三参数威布尔分布在风机载荷外推中的应用

本文通过极大似然法、双线性回归法、相关系数法及概率权重矩法的威布尔估计算法对风机载荷相关变量(叶根面内弯矩、叶根面外弯矩及叶尖挠度)进行载荷外推,并通过极大似然值对该几种方法进行比较,提出适合风机载荷变量的估计算法,为使用三参数威布尔分布对风机载荷外推提供了一定的参考。     

基于SWT模型和威布尔分布的CFRP环带微动疲劳寿命预测

以铰销式碳纤维增强基复合材料(CFRP)环带为研究对象,基于SWT疲劳寿命预测模型,推导了服从威布尔分布的概率疲劳寿命预测方法.在给定的荷载条件下,建立ANSYS有限元模型,获得最不利破坏点处的应力、应变数值,并将其代入SWT模型计算微动疲劳损伤参量.根据微动疲劳寿命实验结果,采用最大似然估计法,建立在双参数威布尔分布下微动疲劳寿命与损伤参量的函数关系,得出在一定可靠度下的微动疲劳寿命预测值.结果表明:有限元的数值模拟结果与实验结果具有一致性,在四个荷载工况下,SWT模型损伤参量和威布尔分布参数值的三次项插值函数均方误差最小,得出95%可靠度下的疲劳寿命循环次数预测值均小于疲劳实验最小测量值,可作为工程设计值使用,同时说明了此疲劳寿命预测模型的正确性.     

智能电能表显示模块可靠性评估技术研究

该文根据《电测量设备可信性第311部分∶温度和湿度加速可靠性试验》（GB/T 17215.9311—2017）的技术要求及试验方法,通过使用加速寿命试验的方式对智能电能表进行温、湿度加速寿命试验,根据智能电能表的温、湿度加速寿命试验失效数据进行威布尔分布的拟合优度检验,从而得到智能电能表显示模块的威布尔分布模型,进而得到基于威布尔分布的智能电能表显示模块的寿命模型,利用温、湿度加速模型对试验数据进行外推扩展,得到智能电能表显示模块的可靠度函数,可及时预测智能电能表的可靠性及质量,该结果对提高智能电能表的可靠性具有参考价值。     

Weibull型部件的参数估计方法研究

本文针对Weibull型部件的参数估计问题,在极大似然估计和Bayes估计的一般计算方法的基础上,进一步分析在已知寿命分布类型情况下,对寿命分布参数进行估计的具体方法。在此基础上,重点分析Bayes估计在装备使用数据下的应用。     

基于最优置信限法机械密封的可靠性研究

为了对机械密封的寿命进行可靠性评定,文中在其寿命服从两参数威布尔分布时,基于无失效数据的最优置信限法给出了可靠性参数—可靠度、可靠寿命的置信限,并分析了影响其估计值的主要因素。     

基于威布尔分布的金属化电容器寿命数据分析及参数估计

针对金属化电容器寿命的可靠性分析,常用的参数估计法人为因素影响大,精度差,而且在现场使用不便,提出联合利用最小二乘及平均秩次估计威布尔分布参数的方法解决了上述问题。结合实际试验数据进行计算,与传统方法比较,有效提高了参数的精度。     

U型试件混凝土冲击寿命的统计分析

采用自行设计的U型混凝土试件,以及自制的落锤冲击实验装置,研究了其抗冲击性能。引入顺序统计量和秩的概念,根据平均秩法的期望估计,计算累积失效概率函数F(N)和生存概率函数R(N)的估计值。利用双参数威布尔(Weibull)分布理论及线性回归理论,分析了在4种不同落锤质量水平下冲击寿命N的分布规律,结果表明,双参数威布尔分布理论可以很好地描述U型试件混凝土冲击寿命N的分布特征。     

基于故障数据的设备运行可靠性分析与评估

基于故障数据,对设备运行可靠性进行了分析与评估。对某汽车制造企业的一台卧式加工中心的故障数据进行了统计与分析,形成观测样本,并拟合出了设备故障间隔时间的概率密度分布函数和累计分布函数曲线,从而推断得出其分布规律可能服从威布尔分布。然后通过对威布尔分布函数相关性进行检验,验证了该设备的故障间隔时间分布服从威布尔分布。最后根据统计结果计算得出了该设备的各项可靠性评估指标。     

基于威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测

为有效描述滚动轴承的退化趋势,提出结合威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测新方法。用威布尔分布形状参数作为滚动轴承的性能退化指标,将该指标作为最小二乘支持向量机的输入构造退化趋势预测模型。鉴于最小二乘支持向量机模型参数对模型的推广预测能力影响较大,选粒子群算法(PSO)优化最小二乘支持向量机模型参数,并用实测滚动轴承全寿命实验数据进行检验。结果表明该方法能获得准确的预测结果。     

基于均方误差修正的渗碳12CrNi3钢疲劳强度预测模型

为对渗碳12CrNi₃合金钢提供一种安全可靠的高周疲劳强度预测方法,本文利用高频疲劳测试机,在10⁴～10⁸循环周次内对渗碳12CrNi₃合金钢疲劳试样开展应力比为-1的高周疲劳实验。采用灰色估计法对渗碳12CrNi₃钢疲劳数据的威布尔W(α,β,γ)参数进行评估,并通过估算的威布尔参数,进一步确定渗碳12CrNi₃合金钢强度预测模型的参数。考虑应力-寿命关系,结合疲劳数据的威布尔分布规律,建立了失效概率为1%时的渗碳12CrNi₃合金钢高周疲劳强度预测模型。从预测疲劳强度安全可靠性出发,通过均方误差(MSE)方法对模型进行修正,并将修正后模型99%MSE下界线与渗碳12CrNi₃合金钢疲劳寿命P-S-N曲线(不同存活率P的S-N曲线)进行对比,研究发现12CrNi₃合金钢疲劳寿命数据点均位于模型99%MSE下界线内,预测结果很好。研究表明,在以渗碳12CrNi₃合金钢...     

