恒定应力加速寿命试验的非参数统计方法

在恒定应力加速寿命试验（简称恒加试验）的统计分析中，非参数统计方法具有一定的实际价值。在一定的假定下，文中给出了两种新估计，并证明了它们的优良性。     

基于仿真的竞争失效产品综合应力加速寿命试验优化设计

针对竞争失效产品综合应力加速寿命试验存在试验时间长、费用高、效率低的问题,提出一种基于仿真的竞争失效产品综合应力加速寿命试验优化设计方法。采用Monte-Carlo进行仿真模拟,以正常使用应力下的p阶分位寿命渐进方差估计最小为目标,在各应力组合下试验截尾数大小给定的条件下,以试验应力水平大小作为设计变量,利用MLE理论进行统计分析,建立基于仿真的竞争失效产品综合应力加速寿命试验优化设计模型。通过实例分析表明该方法有效、可行,为电子装备寿命预测的加速试验方案优化设计提供技术支撑。     

指数分布场合基于竞争失效数据的参数估计

讨论了指数分布场合竞争失效产品在恒定应力加速寿命试验和步进应用加速寿命试验情形加速方程中有关参数的加权最小二乘估计，利用Monte Carlo方法和参数的最大似然估计作了模拟比较。     

竞争失效产品步降加速试验优化设计研究

针对解析方法难以得到竞争失效产品步降加速试验最优方案和仿真法仿真规模大的难题,该文提出一种基于BP神经网络拟合的竞争失效产品步降加速试验优化方法。采用Monte-Carlo对加速试验进行模拟仿真,以最佳应力水平和试样分配比例为设计变量,以正常应力水平下各失效机理的对数特征寿命渐近方差作为目标函数,建立竞争失效产品步降加速试验优化设计模型。通过仿真实例,验证该方法有效可行。     

高分子电容湿敏传感器加速寿命试验统计模型的验证

针对目前普遍存在的加速寿命试验可靠性统计模型的准确性问题,结合加速寿命试验的几个基本假设条件,利用某高分子电容湿敏传感器恒定双应力加速寿命试验数据对加速模型准确性进行了验证。结果表明该产品加速寿命试验满足基本假设条件,所做的加速寿命试验合理,为后续加速寿命试验的分析和评估奠定了基础。     

三参数Weibull分布的多因素作用下混凝土加速寿命试验

针对中国西部盐渍土地区混凝土耐久性较差的突出问题,设计了模拟自然环境的室内混凝土加速寿命试验,其中包括盐卤腐蚀、干湿循环、冻融循环和太阳辐射。采用动弹性模量作为评价混凝土耐久性失效阈值,得到混凝土加速寿命数据。基于三参数Weibull分布,运用概率分析软件对寿命数据进行分析预测,得到加速寿命分布概要图。结果表明,三参数Weibull分布能够很好地拟合加速寿命数据样本点;其阈值参数可直观反映混凝土开始退化失效的时间;其分布概要图可直观反映加速退化过程中的可靠性,从而为混凝土耐久性评估提供新的可靠性依据。     

几种线性回归方法的比较

对两种对称性线性回归方法X方向和Y方向的经典线性回归进行了深入的分析和比较，推导和证明了它们的一些特点与性质，在此基础上得出了一些对实际应用有指导意义的结论。     

综合加速寿命试验与仿真的密封圈性能退化规律研究

传统的密封圈寿命评估多采用加速寿命试验完成,由于评估用的失效判据(压缩永久变形)与实际密封性能难以关联,导致评估的寿命偏保守。因此,该文提出基于回归分析的统计建模方法,运用仿真与实验综合分析密封圈的性能退化规律。首先,应用加速寿命试验得到不同使用状态的密封圈,并测试得到其性能参数。然后,通过仿真分析的方法得到各密封圈的接触应力。最后,应用统计分析的方法实现其整体退化规律的建模。该模型可以准确分析密封圈的退化规律,并依据密封圈失效准则,实现其寿命预测。     

牛津型斯特林制冷机的加速寿命试验

分析了牛津型长寿命斯特林制冷机的主要失效模式、失效机理以及应用特点,提出高温运行的加速寿命评价方法.4 300 h的加速寿命试验结果表明,制冷机性能已明显退化,并呈现污染导致的失效特征,初步验证高温加速污染失效的有效性.     

