雷达板级双应力交叉步降加速退化试验优化设计

针对某型雷达板级的性能往往受到多个应力的影响,且在加速退化试验中该产品有限试验时间内难以获得大量性能退化信息的问题,提出一种雷达板级双应力交叉步降加速退化试验优化设计方法。采用Monte-Carlo对加速试验进行仿真模拟,在样本量大小一定的条件下,以监测频率、应力水平数、监测次数作为设计变量,以总的试验费用作为约束条件,以正常使用应力下的p阶分位寿命渐进方差估计作为目标函数,建立下双应力交叉步降加速退化试验优化设计模型。通过仿真实例,验证了该方法的有效性、可行性。     

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。     

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。     

电应力加速退化试验技术及可靠性评估研究

为探究电冲击对电子装备寿命的影响,选取电源通断瞬间产生的高压为电冲击加速应力,并以雷达某系统功能电路板为研究对象,开展电应力加速退化试验。通过对试验数据的统计分析,得到基于性能退化轨迹的伪失效寿命,并在Weibull分布条件下进行参数估计,提出基于电应力的电源开关通断电加速模型,并实现对某型雷达功能电路板的可靠性评估。     

航天电连接器加速性能退化试验可行性研究

为解决高可靠长寿命的航天电连接器即使进行加速寿命试验也难以获得失效数据的问题,对电连接器失效模式以及长期贮存下接触性能变化结果进行分析,表明电连接器性能退化存在可能性,并根据长期贮存下试验数据的验证,得出电连接器性能具有退化特性.通过温度加速应力下电连接器接触性能数据的分析,得出其性能退化具有加速性,还具有一定的加速退化规律,这为采用加速性能退化试验对航天电连接器进行可靠性评估提供了依据.     

基于ADT的电阻型湿度传感器可靠性评估

传统的寿命试验对电子元件可靠性进行评估需较长时间。如何快速、准确获取电子元件的性能指标是工程实践和试验研究迫切需要解决的问题。本文以电阻型湿度传感器为研究对象,基于加速退化试验(ADT)的可靠性评估方法,建立加速模型和退化轨迹,结合最大似然估计和最小二乘法求解加速应力下伪失效寿命分布参数,从而得出正常应力水平下可靠度函数。结果表明,采用ADT能够准确获取湿度传感器的可靠性信息,缩短试验周期,此评估方法同样适用于其它电子元件的可靠性研究,存在广泛的应用价值。     

基于风险分析的可靠性指标加速验证方法研究

传统的可靠性验证试验由于一般在使用条件下进行,且只利用失效数据进行判定,往往需要大量的时间和费用。这对于高可靠长寿命船舶设备可靠性指标的验证是不可接受的。本文提出一种基于风险分析的可靠性指标加速验证方法,该方法利用退化数据进行分析,在试验风险可接受的条件下提前截止试验,得出验证结果。同时,利用高环境应力可以加速退化过程,进一步减少试验时间。本文具体给出了分析和计算提前截止试验和利用高应力进行试验产生的额外风险的方法,保证了验证结果的真实可靠。最后利用案例证实了该方法在减少验证试验时间上的有效性。     

基于伪寿命分布的电主轴极小子样可靠性研究

在分析和研究了电主轴性能退化失效机理的前提下,选取合适的性能退化特征量,在此条件下设计电主轴可靠性实验方案,并在该方案的指导下进行电主轴可靠性实验。在研究了基于伪寿命分布的退化数据可靠性评估方法的基本原理基础上,应用该方法结合电主轴实验样本的可靠性实验数据,综合虚拟增广样本法对其可靠性进行评估。最终获得失效分布函数、分布密度函数及其曲线,以及一系列可靠性指标,包括可靠度函数及其曲线、失效率函数及其曲线、平均寿命及部分可靠寿命等。     

