Inference and optimal design on step-stress partially accelerated life test for hybrid system with masked data

Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.

Parameter Estimation Based on Censored Data under Partially Accelerated Life Testing for Hybrid Systems due to Unknown Failure Causes

In general,simple subsystems like series or parallel are integrated to produce a complex hybrid system.The reliability of a system is determined by the reliability of its constituent components.It is often extremely difficult or impossible to get specific information about the component that caused the system to fail.Unknown failure causes are instances in which the actual cause of systemfailure is unknown.On the other side,thanks to current advanced technology based on computers,automation,and simulation,products have become incredibly dependable and trustworthy,and as a result,obtaining failure data for testing such exceptionally reliable items have become a very costly and time-consuming procedure.Therefore,because of its capacity to produce rapid and adequate failure data in a short period of time,accelerated life testing(ALT)is the most utilized approach in the field of product reliability and life testing.Based on progressively hybrid censored(PrHC)data froma three-component parallel series hybrid system that failed to owe to unknown causes,this paper investigates a challenging problem of parameter estimation and reliability assessment under a step stress partially accelerated life-test(SSPALT).Failures of components are considered to follow a power linear hazard rate(PLHR),which can be used when the failure rate displays linear,decreasing,increasing or bathtub failure patterns.The Tempered random variable(TRV)model is considered to reflect the effect of the high stress level used to induce early failure data.The maximum likelihood estimation(MLE)approach is used to estimate the parameters of the PLHR distribution and the acceleration factor.A variance covariance matrix(VCM)is then obtained to construct the approximate confidence intervals(ACIs).In addition,studentized bootstrap confidence intervals(ST-B CIs)are also constructed and compared with ACIs in terms of their respective interval lengths(ILs).Moreover,a simulation study is conducted to demonstrate the performance of the estimation procedures and the methodology discussed in this paper.Finally,real failure data from the air conditioning systems of an airplane is used to illustrate further the performance of the suggested estimation technique.

Bayesian Analysis in Partially Accelerated Life Tests for Weighted Lomax Distribution

Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress,such as pressure,temperature,vibration,voltage,or load to induce early failures.In this paper,a step stress partially accelerated life test(SSPALT)is regarded under the progressive type-II censored data with random removals.The removals from the test are considered to have the binomial distribution.The life times of the testing items are assumed to follow lengthbiased weighted Lomax distribution.The maximum likelihood method is used for estimating the model parameters of length-biased weighted Lomax.The asymptotic confidence interval estimates of the model parameters are evaluated using the Fisher information matrix.The Bayesian estimators cannot be obtained in the explicit form,so the Markov chain Monte Carlo method is employed to address this problem,which ensures both obtaining the Bayesian estimates as well as constructing the credible interval of the involved parameters.The precision of the Bayesian estimates and the maximum likelihood estimates are compared by simulations.In addition,to compare the performance of the considered confidence intervals for different parameter values and sample sizes.The Bootstrap confidence intervals give more accurate results than the approximate confidence intervals since the lengths of the former are less than the lengths of latter,for different sample sizes,observed failures,and censoring schemes,in most cases.Also,the percentile Bootstrap confidence intervals give more accurate results than Bootstrap-t since the lengths of the former are less than the lengths of latter for different sample sizes,observed failures,and censoring schemes,in most cases.Further performance comparison is conducted by the experiments with real data.

逐步Ⅰ型混合截尾下复合Rayleigh分布竞争失效产品部分步加寿命试验的统计分析

本文在逐步Ⅰ型混合截尾下,研究复合Rayleigh分布竞争失效产品部分步加寿命试验的统计分析问题。首先,基于复合Rayleigh分布竞争失效产品和损伤失效率(TFR)模型,运用极大似然理论和渐近似然理论,给出未知参数和加速因子的极大似然估计和渐近置信区间;然后,对未知参数和加速因子选取先验,利用MH抽样算法获得参数和加速因子的Bayes估计和最大后验密度置信区间(HPD);最后,通过Monte Carlo模拟对2种估计方法进行比较。结果表明:贝叶斯估计效果整体优于极大似然估计(MLE),在相同置信水平下,基于Bayes估计的HPD置信区间长度略短于MLE的渐近置信区间长度。

恒定应力部分加速寿命试验的可靠性分析

基于定时区间删失样本,研究了指数分布产品在恒定应力部分加速寿命试验下的参数估计。利用EM算法得到了分布参数和加速因子的迭代方程。根据Louis提出的缺失信息原则计算了Fisher信息矩阵。由极大似然估计的渐近正态性,构造出分布参数和加速因子的近似置信区间。通过Monte Carlo模拟,从平均相对误差和均方误差两方面对估计量进行了计算,结果表明所有的估计量都具有大样本性质。最后通过模拟样本,计算了不同应力水平下分布参数、加速因子和可靠度的估计。

