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。     

小样本下基于竞争失效的轴承可靠性评估

轴承是机械产品中重要的组成部分,其性能和寿命与机械设备的运行寿命密切相关。为获得准确的轴承可靠性评估结果,应当综合考虑不同失效模式对轴承可靠性的影响。针对轴承试验中完全失效的数据,利用Bootstrap法构造轴承寿命分布参数的先验分布,依据Bayes法估计出相应的后验分布,并对后验期望修偏即可获得轴承寿命分布参数。通过进一步分析轴承的振动性能退化数据,获得轴承局部失效的寿命分布。利用Copula函数对轴承的完全失效的寿命分布模型和局部失效寿命模型进行综合分析,借助试验数据点的经验Kendall相关秩估计Copula函数的相关参数,求得轴承竞争失效下的可靠性分析结果,获得的分析结果有助于寻找轴承设计过程中存在的缺陷,提高轴承的可靠度。     

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。     

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.     

TNT基炸药储存寿命评估研究?

为解决有效评估炸药储存寿命的难题,提出一套基于炸药加速退化试验失重率阈值的储存寿命评估技术。采用基于伪失效寿命的加速寿命试验数据建模与处理方法,对某TNT基炸药加速退化试验数据,使用正态分布和威布尔分布模型,估计了两种模型参数,评定了其储存寿命。研究表明,该评估技术可行,结论可信。     

基于双方差随机过程的半导体激光器寿命评估

针对高可靠、长寿命半导体激光器的寿命评估问题,提出了基于双方差随机过程的性能退化评估方法。该方法不仅考虑了半导体激光器内部失效机理的固有随机性,还考虑了由人为因素、测量仪器等引起的测量随机误差。首先,建立了半导体激光器性能退化模型及其未知参数的极大似然估计方法。然后,基于首达时的概念给出了失效时间分布函数和概率密度函数的解析表达式,以对半导体激光器的可靠性和寿命进行评估。最后,通过半导体激光器寿命评估工程实例验证了所提出方法的适用性和有效性。结果表明：与现有的性能退化模型相比,所构建模型的拟合效果更好,能够提高寿命评估精度。这可为半导体激光器及其整机系统最优维修决策的制定提供有力支撑。     

基于电子产品板级加速退化数据的可靠性分析

以某24 V-2 A稳压电源板在80 C,100 C,120 C下进行恒加加速退化试验为例,观测到电源板输出电压随温度变化的退化过程,由B-S模型采用回归方法进行了可靠性统计推断,并与其它评估结果比较.结论表明,基于加速性能退化数据进行板级电子产品可靠性评估方法可行、结果可信,且更节约试验成本和时间,无继往数据和无失效数据情况下仍能适用.     

CRH380B系列动车组继电器寿命评估技术研究

动车组继电器是列车的主要元器件之一,其寿命的评估对动车组的安全正常运行至关重要。利用Fick扩散理论和Larson-Miller分析法建立继电器的接触电阻和吸合时间性能退化模型。利用试验台对动车组未安装使用、已运营1 200 000 km和故障3种类型的继电器进行可靠性寿命试验,根据实验数据并利用Matlab中最小二乘法非线性拟合对退化模型中的未知参数进行估计。根据SSE之和最小原则选取继电器特性参数的最优退化模型,根据特性参数的失效阈值得到不同试验温度下的寿命特征量,并利用该寿命特征量,对阿伦尼斯模型中的未知量进行参数估计,实现继电器在常温下的可靠性寿命评估。最后通过试验验证该方法的正确性。研究结果为CRH380B型动车组继电器的安全运用和维护维修提供依据。     

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。     

基于逆高斯过程的竞争失效建模研究北大核心CSSCI

考虑在逆高斯退化过程中发生突发失效,退化失效与突发失效相互竞争导致产品失效的情况,并且考虑突发失效与退化失效的相关性,建立基于逆高斯退化过程的竞争失效可靠性评估模型。采用极大似然估计法对模型进行了参数估计。最终通过某退化试验数据验证了模型的有效性。     

A novel pseudo‐maximum likelihood estimation method for accelerated degradation tests under Wiener process

