厚膜混合集成频率合成器的可靠性增长

本文介绍厚膜混合集成频率合成器在研制与生产过程中依照“试验—分析—改进”的方法进行工作。首先预计了集成器件可能达到的可靠性水平。在初试中产品的可靠性水平较低。经过分析修改了设计方案,在试生产中可靠性明显增加,但环境试验又暴露出封装方面的问题。通过试验改进又进行高温电老化筛选,去除了早期失效的产品,经过这一系列工作后当置信度为0.8时,这批产品置信区下限估计MTBTP、已接近3000小时,通过“试验—分析—改进”的几次循环,产品可靠性增长十分显著。提交装机使用的厚膜混合集成频率合成器组件其MTBF约为7.6×10~5小时,并已超过初期预计的可靠性指标,使用反应良好。     

定时截尾试验零故障时的MTBF估计方法

针对产品定时截尾试验中零故障时无法进行平均故障间隔时间(Mean Time Between Failures,MTBF)估计的问题,分析了不同情况下点估计方法选用的注意事项,并通过公式推导得出了零故障时置信下限的计算公式。对置信下限计算公式进一步推导,得到了便于实际应用的简化计算公式。通过在多个可靠性试验中应用该方法进行MTBF估计,结果表明该方法可行有效。     

某型金属软管的可靠性分析

对某些φ８金属软管的完全样本失效数据进行了拟合优度检验与分布鉴别，结合失效的物理过程分析，选定对数正态分布作为其失效分布，据此给出了产品平均寿命。可靠度与可靠寿命的点估计与置信下限     

成败型产品的可靠性估计

估计成败型产品的可靠性．可以先根据试验子梓用数学方法求出可靠性概率分布密度函数，然后对其积分求出其可靠性置信下限．     

Weibull分布航天产品可靠性评估的MMLE-Bayes方法

航天产品现场使用数据具有小子样、零失效的特点，要实现其工程化可靠性评估，需要充分利用各种环境下的试验信息和相似型号产品的试验信息及使用信息，因此，提出了一种适用于Weibull分布航天产品可靠性综合评估的MMLE—Bayes方法。首先通过MMLE方法得到额定任务时间可靠度的置信下限估计，再通过构造合理的验前分布实现Bayes可靠性评估。在此过程中，为合理融合各来源的验前信息，提出了环境因子和相似因子的概念及其估计方法。最后通过一种航天产品的可靠性评估实例，说明了MMLE—Bayes—Weibull方法的的实用性和有效性。     

定时截尾可靠性鉴定试验中参数MTBF的统计推断

用统计学中样本空间排序法对定时截尾可靠性鉴定试验方案中平均故障间隔时间（MT—BF）的统计推断方法进行了理论推导,并给出了置信下限和置信上限系数的表达式,对GJB899—1990中的表达式提出了修订建议。     

基于Bayes和Fiducial方法的指数分布区间估计

在已知产品失效率不超过某个值的条件下,对定时截尾寿命试验,采用Bayes方法进行可靠性评估;对定数截尾寿命试验,采用参数受限制的Fiducial方法进行可靠性评估。根据这两种方法,分别给出了平均寿命单侧置信下限计算公式,同时讨论了先验分布参数的取值方法。最后,通过算例验证了这两种方法的有效性,算例表明两种方法均能够减少试验时间、提高可靠性评估精度。     

电子元器件的可靠性增长方式

电子元器件通常都是批量生产的,所以它们的增长过程一般是分阶段或按试验序列而逐步进行的.对它们进行增长分析时,通常应该采用离散型的增长模型。工程实践上,电子元器件的可靠性增长,可以采取试验比较法、消除失效模法以及阶段序列增长法等各种方式.试验比较法需要通过前后两次试验来分析产品的增长效果。第一次试验的目的,是要掌握产品的可靠性现状,摸清产品的存在问题;第二次试验的目的,则是要验证纠正措施的有效性,并检验其增长效果。     