空调交流接触器电寿命试验的加速方法研究

分析了空调交流接触器电寿命试验的主要参数,提出了一种基于支持向量回归算法的交流接触器电寿命试验加速方法.工程试验表明:该方法可以有效缩短交流接触器的电寿命试验周期,保证产品及时供货.     

Robust Estimation for Weibull Distribution in Partially Accelerated Life Tests with Early Failures

Maximum likelihood estimation (MLE) is a frequently used method for estimating distribution parameters in constant stress partially accelerated life tests (CS‐PALTs). However, using the MLE to estimate the parameters for a Weibull distribution may be problematic in CS‐PALTs. First, the equation for the shape parameter estimator derived from the log‐likelihood function is difficult to solve for the occurrence of nonlinear equations. Second, the sample size is typically not large in life tests. The MLE, a typical large‐sample inference method, may be unsuitable. Test items unsuitable for stress conditions may become early failures, which have extremely short lifetimes. The early failures may cause parameter estimate bias. For addressing early failures in the Weibull distribution in CS‐PALTs, we propose an M‐estimation method based on a Weibull Probability Plot (WPP) framework, which leads a closed‐form expression for the shape parameter estimator. We conducted a simulation study to compare the M‐estimation method with the MLE method. The results show that, with early‐failure samples, the M‐estimation method performs better than the MLE does. Copyright © 2015 John Wiley & Sons, Ltd.     

Integrated Approach for Field Reliability Prediction Based on Accelerated Life Testing

ABSTRACT

To predict field reliability using analytical modeling, several important reliability activities should be conducted, including failure mode and effect analysis, stress and usage condition analysis, physics of failure analysis, accelerated life testing and modeling, and cumulative damage modeling if needed. With all of the mentioned activities and results, the field reliability confidence limit can be predicted at a certain confidence level, if a modeling framework can be established. This article builds such an integrated process and comprehensive modeling framework, especially with cumulative damage rules when the certain field stresses are random processes. An engineering product is provided as an application to illustrate the effectiveness of proposed method.     

Reliability Analysis of Multiple Causes of Failure in Presence of Independent Competing Risks

The failures of complex systems always arise from different causes in reliability test. However, it is difficult to evaluate the failure effect caused by a specific cause in presence of other causes. Therefore, a generalize reliability analysis model, which takes into account of the multiple competing causes, is highly needed. This paper develops a statistical reliability analysis procedure to investigate the reliability characteristics of multiple failure causes under independent competing risks. We mainly consider the case when the lifetime data follow log‐location‐scale distributions and may also be right‐censored. Maximum likelihood (ML) estimators of unknown parameters are derived by applying the Newton–Raphson method. With the large‐sample assumption, the normal approximation of the ML estimators is used to construct the asymptotic confidence intervals in which the standard error of the variance‐covariance matrix is calculated by using the delta method. In particular, the Akaike information criterion is utilized to determine the appropriate fitted distribution for each cause of failure. An illustrative numerical experiment about the fuel cell engine (FCE) is presented to demonstrate the feasibility and effectiveness of the proposed model. The results can facilitate continued advancement in reliability prediction and reliability allocation for FCE, and also provide theoretical basis for the application of reliability concepts to many other complex systems. Copyright © 2014 John Wiley & Sons, Ltd.     

Bridging the Gap between Quantitative and Qualitative Accelerated Life Tests

Test planners have long sought the ability to incorporate the results of highly accelerated life testing (HALT) into an early estimate of system reliability. While case studies attest to the effectiveness of HALT in producing reliable products, the capability to translate the test's limited failure data into a meaningful measure of reliability improvement remains elusive. Further, a review of quality and reliability literature indicates that confusion exists over what defines a HALT and how HALT differs from quantitative accelerated life testing methods. Despite many authors making a clear distinction between qualitative and quantitative accelerated life tests, an explanation as to why this delineation exists cannot be found. In this paper, we consider an exemplary HALT composed of a single stressor to show that the HALT philosophy precludes the estimation of a system's hazard rate function parameters because of the test's fix implementation strategy. Four common accelerated failure data analysis methods are highlighted to show their limitations with respect to estimating reliability from HALT data. Finally, a modified accelerated reliability growth test is proposed as a way forward for future research in HALT scenarios to characterize the risk of attaining a reliability requirement and improve parameter estimation. Copyright © 2014 John Wiley & Sons, Ltd.     