For reliability assessment based on accelerated degradation tests (ADTs), an appropriate parameter estimation method is very important because it affects the extrapolation and prediction accuracy. The well‐adopted maximum likelihood estimation (MLE) method focuses on interpolation fitting and obtains results via maximizing the likelihood of the observations. However, a best interpolation fitting does not necessarily yield a best extrapolation. In this paper, therefore, a pseudo‐MLE (P‐MLE) method is proposed to improve the prediction accuracy of constant‐stress ADTs by considering the degradation mechanism equivalence under Wiener process. In particular, the degradation mechanism equivalence is characterized by a mechanism equivalence factor which presents the proportional relationship between degradation rate and variation. Then, the mechanism equivalence factor is determined via a two‐step method. The other model parameters can be estimated by the general MLE method. The asymptotic variances of acceleration factors and the p‐quantile of product failure time under normal condition are adopted to compare the statistical properties of the proposed method and the general MLE approach. Numerical examples show that the novel P‐MLE method may not achieve a maximum likelihood but can provide more benefits regarding prediction accuracy enhancement especially when the sample size is limited.     

Lifetime prediction and reliability estimation methodology for Stirling-type pulse tube refrigerators by gaseous contamination accelerated degradation testing

Lifetime and reliability are the two performance parameters of premium importance for modern space Stirling-type pulse tube refrigerators (SPTRs), which are required to operate in excess of 10 years. Demonstration of these parameters provides a significant challenge. This paper proposes a lifetime prediction and reliability estimation method that utilizes accelerated degradation testing (ADT) for SPTRs related to gaseous contamination failure. The method was experimentally validated via three groups of gaseous contamination ADT. First, the performance degradation model based on mechanism of contamination failure and material outgassing characteristics of SPTRs was established. Next, a preliminary test was performed to determine whether the mechanism of contamination failure of the SPTRs during ADT is consistent with normal life testing. Subsequently, the experimental program of ADT was designed for SPTRs. Then, three groups of gaseous contamination ADT were performed at elevated ambient temperatures of 40 °C, 50 °C, and 60 °C, respectively and the estimated lifetimes of the SPTRs under normal condition were obtained through acceleration model (Arrhenius model). The results show good fitting of the degradation model with the experimental data. Finally, we obtained the reliability estimation of SPTRs through using the Weibull distribution. The proposed novel methodology enables us to take less than one year time to estimate the reliability of the SPTRs designed for more than 10 years.     

Using degradation data to assess reliability: a case study on train wheel degradation

Degradation experiments are usually used to assess the lifetime distribution of highly reliable products, which are not likely to fail under the traditional life tests or accelerated life tests. In such cases, if there exist product characteristics whose degradation over time can be related to reliability, then collecting ‘degradation data’ can provide information about product reliability. In general, the degradation data are modeled by a nonlinear regression model with random coefficients. If we can obtain the estimates of parameters under the model, then the failure‐time distribution can be estimated. In order to estimate those parameters, three basic methods are available, namely, the analytical, numerical and the approximate. They are chosen according to the complexity of the degradation path model used in the analysis. In this paper, the numerical and the approximate methods are compared in a simulation study, assuming a simple linear degradation path model. A comparison with traditional failure‐time analysis is also performed. The mean‐squared error of the estimated 100pth percentile of the lifetime distribution is evaluated for each one of the approaches. The approaches are applied to a real degradation data set. Copyright © 2008 John Wiley & Sons, Ltd.     

Analyzing Degradation by an Independent Increment Process

There is no denying that degradation test is a widely used technique to assess the life‐time information for complex systems and highly reliable products. In this paper, motivated by laser degradation data, valve recession data and independent increment process theory, we propose an independent increment random process method, in which linear mean and standard deviation functions are used to describe the degradation procedure. A one‐stage maximum likelihood estimation of parameters is established. Product's mean‐time‐to‐failure and percentile of the failure time distribution are also derived. The proposed method is illustrated and verified in practice degradation analyzing for the two motivating data sets. It shows that the model can provide reasonable results. Copyright © 2013 John Wiley & Sons, Ltd.     