基于ARMA模型的加速退化试验可靠性评估

有限时间内很难获得大量电子产品的失效数据,传统可靠性评估方法存在一定局限。在分析传统加速退化试验可靠性评估的基础之上,提出利用时间序列对产品加速退化过程描述的方法,使用自回归滑动平均混合(ARMA)模型对退化数据进行建模,通过参数估计得到其退化规律的表达式,从而外推出其失效寿命,进而利用极大似然估计理论进行可靠性评估。最后以某电源电路板加速退化试验数据为例,分时间序列建模和可靠性评估两大步骤,分别对95℃、105℃、115℃下加速退化试验数据进行分析处理,得出可靠性评估结果,验证该方法的有效性和实用性。     

小波包-支持向量数据描述在轴承性能退化评估中的应用研究

轴承是旋转机械中的关键部件,相对于故障模式识别,性能退化评估可以更为有效地服务于设备主动维护以实现零停机率。小波包分解可以对信号进行更为精细的刻画,基于统计学习理论的支持向量数据描述是一种具有良好计算性能的单值分类方法。基于此,本文提出了一种基于小波包－支持向量数据描述的轴承性能退化评估方法,该方法以小波包分解的节点能量构成特征向量,仅需要正常状态下的数据样本即可用支持向量数据描述建立知识库,在一定程度上实现了对待测样本退化程度的定量评估。通过应用于轴承不同点蚀大小和其加速疲劳寿命试验的全寿命周期,验证了所提出方法的可行性和有效性。     

A literature review on planning and analysis of accelerated testing for reliability assessment

Accelerated testing has been widely used for several decades. Started with accelerated life tests with constant‐stress loadings, more interest has been focused prominently on accelerated degradation tests and time‐varying stress loadings. Because accelerated testing is crucial to the assessment of product reliability and the design of warranty policy, it is important to develop an efficacious test plan that encompasses and addresses important issues, such as design of stress profiles, sample allocation, test duration, measurement frequency, and budget constraint. In recent years, extensive research has been conducted on the optimal design of accelerated testing plans, and the consideration of multiple stresses with interactions has become a big challenge in such experimental designs. The purpose of this study is to provide a comprehensive review of important methods for statistical inference and optimal design of accelerated testing plans by compiling the existing body of knowledge in the area of accelerated testing. In this work, different types of test planning strategies are categorized, and their drawbacks and the research trends are provided to assist researchers and practitioners in conducting new research in this area.     

Optimal Design of Step‐stress Accelerated Degradation Test with Multiple Stresses and Multiple Degradation Measures

Products with high reliability and long lifetimes undergo different types of stresses in use conditions. Often there are multiple performance indicators for products that gradually degrade over time. An accelerated degradation test (ADT) with multiple stresses and multiple degradation measures (MSMDM) may provide a more accurate prediction of the lifetime of these products. However, the ADT requires a moderate sample size, which is not practical for newly developed or costly products with only a few available test specimens on hand. Therefore, in this study, a step‐stress ADT (SSADT) with MSMDM is developed. However, it is a difficult endeavor to design SSADT with MSMDM to predict accurate reliability estimation under several constraints. Previous methods are used only for cases with a single stress or degradation measure, and are not suitable for SSADT with MSMDM. In this paper, an approach of optimal design is proposed for SSADT with MSMDM and its steps for a rubber sealed O‐ring are demonstrated to illustrate its validity. Results of the sensitivity analysis for the optimal test plan indicate robustness when the deviation of model parameters is within 10% of the estimated values. Copyright © 2017 John Wiley & Sons, Ltd.     