压电陀螺的可靠性研究

本文分析了压电陀螺的可靠性,报导了压电陀螺的失效机理、失效模式和失效分布的研究结果,以及恒应力寿命试验和加速寿命试验确定的威布尔分布参数;η=8,945小时,t_E=13,912小时,m=0.58;加速度系数τ=3.8。 提高压电陀螺的可靠性研究表明,元器件筛选和改进工艺能降低压电陀螺的失效率;压电陀螺进行早期失效筛选能提高可靠度。 单轴和三轴压电陀螺的MTBF下限分别为9,651小时和3,217小时。洲际导弹用单轴和三轴压电陀螺的可靠度分别为99.82％和99.46％     

MEMS惯性传感器可靠性试验方法研究

MEMS惯性传感器在军事与商业应用中的一个主要问题便是可靠性试验方法尚未标准化,因而目前绝大多数MEMS惯性传感器器件的可靠性试验依据的是傲电子的试验标准。但是,这些标准对于这类器件的适用性却受到许多机构的质疑,国外关于该问题的研究也已起步。汇总了MEMS惯性传感器器件的结构和工作原理等信息,重点总结了该类器件的典型环境失效机理,并将典型的环境载荷情况与失效机理进行了对比分析：从现有的微电子可靠性试验标准中选取了针对不同环境失效机理的试验方法。并对其适用性问题进行了讨论。     

直升机机载综显模块电路板可靠性预计

一种电子产品的成功研发制造,除了要考虑产品的功能性以外,产品的可靠度高低也被视为决定产品质量好坏的主要因素之一。为了探讨电子产品的可靠度水平,采用军用标准,配合可靠性软件Relex来估计其MTBF与失效率。研究结果给研制单位提供了一份有效的可靠度预计的参考报告,有助于缩短产品的研发周期。     

Approximated reliability of r-out-of-n(F) system with common cause failure and maintenance

In modern industries very high reliability system are needed. To improve the reliability of system, the component redundancy and maintenance of component or system play an impotant role and must be studied. This paper presents a reliability model of a r-out-of-n(F) redundant system with maintenance and Common Cause Failure. Failed component repair times are arbitrarily distributed. The system is in a failed state when r units failed because of the combination of single element failure or CCF(Common Cause Failure). Laplace transformation of reliability is derived by using analysis of Markov state transition graph. By using the analyzed MTBF, we compute MTBF of r-out-of-n(F) system. The MTBF with CCF is saturable even if repair rate is large.Approximated reliability of the r-out-of-n(F) system with maintenance and Common Cause Failure O.SummaryThe paper presents a reliability model of a r-out-of-n(F) redundant system with maintenance and Common Cause Failure. Failed component repair times are arbitrarily distributed. The system is in a failed state when r units failed because of the combination of single element failure or Common Cause Failure. Laplace transformation of reliability is derived by using analysis of Markov state transition graph. By analyzing this mean visiting time equations, we compute MTBF and shows computational example. The MTBF with CCF is saturable even if repair rate is large. In general the maintenance overcomes MTBF bounds, But the repair method not overcome the MTBF saturation when the system has Common Cause Failure.     

智能手机系统的MTBF自动测试分析与研究

智能手机系统的开发近年来一直是行业的热点,而在软件系统开发过程中,为了保证软件系统的质量和可靠性,通常引入MTBF(Mean Time between Failures)测试以提供有效的质量监控,并衡量软件是否达到相应的可靠性置信度指标.本文对MTBF指标值进行了深入分析与设计研究,基于python脚本和Monkey工...     

