distribution as long as its shape parameter is in the open interval (3.25, 3.61). This fact enables them to perform a refined statistical analysis of their data. Conversely, they can now easily compute the desired normal cumulative probabilities from the Weibull distribution function, which would be especially helpful for those standard normal deviates whose cumulative probabilities cannot be read from the available tables of normal cumulative probability. In a similar situation they can also use the Weibull distribution to obtain an approximation to any desired normal deviate for a given normal probability which may be better than those obtained by the linear interpolation method.     

按Weibull分布计算滚动轴承的寿命和强度的可靠性

运用weibull分布推导出任意可靠性指标下滚动轴承的寿命和强度计算公式,给出了多列轴承和轴承系统的可靠性计算方法和公式,并按照工程的方法加以简化处理。     

A good simple percentile estimator of the weibull shape parameter for use when all three parameters are unknown

In this paper we consider a simple three-order-statistic asymptotically unbiased estimator of the Weibull shape parameter c for the case in which all three parameters are unknown. Optimal quantiles that minimize the asymptotic variance of this estimator, c? are determined and shown to depend only on the true (unknown) shape parameter value c and in a rather insensitive way. Monte Carlo studies further verified that, in practice where the true shape parameter c is unknown, using always c? with the optimal quantities that correspond to c = 2.0 produces estimates, c?, remarkably close to the theoretical optimal. A second stage estimation procedure, namely recalculating c? based on the optimal quantiles corresponding to c?, was not worth the additional effort. Benchmark simulation comparisons were also made with the best percentile estimator of Zanakis [20] and with a new estimator of Wyckoff, Bain and Engelhardt [18], one that appears to be the best of proposed closed-form estimators but uses all sample observations. The proposed estimator, c?, should be of interest to practitioners having limited resources and to researchers as a starting point for more accurate iterative estimation procedures. Its form is independent of all three Weibull parameters and, for not too large sample sizes, it requires the first, last and only one other (early) ordered observation. Practical guidelines are provided for choosing the best anticipated estimator of shape for a three-parameter Weibull distribution under different circumstances.     

Mean hazard rate and its application to the normal approximation of the weibull distribution

It is important for applied statisticians to know if data that can be described by the Weibull distribution can also be described by the normal distribution. Some investigations have been published on this subject. In this article, we review the results in papers related to the above problem. Next, we define the mean hazard rate. We then investigate the properties of the mean hazard rate for the Weibull distribution and the normal distribution, respectively. Finally, we discuss the method of approximating the normal distribution by the Weibull distribution using the mean hazard rate.     

考虑外场差异的步进加速寿命数据可靠性评估方法

针对寿命服从双参数Weibull分布的机电产品,进行单应力、定时转换的步进加速寿命试验,研究加速寿命试验数据下的可靠性评估问题。考虑到外场多应力环境与实验室单一应力环境之间的差异性,构建基于修正系数的分布参数计算模型;开展步进加速寿命试验过程的统计分析,建立各应力下的累计分布函数关系;构建极大似然函数,运用数值迭代法,求解未知参数估计;结合某型机电产品进行仿真方案设计,采用Monte Carlo仿真方法产生样本数据,通过对比分析说明所提出的模型评估精度更高。     

一种新的Weibull分布恒定应力加速寿命试验分析方法

分析了Weibull分布恒定应力加速寿命试验常用的二步分析方法存在的不足 ,建立了一种新的构造数据分析方法。考虑到分布参数的相关性 ,该方法引入了Weibull分布特征寿命参数的二次估计 ,在模型拟合优度上高于原来的二步分析方法 ,使分析精度得到了改善。同时 ,该方法避免了原来二步分析方法的查表过程 ,便于软件实现和工程实际应用。     

Inference on a future reliability parameter with the weibull process model

An inferential procedure is presented which provides confidence intervals for a future reliability parameter when reliability growth testing is only partially completed. Hypothesis tests based on this method are uniformly most powerful unbiased. These results are applicable if (1) the system failure rate can be modeled as the intensity function of a Weibull process and (2) efforts to improve reliability are assumed to continue at a steady rate throughout the intervening period of testing. The usefulness of this methodology is illustrated by evaluating the risk of not reaching some future reliability milestone. If such risk is unacceptably high, program management may have time to identify problem areas and take corrective action before testing has ended. As a consequence, a more reliable system may be developed without incurring overruns in the scheduling or cost of the development program.