可靠性评估中利用先验信息的一种新思路

Bayes分析的一般程序是先验分布 数据=后验分布.提出一种新的Bayes分析程序:先基于数据诱导出参数的置信分布,而将参数的原先验分布等效成某种类型数据;然后将置信分布作为参数的"先验分布",而将先验信息的等效数据作为"试验数据";最后采用通常的Bayes方法进行可靠性评估.该新思路主要用于多参数寿命分布的可靠性评估.它克服了多参数模型下确定先验分布的困难及计算上的困难.仿真结果表明该方法具有较高的精度,且计算简单,可以应用于实际.     

融合非线性加速退化模型与失效率模型的产品寿命预测方法

针对高可靠性产品寿命数据少、获取成本高的问题,基于充分利用产品在研制、加速试验等不同环境下的退化数据、失效数据等可靠性数据的思想,提出了一种融合非线性加速退化模型和失效率模型的产品寿命预测方法.首先,根据退化数据对非线性退化过程进行分析,估计退化过程的参数;然后,根据加速退化数据及相应的加速退化模型估计加速退化模型的参数,从而得到退化参数与应力之间的关系.进一步,利用比例风险模型融合产品的寿命数据和未失效截尾数据,并基于此计算产品的可靠度函数、预测产品的寿命.实例应用验证了所提方法的有效性,同时说明了所提方法的应用价值.     

基于加速因子不变原则的加速退化数据分析方法

加速退化试验技术已经成为评估退化失效型产品可靠性的高效手段,然而,目前对加速退化数据分析时过多依据主观经验,容易造成可靠性评估结果不准.本文提出了基于加速因子不变原则较为客观分析加速退化数据的一种方法.首先,根据加速因子不变原则推导退化模型各参数在加速退化试验中应该满足的变化规律;然后,利用与加速应力无关的参数等式辨识各加应力水平下的加速退化数据是否有效,核心是构建t统计量检验参数估值是否满足等式关系;接下来,确定与加速应力相关的参数从而实现加速退化建模;最后,利用有效的加速退化数据估计出模型参数值,外推出产品在常规应力下的可靠度.以逆高斯退化模型为例对所提方法进行了具体阐述.仿真试验和实例应用表明,本文研究为基于加速退化数据的可靠性评估提供一种更客观、合理的技术途径.     

具有离群值的性能退化产品可靠性评估

运用性能退化数据对高可靠长寿命产品进行可靠性评估整体效果良好。但是,在进行产品性能检测过程中,由于随机误差的影响,导致性能退化数据中可能存在一些离群值,使得评估结果不够稳健。在这种情况下,利用模糊聚类的最小二乘估计方法对退化轨迹参数进行估计,从而削弱离群值的影响,提高了评估的精度。本文利用实例进行分析,验证了方法的正确性与有效性。     

不同的退化模式共存时产品退化失效分析

在高可靠性、长寿命产品的可靠性分析中,当产品的失效为退化型失效时,利用产品的性能退化数据进行可靠性分析是一种更合理的方法。在考虑产品既存在平稳退化,又存在随机退化时,研究了产品退化失效的一般模型并给出了模型参数的估计方法。最后,利用所给的模型对强激光装置所用的某型金属化膜脉冲电容器进行了可靠性分析,并验证了该方法的可行性。     

塑封器件可靠性快速评估的研究

本文采用ＰＣＴ试验方法ＳＤ１３００５型ＮＰＮ高反压大功率晶体管进行了加速寿命试验，发现ＢＶｃｅ０是其退化的最敏感参数。并利用自行研究开发的《微电子器个可靠性指标数据处理计算机软件》对试验数据进行统计分析，得知该的失效分布符合威布尔分布，同时评估了不同应力下产品的可靠性寿命特征，外推出１００％相对湿度时，曙下该产品的寿命特征。     

Arrhenius 程应用新方法研究

提出了一种快速评价电子元器件的新方法,该方法具有快速、准确、成本低、效率高等优点.利用Arrhenius方程,能快速准确地确定元器件退化的失效敏感参数和退化机理;可对单样品求出与失效机理相关的失效激活能和寿命;通过多样品试验,可得到寿命分布、寿命加速特性和失效率等可靠性参数.以DC/DC电源变换器和高频小功率管3DG130为例,通过实验与现场数据的对比,证明了新方法的正确性和有效性.该方法适用于失效率优于10-7/h(λ＜10-7/h)的高可靠性产品的定量评价.     

