基于可靠性理论的包装振动试验方法改进

目的基于可靠性理论改进包装件的振动试验方法。方法通过界定产品平均寿命、试验截止时间和失效数,研究了样本大小对整个试验可靠程度的影响,以及改变包装阻尼与振动频率对试件振动幅值的影响。结果保证包装产品的可靠性有必要明确样本的大小,样本过小将不能完全体现该批产品的可靠程度;样本过大,虽然能充分体现该批产品的可靠程度,但试验费用将大大提高。随着产品样本的增大,使用者的风险降低。结论在包装件振动试验中融入可靠性理论,并在试验标准中加入失效率验证程序和失效率等级等内容,提高了包装件的可靠性。     

考虑可靠度可信区间的结构可靠性优化设计

 在贝叶斯统计理论和结构可靠性优化设计方法的基础上,研究了结构在小样本情况下考虑可靠度可信区间的结构可靠性优化设计问题.将结构可靠度作为随机变量,根据先验信息和样本信息,采用贝叶斯推断技术获得结构可靠度的概率分布,给出了可靠度的点估计及区间估计.建立了考虑可靠度可信区间的结构可靠性优化设计模型,提出了考虑可靠度可信区间的结构可靠性优化设计方法.所提出的方法为解决小样本情况下的结构可靠性优化设计问题提供了新的解决方案.数值算例验证了所提出的结构可靠性优化设计方法的有效性和正确性.     

人因可靠性仿真

为了改进人因可靠性分析中时间接口、误差传播等方面的不足,进行了人因可靠性仿真方法研究.对仿真中的关键问题如仿真模型建立、仿真流程设计、仿真误差分析和仿真规模确定等进行了深入的理论分析并给出了合理的解决方案.并以核电工程中广为应用的THERP+ HCR方法为原型,给出了人因可靠性仿真的实现实例.仿真结果表明,前述理论分析...     

平衡损失函数下Cox模型的可靠性分析:记录值样本情形

本文在记录值样本下,分别讨论了一类指数分布可靠性指标的经验贝叶斯估计以及未来失效样本的预测问题.通过ML-Ⅱ方法估计超参数,进而在平衡均方损失和平衡Linex损失下,获得了相关指标的经验贝叶斯估计,并给出未来记录值样本的经验贝叶斯预测区间.利用Monte-Carlo模拟方法给出了一个数值算例,研究了结果的精确性.     

贝叶斯法在电子器件可靠性指标估计中的应用

提出了一种新的基于贝叶斯法的电子器件可靠性指标估计方法.即首先利用以往批次产品的样本试验数据求出先验分布,然后利用当前批次产品的样本试验数据求出后验信息,最后将先验分布和后验信息代入贝叶斯公式求出需要的估计值.运用这种方法对电子器件的失效率λ,平均无故障工作时间θ和可靠度R(t)进行了点估计和区间估计.最后用算例加以分析.这种方法增加了估计样本的容量,解决了电子器件试验样本少、可靠性指标估计时缺少数据这一问题.     

基于支持向量回归的装备可靠性评估新方法

针对经典的装备可靠性评估方法在小样本情况下难以得到满意的评估结果的问题,提出了一种综合运用支持向量回归(SVR)算法和自适应重要抽样(AIS)算法进行评估的新方法.该方法通过分析小样本条件下装备可靠性评估原理,建立基于SVR和AIS的装备可靠性评估模型,分析小样本条件下开展装备可靠性评估的过程,给出相应的统一建模语言(...     

仿真系统实现中样本提取算法的研究

如何提取样本图像是当前仿真系统实现中的主要难点而提取样本图像在数字图像处理中属于边缘检测技术.目前国内外边缘检测的方法有很多种主要研究基于Canny边缘检测的样本提取算法.     

基于Bayes法的电主轴极小子样可靠性研究

针对长寿命的磨削电主轴极小子样的可靠性评估问题,提出了Bayes结合虚拟增广样本的分析方法。首先,在Bayes法基本流程的指导下,研究了基于Bayes法的磨削电主轴可靠性评估方法。根据定时截尾试验的原则对电主轴进行可靠性试验,应用Bayes法结合磨削电主轴试验样本的可靠性试验数据,综合虚拟增广样本法对其可靠性进行评估,最终获得电主轴的可靠性评估结果。最后,将基于Bayes法与基于伪寿命分布法的磨削电主轴极小子样可靠性评估结果进行比较,以验证基于Bayes法可靠性评估理论的合理性。     

牙膏纸箱堆码时间的统计处理

为了研究在不同工况下牙膏纸箱的堆码层数和堆码时间的关系,指导牙膏纸箱的储运,通过对样本的堆码试验,利用统计分析方法对其堆码中产生整体蠕变的时间数据进行处理,然后在一定置信度下进行参数和区间估计,从而得出牙膏纸箱堆码时产生蠕变的大小和堆码时间的关系,确保牙膏纸箱堆码整体蠕变时间确定的可靠性.     

