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.     

高速InGaAs光电探测器可靠性寿命评估

随着航天应用高速InGaAs探测器的迅速发展,其可靠性问题日益突出,本文选择温度应力与光应力为加速应力,通过爱林模型与恒定应力加速寿命试验方法对XX型高速InGaAs探测器进行了研究,获得了InGaAs探测器在4种组合应力下的退化数据与寿命模型,进而推算出器件在正常工作状态下的寿命。     

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。     

LED光源双应力交叉加速寿命试验

在LED光源的特性和寿命试验原理的基础上,基于单一应力的加速寿命测试方法,结合温度应力与电应力,提出LED光源双应力交叉加速寿命试验的方法,并详细阐述了加速寿命试验与测量系统架设,建立寿命试验的数学模型,根据参数测量与分步计算进行寿命评估与可靠性分析。该方法可大大缩短试验周期,具有较高的实用性,为LED光源的寿命评估和可靠性分析提供一种高效实用的方法。     

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。     

基于四组加速退化试验的VFD寿命预测

为在短时间内准确获得真空荧光显示器的寿命信息,该文在提高阴极灯丝温度的基础上开展四组恒定应力作用下的加速退化试验,利用加速时间与加速系数的关系将加速应下的试验数据转化为常规应力下的亮度衰减数据,基于三参数威布尔函数和右逼近法建立寿命预测模型,从而实现寿命的估算。结果表明,加速退化试验方案切实可行,基于亮度衰减的寿命模型不仅准确揭示亮度随时间的变化规律,而且具有较高的预测精度,可为制造商和用户提供技术参考,并可推广到其他光电产品。     

性能退化量分布LED可靠性评估

针对LED可靠性评估的方法大部分均基于样品伪寿命这一现象,提出基于退化量分布的LED可靠性评估方法。先依据指数模型预测后续时刻的退化量,再通过β分布统示法求出各时刻的累积失效概率,然后根据加速模型确定常应力下LED的可靠度函数。以国内某型LED灯具为实验对象,求出该型灯具寿命估计值为77100h,验证方法的实用性、有效性。     

小样本条件下双应力步降加速退化试验优化设计

针对小样本条件下高可靠性长寿命产品的性能往往受到多个应力的影响,且在加速退化试验中该类产品在有限试验时间内难以获得大量性能退化信息的问题,提出一种小样本条件下双应力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,建立小样本条件下双应力步降加速退化试验优化设计模型。通过仿真实例,验证该方法的有效性、可行性。     

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。     

Integration method for reliability assessment with multi-source incomplete accelerated degradation testing data

ABSTRACT

During the product life cycle, the lifetime information will be collected at each stage, mainly from different tests at the R&D phase, field usage, and maintenance. To comprehensively conduct reliability assessments, it generally requires the integration of multi-source datasets, even that from similar products. In this article, we considered the scenario that products have been arranged with several accelerated degradation tests (ADT) under different types of accelerated stresses with dependency. The obtained data is called incomplete ADT dataset with incomplete stress conditions which fails the traditional integration method for reliability assessments. A novel method is proposed to accomplish this task through mutually exclusive set (MES) theory. The probability assignments for each dataset are given through the union set of several MESs. Then, the multi-source ADT datasets are integrated with the assigned weights of probabilities. Finally, a simulation study and a real application are given to illustrate the effectiveness of the proposed methodology.     

Utilizing Accelerated Degradation and Field Data for Life Prediction of Highly Reliable Products

For newly developed, highly reliable, and long‐lifespan products, it is quite difficult to implement effective remaining useful life (RUL) prediction in the early usage under limited time cost. However, accelerated degradation testing (ADT) is generally used for lifetime evaluation for such products with harsher test conditions and shorter test time in the late research and development phase. Thus, in this paper, we propose a life prediction framework to integrate the information from ADT to conduct field RUL prediction for highly reliable products. Because ADT belongs to reliability testing used for inferring the population information from the selected test samples, we at first present the modified Wiener process (MWP) model. Different from traditional methods that embody both the random variability and unit‐to‐unit variability into the diffusion coefficient, the proposed method describes them separately in ADT analysis. Then, the MWP model from ADT is used as a prior for field RUL prediction of the target product during which the strong tracking filtering algorithm is introduced for updating the hidden state and computing the RUL prediction results when the new monitoring data are available. Because of the complexity of the MWP model, the Markov chain Monte Carlo method is provided to estimate the unknown parameters. Finally, the simulation study and the light‐emitting diode application verify the effectiveness of the proposed framework that can achieve reasonable life prediction results for highly reliable products for both linear and nonlinear scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