左删失恒定应力部分加速寿命试验下逆Rayleigh分布的参数估计

在恒定应力部分加速寿命试验下,基于左删失样本,研究了逆Rayleigh分布的参数估计问题。运用极大似然法得到未知参数和加速因子的极大似然估计。根据Louis提出的缺失信息原则,计算了Fisher信息矩阵,得到分布参数和加速因子的近似置信区间。当取参数的先验分布为指数分布时,在平方损失函数下求得了参数的贝叶斯估计,并利用极大似然法估计了超参数。通过Monte Carlo模拟,得到估计量的均方误差,据此对极大似然估计和贝叶斯估计进行了比较。最后,计算了不同左删失样本下逆Rayleigh分布参数及加速因子的估计。

竞争失效产品部分加速寿命试验的统计分析

基于加速寿命试验的失效数据对产品的寿命特征进行统计分析时,需要利用产品寿命和应力水平之间的关系即加速模型。但在工程实际中,加速模型不一定总是已知的,例如新研制的产品。针对这个问题,提出了一种利用部分加速寿命试验研究竞争失效产品寿命特征的统计分析方法。在逐步Ⅰ型混合截尾下,讨论Pareto分布竞争失效产品恒定应力部分加速寿命试验的统计分析问题。文中给出了未知参数和加速因子的极大估计(MLE)和贝叶斯估计(BE)。利用Bootstrap方法及贝叶斯理论分别获得了参数和加速因子的Bootstrap置信区间(Stud-t)、最高后验概率密度置信区间(HPD)。最后运用Monte Carlo方法对各种估计的平均相对误差(ARE)、均方误差(MSE)及参数的置信区间进行了模拟计算,并讨论了样本量和分配比例对估计精度的影响。结果表明:参数的MLE和BE的ARE和MSE均随样本量增大而减小;而参数BE的ARE和MSE均小于MLE的ARE和MSE;样本分配比例和2种估计所对应的ARE和MSE呈负相关关系;在相同的置信度下,参数的HPD置信区间的长度小于Stud-t置信区间的长度。

区间删失下恒定应力部分加速寿命试验的统计分析（英文）

In this paper, a statistical analysis method is proposed to research life characteristics of products based on the partially accelerated life test. We discuss the statistical analysis for constant-stress partially accelerated life tests with Lomax distribution based on interval censored samples. The EM algorithm is used to obtain the maximum likelihood estimations(MLEs) and interval estimations for the shape parameter and acceleration factor.The average relative errors(AREs), mean square errors(MSEs), the confidence intervals for the parameters, and the influence of the sample size are discussed. The results show that the AREs and MSEs of the MLEs decrease with the increase of sample size. Finally, a simulation sample is used to estimate the reliability under different stress levels.

基于逐步Ⅱ型删失恒定应力部分加速寿命试验下Burr Ⅲ分布的统计分析

在可靠性工程试验中,恒定应力部分加速寿命试验不仅能提高效率节省成本,且不需要试验设备的加速方程,这种寿命试验被广泛应用。基于逐步Ⅱ型删失数据,在恒定应力部分加速寿命试验下,对加速因子和Burr Ⅲ分布中的形状参数进行估计。运用极大似然法和贝叶斯方法得到了Burr Ⅲ分布形状参数和加速因子的估计式。此外,利用Monte-Carlo模拟,对上述2种估计方法的效果进行了对比分析。模拟结果表明,在均方误差(MSE)准则下,贝叶斯估计与极大似然估计近似。

逐步Ⅱ型截尾样本下恒定应力部分加速寿命试验的统计分析

在逐步Ⅱ型截尾样本下,研究广义逆Weibull分布恒定应力部分加速寿命试验(PALTs)的参数估计问题。利用极大似然法得到未知参数和加速因子的极大似然估计(MLE),并根据其渐近正态性计算渐近方差—协方差矩阵,以得到未知参数和加速因子的近似置信区间。取未知参数的先验分布为伽马分布,取加速因子的先验分布为均匀分布,使用Lindley近似算法和Metropolis-Hastings(MH)算法,在平方误差损失(SEL)函数下给出未知参数和加速因子的贝叶斯(Bayes)估计和最大后验密度(HPD)置信区间。通过蒙特卡洛模拟,对MLE和Bayes估计进行了比较。结果表明,在所有考虑的情况下,Bayes估计效果较好,其中MH算法相比Lindley估计更有优势。

基于自适应逐步Ⅱ型混合截尾试验Burr-Ⅻ分布的统计分析

本文分别基于自适应逐步Ⅱ型混合截尾试验和逐步Ⅱ型混合截尾试验,结合部分步进应力加速寿命试验,使用经典统计理论和Bayes理论讨论了Burr-Ⅻ分布下的统计分析问题.给出了Burr-Ⅻ分布在不同截尾试验下的极大似然估计和Bayes估计.利用Monte-Carlo模拟对自适应逐步Ⅱ型混合截尾试验和逐步Ⅱ型混合截尾试验下的参数估计进行模拟比较.结果表明,自适应逐步Ⅱ型混合截尾试验下的均方误差更小,区间长度更短,相对误差更小,所以自适应逐步Ⅱ型混合截尾试验更优越.