港口机械智能维护的决策支持方法

围绕集装箱自动化码头建设智能维护服务平台所需的关键技术,研究港口机械的维护决策支持方法,变被动服务为主动服务,实现智能维护理念.结合港机的机构功能,研究了支持维护模式选择的故障模式和后果分析方法;针对设备状态的不确定性,进行了基于不确定概率的故障树定量分析;为支持实现动态维护决策,研究了港机健康指标的灰色预测建模方法.研究结果和工程实践表明,所提出的决策支持方法能有效提高港口机械的可靠性,为管理者能及时了解设备的运行状态并做出维护决策提供有力的支持.     

非平稳随机激励下结构动力可靠度时域显式子集模拟法

基于动力响应显式表达式,时域显式随机模拟法可以通过减少单次样本计算时间有效提高动力可靠度的计算效率。然而,对于小失效概率问题,由于需要大量次样本计算,该法的计算量仍相当可观。为了克服上述困难,在时域显式随机模拟法基础上引入子集模拟法的基本思想,把小失效概率表示为一系列较大的条件概率的乘积,其中各条件概率采用时域显式随机模拟法计算,条件域内的样本采用Metropolis-Hastings抽样方法生成,从而实现了减少随机模拟所需的样本数,进一步提高了计算效率。算例结果表明改进的方法具有更高的计算效率,更适用于小失效概率和多自由度结构的动力可靠度问题。     

Comparison of Weibull small samples using Monte Carlo simulations

The evaluation of the functional reliability of different designs is a common task and times to failure can be compared using the likelihood ratio test. In the microelectronics industry, as in many others, the high cost of testing places severe restrictions on the sample size. Moreover, the products in these tests are often new and do not have previous reliability histories. These factors make the selection of the Type I and Type II errors in comparison tests very difficult. This paper presents the Monte Carlo simulation results of Type II errors for the likelihood ratio test of comparison as a function of the Type I error and the (small) sample size. Our conclusions are summarized as follows: (1) the common microelectronics industry standard sample size of 32 is often insufficient to reach satisfactory conclusions; (2) small sample tests should only be used for prescreening for significant differences; and (3) when only small samples are available, the Type I and the Type II errors must be selected carefully to prevent misleading conclusions. Copyright © 2006 John Wiley & Sons, Ltd.     

A simulation-based investigation of the staircase method for fatigue strength testing

A critical evaluation of the statistics of the fatigue strength distribution as determined by the staircase (or up-and-down) method is presented. The effects of test parameters (namely, step size and sample size) were analyzed using numerical simulation to determine the accuracy of fatigue strength standard deviation calculations using traditional staircase statistics, resulting in a quantification of standard deviation bias as a function of step size and sample size. A non-linear correction was formulated to mitigate this standard deviation bias inherent in small-sample tests. In addition, the simulation was used to investigate the effectiveness of a bootstrapping algorithm on standard deviation estimates. The bootstrapping algorithm was found to significantly reduce the potential of large standard deviation errors in small-sample tests. Together, the use of the non-linear correction factor and the bootstrapping algorithm may allow an improved method to estimate the statistics of a material’s fatigue strength distribution using a small-sample staircase test strategy.     

Statistical Inference on Constant Stress Accelerated Life Tests under Generalized Gamma Lifetime Distributions

We will discuss the reliability analysis of the constant stress accelerated life tests when a parameter in the generalized gamma lifetime distribution is linear in the stress level. Statistical inference on the estimation of the underlying model parameters as well as the mean time to failure and the reliability function will be addressed on the basis of the maximum likelihood approach. Large sample theory will be derived for the goodness of fit of the data. Some simulation study and an illustrative real example will be presented to show the appropriateness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

Modified Metropolis-Hastings algorithm with reduced chain correlation for efficient subset simulation