Designing an accelerated degradation experiment by optimizing the estimation of the percentile

Degradation tests are widely used to assess the reliability of highly reliable products which are not likely to fail under traditional life tests or accelerated life tests. However, for some highly reliable products, the degradation may be very slow and hence it is impossible to have a precise assessment within a reasonable amount of testing time. In such cases, an alternative is to use higher stresses to extrapolate the product's reliability at the design stress. This is called an accelerated degradation test (ADT). In conducting an ADT, several decision variables, such s the inspection frequency, sample size and termination time, at each stress level are influential on the experimental efficiency. An inappropriate choice of these decision variables not only wastes experimental resources but also reduces the precision of the estimation of the product's reliability at the use condition. The main purpose of this paper is to deal with the problem of designing an ADT. By using the criterion of minimizing the mean‐squared error of the estimated 100 th percentile of the product's lifetime distribution at the use condition subject to the constraint that the total experimental cost does not exceed a predetermined budget, a nonlinear integer programming problem is built to derive the optimal combination of the sample size, inspection frequency and the termination time at each stress level. A numerical example is provided to illustrate the proposed method. Copyright © 2003 John Wiley & Sons, Ltd.     

Equivalence analysis of accelerated degradation mechanism based on stochastic degradation models

For an effective accelerated degradation test, it is important to ensure that the degradation mechanism under different stress levels remains unchanged. In this article, we are interested in the equivalence analysis of accelerated degradation mechanism based on degradation data rather than physical or chemical techniques. Under the assumption that products' underlying degradation follows stochastic degradation models, we first introduce the relationship between mechanism equivalence and parameters of stochastic degradation models based on the acceleration factor invariant principle. Then the necessary conditions for mechanism equivalence, which should be satisfied under different stress levels, are derived and tested by the proposed parameter equivalence test method based on the modified Bartlett statistic and T statistic. Next a novel selection method for stochastic degradation models is derived therefrom by comparing the variation of coefficients of acceleration factors. The accuracy of the necessary conditions and the parameter equivalence test method is demonstrated through a simulation study. In addition, an electrical connector example with real stress relaxation data is analyzed to illustrate the proposed method further.     

COMPARISONS OF OPTIMAL ACCELERATED TEST PLANS FOR ESTIMATING QUANTILES OF LIFETIME DISTRIBUTION AT THE USE CONDITION

Accelerated life tests (ALTs) and accelerated degradation tests (ADTs) are widely used for the reliability assessment of components or materials. In an ALT, failure times of test units are observed while in an ADT the failure-causing performance characteristic is measured. This article develops optimal ALT and ADT plans for estimating the qth Quantile of the lifetime distribution at the use condition, the latter being an extension of Park and Yum. Then, the two test plans are compared in terms of the asymptotic efficiency in estimating the qth quantile and of the robustness to the mis-specifications of failure probabilities. Computational results show that the ADT provides a more precise estimator than the corresponding ALT, especially when the failure probabilities are small. Concerning the robustness of a test plan to the departures of the guessed failure probabilities from their true values, neither plan completely dominates the other.     

Robustness of asymptotic accelerated life test plans to small‐sample settings

Much of the statistical literature on optimal test planning for accelerated life testing utilize asymptotic methods to derive optimal test plans. While sufficient effort is made to assess the robustness of these test plans to the choice of design parameters and distribution assumptions, there is very little literature on the performance of asymptotic test plans relative to small samples (on the order of 10‐15 samples). An alternative concern is that the asymptotic test plans may not necessarily be the true “optimal” test plan for a given sample size. The purpose of this research is to present exact or “near‐exact” methods for developing test plans and compare the performance of these test plans with corresponding asymptotic test plans in small‐sample settings. The optimal location of design points and sample allocation is determined using each method for lognormal and Weibull lifetime distributions with both complete and Type 1 right‐censored data under two selected acceleration factor models. The investigations reveal that asymptotic test plans tend to corroborate quite well with exact test plans and thus are suitably robust to small‐sample settings in terms of optimal variance.     

A Bayesian Predictive Analysis of Step‐Stress Accelerated Tests in Gamma Degradation‐Based Processes

Degradation modeling might be an alternative to the conventional life test in reliability assessment for high quality products. This paper develops a Bayesian approach to the step‐stress accelerated degradation test. Reliability inference of the population is made based on the posterior distribution of the underlying parameters with the aid of Markov chain Monte Carlo method. Further sequential reliability inference on individual product under normal condition is also proposed. Simulation study and an illustrative example are presented to show the appropriateness of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.