Degradation Tests Using Geometric Brownian Motion Process for Lumen Degradation Data

Running a traditional life test over an affordable time period with highly reliable products is inefficient to collect the lifetime information of products even if the products are subject to higher stress conditions. This fact makes it difficult to infer the reliability of highly reliable products. The accelerated degradation test (ADT) method has been suggested as an alternative to infer the reliability of highly reliable product based on its degradation measurements. The current study is motivated by the statistical modeling of the lumen degradation date set of transistor outline can packaged light emitting diodes (LEDs). All degradation measurements were collected from an ADT, which was conducted with two stress loadings, the ambient temperature and drive current. To study the reliability of the LEDs under the ADT, the geometric Brownian motion process and generalized Eyring model are applied to estimate the distribution parameters and percentiles of the LEDs. Planning strategies of the sample size and measurement times for the proposed ADT are established to minimize the asymptotic variance of maximum‐likelihood estimator of the lower 100pth percentile of LED lifetimes under the given budget. An algorithm is provided to reach the planning strategy. The guidelines of this study can be extended to infer the reliability of other highly reliable product besides LEDs. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Acceleration Factor Constant Principle and the Application under ADT

Accelerated degradation test (ADT) has become an efficient approach to evaluate the reliability for highly reliable products. However, when modeling accelerated degradation data with degradation models, it is difficult to exactly figure out the changing rules of parameters with stress variables varying. At present, the changing rules are mainly assumed according to engineering experience or subjective judgements, which probably results in inaccurately extrapolating the reliability. To figure out the changing rules of parameters with stress variables varying, the acceleration factor constant principle and its application under ADT are studied in the paper. It is well known that the acceleration factor between any two different stress levels should be a constant under an effective ADT. For each degradation model, its parameters should obey special changing rules to satisfy that the acceleration factor maintains a constant throughout an ADT. Taking three extensively used stochastic process models as examples, including Wiener process model, gamma process model, and inverse Gaussian process model, the method of deducing the changing rules of the parameters according to the acceleration factor constant principle was demonstrated. A simulation test was conducted to validate the deduced changing rules of the parameters for the three stochastic process models. An illustrative example involving self‐regulating heating cables was used to illustrate the application of the acceleration factor constant principle under ADT. Results indicate that the acceleration factor constant principle offers an appealing and credible approach to help model accelerated degradation data. Copyright © 2016 John Wiley & Sons, Ltd.     

A Bayes approach to reliability prediction utilizing data from accelerated life tests and field failure observations

A Bayes approach is proposed to improve product reliability prediction by integrating failure information from both the field performance data and the accelerated life testing data. It is found that a product's field failure characteristic may not be directly extrapolated from the accelerated life testing results because of the variation of field use condition that cannot be replicated in the lab‐test environment. A calibration factor is introduced to model the effect of uncertainty of field stress on product lifetime. It is useful when the field performance of a new product needs to be inferred from its accelerated life test results and this product will be used in the same environment where the field failure data of older products are available. The proposed Bayes approach provides a proper mechanism of fusing information from various sources. The statistical inference procedure is carried out through the Markov chain Monte Carlo method. An example of an electronic device is provided to illustrate the use of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

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。     

Reliability Estimation for Accelerated Life Tests Based on a Cox Proportional Hazard Model with Error Effect

In this paper, a Cox proportional hazard model with error effect applied on the study of an accelerated life test is investigated. Statistical inference under Bayesian methods by using the Markov chain Monte Carlo techniques is performed in order to estimate the parameters involved in the model and predict reliability in an accelerated life testing. The proposed model is applied to the analysis of the knock sensor failure time data in which some observations in the data are censored. The failure times at a constant stress level are assumed to be from a Weibull distribution. The analysis of the failure time data from an accelerated life test is used for the posterior estimation of parameters and prediction of the reliability function as well as the comparisons with the classical results from the maximum likelihood estimation. Copyright © 2017 John Wiley & Sons, Ltd.