Simulation of Markov chain samples using the Metropolis-Hastings algorithm is useful for reliability estimation. Subset simulation is an example of the reliability estimation method utilizing this algorithm. The efficiency of the simulation is governed by the correlation between the simulated Markov chain samples. The objective of this study is to propose a modified Metropolis-Hastings algorithm with reduced chain correlation. The modified algorithm differs from the original in terms of the transition probability. It has been verified that the modified algorithm satisfies the reversibility condition and therefore the simulated samples follow the target distribution for the correct theoretical reasons. When applied to subset simulation, the modified algorithm produces a more accurate estimate of failure probability as indicated by a lower coefficient of variation and a lower mean square error. The advantage is more significant for small failure probability. Examples of soil slope with spatially variable properties were presented to demonstrate the applicability of the proposed modification to reliability estimation of engineering problems. It was found that the modified algorithm produces a more accurate estimator over the range of random dimensions studied.     

Parallel simulation algorithm for maintenance optimization based on directed Acyclic Graph

An efficient simulation algorithm for the quantification of reliability performance indicators of a complex system is demonstrated in the paper that is based on Monte Carlo method. A directed Acyclic Graph is used as a useful system representation. A parallel simulation technique is used in the algorithm which is based on the construction of the special course of life sequence of transformed transition times subjected to the corresponding part of the Acyclic Graph. The parts of the Acyclic Graph represent individual subsystems of a given system and may be effectively evaluated from the reliability point of view. The wide range of models for both deterministic and stochastic processes applied on the terminal nodes of the Acyclic Graph is allowed in the algorithm. The use of the algorithm for comparative theoretical calculations as well as for industrial applications is shown by a visual demonstration. A cost-optimization problem is shortly introduced which may be fully solved by the algorithm using additional genetic algorithms as an applicable optimization technique. The problem takes into account also additional objective that is defined as a prescribed constraint of a selected reliability performance indicator. The solution of the cost-optimization problem is demonstrated on two practical examples.     

Parameter calibration of corncob based on DEM

Corncob is one of the main components of corn ears. Because the mechanical properties of different parts of corncob are very different, and there is a lack of research on the simulation and calibration of corncob parameters at present, the established corn ear or corncob simulation model has low accuracy and poor reliability. In this study, the simulation tests of corncob calibration parameters are carried out based on DEM. Firstly, a modelling method of corncob is proposed to establish sample models of corncob. Then, the DEM simulation parameters that restitution coefficient, static friction coefficient, and rolling friction coefficient of “particle–particle” and “particle-geometry”, and Poisson’s ratio of particle are determined by the Plackett-Burman test and Box-Behnken test. Next, the simulated bending test of corncob is carried out using the calibrated parameters. Finally, by comparing the physical and simulated bending test results, it shows the anti-destructive forces of corncob are 204.52 N and 197.3 N, respectively, with a relative error of 3.53%. This study verifies the reliability of parameter calibration for the discrete element model of corncob and provides a new method for establishing simulation models of corn ear and other materials.     

Fast algorithm for the reliability evaluation of large-scale electrical distribution networks using the section technique

An efficient practical reliability evaluation algorithm is presented for large-scale radial electrical distribution networks (EDN) using a section technique and taking into account the construction features of the EDN. The definition and properties of the section of a branch (SOB) concept, the component spreading method (CSM) for forming the SOB, and an algorithm for the reliability evaluation of an EDN based on the SOB are presented in this paper. The functional unit of an EDN in the failure simulation process is an SOB rather than a component as used in the conventional method and the CSM is used to identify the area controlled by switches. The proposed algorithm should prove useful in practical engineering applications as the repeated search for the areas controlled by switches can be avoided, which sharply reduces the demand for fault enumeration simulation. The developed algorithm was tested on a small test system and a number of practical EDN and the results show the effectiveness and applicability of the proposed approach.     

A Nonparametric Test for Testing Exponentiality Against NBUCA Class of Life Distributions Based on Laplace Transform

In this paper, we introduce a new test statistic for testing exponentiality against new better than used in the increasing convex average class of life distributions (NBUCA) based on Laplace transform. For this proposed test, the asymptotic properties are studied, and the critical values for sample size 5(1)50 are calculated. The powers of the test are also estimated by using a simulation study for commonly used distributions in reliability. The Pittman asymptotic efficiencies of this test are calculated and compared with some old tests. Finally, the test is applied to some real data. Copyright © 2014 John Wiley & Sons, Ltd.     

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.