用加速寿命试验法研究瓦楞纸箱寿命初探

选用空气相对湿度H和垂直载荷G为加速应力，用加速寿命试验法对瓦楞纸箱寿命进行研究，建立了瓦楞纸箱双应力加速方程，并计算出正常工作应力下瓦楞纸箱的平均寿命及可靠度。     

Lower Percentile Estimation of Accelerated Life Tests with Nonconstant Scale Parameter

Lower percentiles of product lifetime are useful for engineers to understand product failure, and avoiding costly product failure claims. This paper proposes a percentile re‐parameterization model to help reliability engineers obtain a better lower percentile estimation of accelerated life tests under Weibull distribution. A log transformation is made with the Weibull distribution to a smallest extreme value distribution. The location parameter of the smallest extreme value distribution is re‐parameterized by a particular 100pth percentile, and the scale parameter is assumed to be nonconstant. Maximum likelihood estimates of the model parameters are derived. The confidence intervals of the percentiles are constructed based on the parametric and nonparametric bootstrap method. An illustrative example and a simulation study are presented to show the appropriateness of the method. The simulation results show that the re‐parameterization model performs better compared with the traditional model in the estimation of lower percentiles, in terms of Relative Bias and Relative Root Mean Squared Error. Copyright © 2017 John Wiley & Sons, Ltd.     

Life Tests Under Dependent Competing Causes of Failure

In a life testing situation the failure of an individual, either a living organism or an inanimate object, may be classified into one of k(< 1) mutually exclusive classes, usually causes of failure. One often has dependent causes of failure in actual physical situations, i.e., the theoretical lifetime of an individual failing from one cause may be correlated with the theoretical lifetime of the same individual failing from a different cause. This paper i) discusses some properties of a bivariate Weibull distribution and ii) is concerned with estimating, by the method of maximum likelihood, the unknown parameters of life distributions belonging to two particular parametric families, viz., bivariate normal and bivariate Weibull, when the causes of failure are dependent. An example involving the failure of small electrical appliances is analyzed and compared with an analysis which assumes the causes of failure to be independent.     

竞争失效场合Weibull产品的可靠性分析

在竞争失效场合下,建立了Weibull产品的具有二项移走的逐步Ⅱ型截尾寿命试验模型.针对一般近似算法在小样本情形下精度较低的问题,以Gamma分布综合产品的先验信息,采用Gibbs抽样建立了参数的Bayes估计.利用Bootstrap方法、Bayes后验分布法分别构造了参数的置信区间.最后,利用随机模拟方法对估计结果进行了比较,结果表明在小样本情形下,Bayes比MLEs估计效果更好.     

Accelerated Life Testing (ALT) Design Based on Computational Reliability Analysis

Accelerated life testing (ALT) design is usually performed based on assumptions of life distributions, stress–life relationship, and empirical reliability models. Time‐dependent reliability analysis on the other hand seeks to predict product and system life distribution based on physics‐informed simulation models. This paper proposes an ALT design framework that takes advantages of both types of analyses. For a given testing plan, the corresponding life distributions under different stress levels are estimated based on time‐dependent reliability analysis. Because both aleatory and epistemic uncertainty sources are involved in the reliability analysis, ALT data is used in this paper to update the epistemic uncertainty using Bayesian statistics. The variance of reliability estimation at the nominal stress level is then estimated based on the updated time‐dependent reliability analysis model. A design optimization model is formulated to minimize the overall expected testing cost with constraint on confidence of variance of the reliability estimate. Computational effort for solving the optimization model is minimized in three directions: (i) efficient time‐dependent reliability analysis method; (ii) a surrogate model is constructed for time‐dependent reliability under different stress levels; and (iii) the ALT design optimization model is decoupled into a deterministic design optimization model and a probabilistic analysis model. A cantilever beam and a helicopter rotor hub are used to demonstrate the proposed method. The results show the effectiveness of the proposed ALT design optimization model. Copyright © 2015 John Wiley & Sons, Ltd.