Two optimized models of life prediction based on luminance degradation for VFD under a conventional life test

Focusing on improving the accuracy of existing life prediction models for optoelectronic products, the three‐parameter Weibull right approximation method (TPWRAM) was employed to substitute exponential function based on the least square method in the analysis and two‐staged methods. Two optimized models were established (Model I and Model II), based on maximum likelihood estimation and the Monte Carlo method, respectively. One group of conventional life tests (CLTs) of vacuum fluorescent display (VFD) were conducted to collect luminance degradation data for each sample, and the two optimized models were applied to achieve VFD life prediction and obtain mean time to failure, median life, and confidence intervals. The results indicate that the CLT test design is correct and feasible, the amount of data on luminance degradation is large, and the test data selection method is reasonable. Model I and Model II optimized by TPWRAM both reflect the VFD luminance variation law well, and the predicted life approaches VFD service life from user feedback, proving that the two models are precise, and thus, can provide technical references for researchers and engineers regarding aspects of life prediction.     

Analysis of correlated multivariate degradation data in accelerated reliability growth

Modern engineering systems have become increasingly complex and at the same time are expected to be developed faster. To shorten the product development time, organizations commonly conduct accelerated testing on a small number of units to help identify failure modes and assess reliability. Many times design changes are made to mitigate or reduce the likelihood of such failure modes. Since failure-time data are often scarce in reliability growth programs, existing statistical approaches used for predicting the reliability of a system about to enter the field are faced with significant challenges. In this work, a statistical model is proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. The model allows the components in the system to have multiple failure modes, each associated with a monotone stochastic degradation process. To take into account unit-to-unit variation, the random effects of degradation parameters are explicitly modeled. Moreover, a mean-degradation-stress relationship is introduced to quantify the effects of different accelerating variables on the degradation processes, and a copula function is utilized to model the dependency among different degradation processes. Both a maximum likelihood (ML) procedure and a Bayesian alternative are developed for parameter estimation in a two-stage process. A numerical study illustrates the use of the proposed model and identifies the cases where the Bayesian method is preferred and where it is better to use the ML alternative.     

融合多源数据的非线性退化建模与剩余寿命预测

针对目前基于单个传感器剩余寿命预测方法存在预测精度不高的问题,该文提出一种融合多源传感器数据的非线性退化建模与剩余寿命预测方法。该方法包括复合健康指标的构建、模型参数的估计和传感器融合系数的确定,在确定融合系数后,结合设备历史寿命数据与实时监测数据,利用Bayesian参数更新公式推导出设备的剩余寿命概率分布,实现设备的剩余寿命在线预测。最后通过由商用模块化航空推进系统仿真生成的发动机退化数据集进行仿真实验,结果表明该文所提方法能够有效提高设备剩余寿命预测的准确性。     

自变量含有类型变量的线性回归模型的树方法

给出了求解自变量含有类型变量的线性回归模型的树方法。它是一个非参数方法。讨论了修剪树对参数估计和预测的影响，给出了通过修剪树提高参数估计和预测精度的充要条件。     

Engineering‐driven performance degradation analysis of hydraulic piston pump based on the inverse Gaussian process

As a key aircraft component, hydraulic piston pumps must be developed with high reliability. However, collecting failure time data of such pumps for reliability analysis is a big challenge. To save testing time, performance degradation data obtained from degradation tests can be used for quick reliability estimation of hydraulic piston pumps. This paper proposes an engineering‐driven performance degradation analysis method considering the nature of mechanical wear of hydraulic piston pumps. First, the failure mechanism of a type of hydraulic piston pump is investigated. By taking into account the close relationship between the degradation rate and the failure mechanism, an inverse Gaussian (IG) process model with a variable rate is developed to describe the degradation behavior of the pump. Under this model, a Bayesian statistical method is developed for degradation data analysis. The corresponding procedure for model parameter estimation and reliability evaluation is also presented. The proposed degradation analysis method is illustrated using a real experimental data. The results show that the engineering‐driven approach is quite effective in evaluating the lifetime of the hydraulic piston pump and will improve the overall reliability of aircraft operation in the field.