Survey of reliability and availability evaluation of complex networks using Monte Carlo techniques

Generally there are four main difficulties in evaluating complex large-scale system reliability, availability and MTBF: the system structure may be very complex; subsystems may follow various failure distributions; subsystems may conform to arbitrary failure and repair distributions for maintained systems; the failure data of subsystems are sometimes not sufficient, reliability test sample sizes tend to be small. It is difficult and often impossible to obtain s-confidence limits of them by classical statistics. Monte Carlo technique combined with Bayes method is a powerful tool to solve this kind of problems. In this survey, the typical existing Monte Carlo reliability, availability and MTBF simulation procedures, variance reduction methods, and random variate generation algorithms are analyzed and summarized. The advantages, drawbacks, accuracy and computer time of Monte Carlo simulation in evaluating reliability, availability and MTBF of a complex network are discussed. Finally, some conclusions are drawn and a general Monte Carlo reliability and MTTF assessment procedure is recommended.     

On the Behavior of Some Quantities Used in Bayesian Reliability Demonstration Tests

Equipment mean time between failures (MTBF) is assumed to be a frequency random variable. The goodness of fit of the uniform prior as a probability model for the MTBF is compared to the goodness of fit of the inverted gamma prior for actual failure data. These distributions can both be adequately fitted to the same failure data when the method of moments is used to fit the distributions. A comparison of posterior producer and consumer risks in a Bayesian reliability demonstration test is made using the fitted inverted gamma and uniform distributions as the priors. There can be rather large differences in the values of the posterior risks even when the two priors fit the data equally well or equally poorly.     

Reliability Case History of an Airborne Air Data Computer

The life of an airborne Air Data computer and some of its components have been observed in the field and in the laboratory. This paper treats 3 aspects of the reliability. Part 1 shows the relationship of predicted to actual MTBF and explains some seasonal fluctuations in actual MTBF. Monthly and yearly fluctuations are also explored. Part 2 shows some effects of workmanship-in-repair on reliability growth. The repair procedure was changed and the reliability gradually improved over a long period of time. Part 3 treats one of the components, a wirewound potentiometer, in some detail. A failure analysis and an improvement program are described.     

大型相控阵雷达可靠性评估方法

大型相控阵雷达整机外场可靠性试验可分为联调联试和验证试验两个阶段。针对现有的整机可靠性评估仅利用验证试验阶段较少的试验数据,导致整机可靠性评估结果置信度低的问题,提出一种基于改进序化关系模型的整机可靠性评估方法。该方法通过构建新的联合似然函数对传统序化关系模型进行改进,并融合联调联试数据和验证试验数据来评估整机的可靠性水平。实例表明:该方法能提高整机平均故障间隔时间在同一置信水平下的估计下限,缩短平均故障间隔时间的估计区间。     

A Monte Carlo simulation algorithm for finding MTBF

Prediction of mean time between failures (MTBF) is an important aspect of the initial stage of system development. It is often difficult to predict system MTBF during a given time since the component failure processes are extremely complex. The authors present a Monte Carlo simulation algorithm to calculate the MTBF during a given time of a binary coherent system. The algorithm requires the lifetime distributions of the components and the minimal path sets of the system. The MTBF for a specific time interval, e.g. a month or a year, can be estimated. If the component lifetime distributions are unknown, then a lower bound of system MTBF can be estimated by using known constant failure rates for each component     

A new reliability growth model: its mathematical comparison to the Duane model

The Duane reliability growth model has been traditionally used to model electronic systems undergoing development testing. This paper proposes a new reliability growth model derived from variance stabilisation transformation theory which surpasses the Duane model in typical reliability growth situations. This new model is simpler to plot and fits the data more closely than the Duane model whenever the Duane slope is less than 0.5. This paper explores the mathematical relationships between these two models; and shows that at a Duane slope of 0.5, both models are mathematically equivalent in their capacity to fit the observed data. The instantaneous MTBF of the new model is also developed and compared to that of Duane. As the new model is influenced by the later failures, compared to early failures for the Duane model, it has the further advantage of leading to reduced test times for achieving a specified instantaneous MTBF. As the reliability of electronic systems increases, this has positive implications for testing.