基于平均寿命MSE的步进应力加速退化试验优化设计

步进应力加速退化试验常应用于高可靠性、长寿命产品的可靠性评估。为了更加准确的评估产品的可靠性,论文针对退化过程服从Wiener过程的退化型产品,提出了步进应力加速退化试验优化设计建模与分析方法。该方法在试验总费用约束下,利用蒙特卡洛仿真估计产品平均寿命的均方误差(MSE),并通过最小化MSE,确定最优的步进应力加速退化试验应力水平、样品数、参数测量间隔及测量次数等。最后,以某磁性产品为例,对该方法的有效性进行实例验证。     

融合随机退化过程与失效率建模的设备剩余寿命预测方法

剩余寿命预测对于设备的维修与保养具有十分重要的意义。现有的剩余寿命预测方法大多只利用了设备的当前退化信息,对设备的历史寿命信息没有充分利用,而这些信息往往包含着设备寿命的演化信息,对于准确预测设备的剩余寿命具有重要意义。针对这个问题,提出了一种融合随机退化过程与失效率建模的设备剩余寿命预测方法。该方法首先将设备的退化过程建模为Wiener过程,然后利用Cox比例失效模型建模的方法融合设备退化过程对设备失效率的影响,由此达到利用设备历史监测信息的目的。进一步通过Bayes方法,利用当前退化监测信息对退化过程模型的参数进行更新,基于此进行剩余寿命预测,从而实现设备历史数据与当前数据的有效融合。最后,通过激光发生器的退化测量数据验证了提出的方法,说明该方法是有效的,具有一定的应用价值。     

一种电子设备贝叶斯可靠性评估的新方法

针对贝叶斯方法应用中后验参数的运算复杂性问题,提出了一种电子设备贝叶斯可靠性评估的新方法.基于工程实践中常见的先验信息建立失效率先验分布,通过随机采样构建设备寿命分布参数的离散联合先验分布,结合截尾试验数据,再通过二次随机采样得到分布参数的离散联合后验分布函数.通过实例给出了运算过程,并与其它贝叶斯运算方法进行了比较.结果表明,此方法在确保精度的同时可以大大简化计算过程,在电子设备可靠性评估中有较高的应用价值.     

基于加速性能退化试验的板级可靠性评估

传统的可靠性评估方法一般基于失效寿命数据,而目前对于高可靠长寿命的电子产品,很难通过加速试验获得其失效寿命时间。为解决这一矛盾,将性能退化理论引入到传统可靠性评估中,提出了基于失效数据及加速性能退化的可靠性评估的新方法。应用某型雷达24V/2A稳压电源板加速性能退化试验进行验证,结果表明该方法用于高可靠长寿命电子装备的可靠性评估是正确有效的。     

On Bayesian estimation of system reliability

For a system with n s-independent components, the uncertainty regarding the reliability of components for a fixed point of time is expressed by a Bayesian probability distribution. Using the moments of these distributions, the exact moments of the system reliability distribution are derived, from which a discrete probability density function is obtained on the basis of the principle of maximum entropy. Taking this distribution as a prior distribution for system reliability, a posterior density function for the system reliability is constructed either using the data obtained from life tests conducted at a system level or from field data. For tracking the evolution of the reliability distribution over time, a modified Kalman filter technique, along with use of a Bayesian procedure, is proposed. This method is simple, elegant and easy to compute.     

An engineering approach to Bayes estimation for the Weibull distribution

In this paper an engineering approach to Bayes reliability analysis of Weibull failure data collected under a randomly censored sampling is proposed. The posterior distribution of several decision variables, such as the meanlife, the reliability function, the reliable life, and the hazard rate, are derived, when either a prior information on the reliability or a prior information on the hazard rate is available. Point estimates of the selected decision variables are given, by assuming both symmetric and asymmetric loss functions. Finally, numerical examples are presented to illustrate the proposed estimation procedures.     

Accelerated degradation-tests with tightened critical values

ALT (accelerated life tests) are widely used to provide quickly the information about life distributions of products. Life data at elevated stresses are extrapolated to estimate the life distribution at design stress. The existing estimation methods are efficient and easy to implement-given sufficient life data. However, ALT frequently results in few or no failures at low-level stress, making it difficult to estimate the life distribution. For products whose failures are defined in terms of performance characteristics exceeding their critical values, reliability assessment can be based on degradation measurements by using degradation models. The estimation, however, is usually mathematically complicated and computationally intensive. This paper presents a method for the estimation of life distribution by using life data from degradation measurements. Since the time-to-failure depends on the level of a critical value, more life data can be obtained by tightening the critical value. The relationship between life and critical value and stress is modeled and used to estimate the life distribution at a usual critical value and design stress. The model parameters are estimated by using maximum likelihood. The optimum test plans, which choose the critical values, stress levels, and proportions of sample size to each stress level, are devised by minimizing the asymptotic variance of the mean (log) life at a usual critical value and design stress. The comparison between the proposed and existing 2-level test plans shows that the proposed plans have smaller asymptotic variance and are less sensitive to the uncertainty of the pre-estimates of unknown parameters.     

A Bayesian Reliability Assessment of Complex Systems for Binomial Sampling

This paper presents a reliability assessment procedure that systematically combines complete system binomial test data with lower level binomial test data obtained from either partial system or component tests. The procedure uses beta prior distributions of all reliabilities, Bayes theorem, and probability moments. The result is a posterior distribution of system reliability that can be used to determine Bayes point and interval estimates. The beta prior distributions evolve from data on predecessor systems similar to the system in question and engineering knowledge about what the various test-alternatives measure.     

基于退化量分布的高量程MEMS加速度计的可靠性评估

高量程MEMS微加速度计主要应用于航空、航天及军工等高可靠性要求的惯导领域。为了更好进行微加速度计的可靠性寿命预测,提出一种基于退化量分布,以实验数据拟合推导出分布模型参数的变化关系从而更加准确的评估微加速度计的可靠性寿命。实验研究结论表明采用新的评估方法能够获得更准确的微加速度计可靠性预测。     

Bayes' Equation, Reliability, and Multiple Hypothesis Testing

The rudiments of applying Bayes' Equation to hypotheses concerning reliability are introduced in a simple manner. The application is a means of obtaining posterior probabilities, for the reliability hypotheses, which are consistent with the prior beliefs and the available test results. The posterior distributions, from which decision theory could formally arrive at optimal estimates, are greatly dependent on the prior distributions. Thus, the discussion centers about the desired properties of a prior and its effects on the posterior for various data situations. Formulations for both continuous-conjugate and discrete representations of the prior beliefs are discussed and contrasted. The use of discrete priors offers many advantages over the use of continuous-conjugate priors.     

Bayesian可靠性评估中多源信息融合的概率模型方法

在Bayesian可靠性评估理论中,验前分布的获取和表示是一个关键问题.针对工程实践中遇到的验前信息的多源性.给出了一种基于概率模型的多源异总体验前分布的融合方法,并通过仿真实例证明了该方法的有效性.     

Bayes credibility intervals for the left-truncated exponential distribution

In this paper, a Bayes approach for statistical inference on life characteristics is proposed, when the underlying lifetime distribution has the left-truncated exponential density function. The proposed Bayes procedure provides credibility intervals on several life characteristics of great interest to the applied reliability engineer, when the experimental data are collected under a randomly censored sampling. The prior technical knowledge is expressed in the form of a prior density on the reliability level at a prefixed time in conjunction with an upper bound on the location parameter. The statistical properties of the proposed Bayes procedure are compared, via Monte Carlo simulation, with those of the Bayes procedure under the noninformative prior, when both correct and uncorrect prior information on the reliability is available. A numerical example is used for illustration and comparison.