Probabilistic cumulative damage model to estimate fatigue life

This paper introduces a numerical model to estimate fatigue life under step‐stress conditions, using the Weibull and lognormal distributions. The maximum likelihood method was used to estimate the free parameters of the distributions. The model was fitted to an experimental data on fatigue life in the specimens of steel SAE 8620, by using evolutionary computation to optimize the likelihood function. Results are reported on the values of the parameters and their confidence interval. Also, a validation of the model is discussed using analysis of residuals.     

基于Android的智能可调光LED灯节能控制

针对家庭LED灯能源耗电问题,提出了一种智能调光节能控制方法。该方法以安卓操作系统为平台,智能手机作为客户端,利用LM3409HV降压自适应反馈控制机制,通过Wi Fi网络实现无线远程可调光节能LED灯的低功率照明。该系统采用太阳能绿色可再生能源为智能电网供电,结合室外光强,通过光敏传感器和脉宽调制(PWM)技术调节LED灯亮度。在通用输入电压下,可实现高效率、高功率因数调光。与传统的机械式开关LED照明电灯相比,智能调光系统设计的软硬件更为灵活和方便、成本更低、功耗更小,界面和功能都具有良好的通用性、准确性,具有广泛的应用前景。     

Modelling curved S–N curves

The fatigue limit distribution is estimated using fatigue data and under the assumption that the fatigue limit is random. The stress levels for the broken and unbroken specimens are used. For the broken specimen the number of cycles to failure is also used. By combining the finite life and fatigue limit distribution it is possible to get the probability of not surviving a certain life. This probability is used to estimate a curved S–N curve by using the method of likelihood. The whole S–N curve is estimated at the same time. These curves show the predictive life given a certain stress level. The life and the quantile of the fatigue limit distribution are also predicted by using profile predictive likelihood. In this way the scatter around the S–N curve as well as the uncertainty of the S–N curve are taken into account.     

Bayes estimation of residual life by fusing multisource information

Residual life estimation is essential for reliability engineering. Traditional methods may experience difficulties in estimating the residual life of products with high reliability, long life, and small sample. The Bayes model provides a feasible solution and can be a useful tool for fusing multisource information. In this study, a Bayes model is proposed to estimate the residual life of products by fusing expert knowledge, degradation data, and lifetime data. The linear Wiener process is used to model degradation data, whereas lifetime data are described via the inverse Gaussian distribution. Therefore, the joint maximum likelihood (ML) function can be obtained by combining lifetime and degradation data. Expert knowledge is used according to the maximum entropy method to determine the prior distributions of parameters, thereby making this work different from existing studies that use non-informative prior. The discussion and analysis of different types of expert knowledge also distinguish our research from others. Expert knowledge can be classified into three categories according to practical engineering. Methods for determining prior distribution by using the aforementioned three types of data are presented. The Markov chain Monte Carlo is applied to obtain samples of the parameters and to estimate the residual life of products due to the complexity of the joint ML function and the posterior distribution of parameters. Finally, a numerical example is presented. The effectiveness and practicability of the proposed method are validated by comparing it with residual life estimation that uses non-informative prior. Then, its accuracy and correctness are proven via simulation experiments.     

A novel MTTF estimator and associated parameter estimation method on heavily censoring data

Many reliability and maintenance decision problems need to estimate mean-time-to-failure (MTTF) of a particular product component and/or build its life distribution model as early as possible based on field failure data. The field failure data are often heavily censored. In this case, the exponential-assumption–based method can considerably overestimate the MTTF, and the classical parameter estimation methods such as the maximum likelihood method (MLM) cannot provide robust estimates. This paper aims to address this issue through proposing a novel method to estimate the MTTF and life distribution parameters. The proposed method first derives a nonparametric estimator of MTTF based on the decomposition of the integral expression of the theoretical MTTF and sample statistical characteristics. The estimated MTTF is then combined with a two-step single-parameter MLM to estimate the distribution parameters. A numerical experiment is carried out, and a real-world dataset is analyzed. The results show that the proposed method can provide accurate and robust estimates for the MTTF and distribution parameters. The method is applicable for any life distribution and offers practitioners an efficient tool for reliability analysis.     

Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Gaussian-Process-Based Optimization

Stochastic differential equations (SDEs) are used as statistical models in many disciplines. However, intractable likelihood functions for SDEs make inference challenging, and we need to resort to simulation-based techniques to estimate and maximize the likelihood function. While importance sampling methods have allowed for the accurate evaluation of likelihoods at fixed parameter values, there is still a question of how to find the maximum likelihood estimate. In this article, we propose an efficient Gaussian-process-based method for exploring the parameter space using estimates of the likelihood from an importance sampler. Our technique accounts for the inherent Monte Carlo variability of the estimated likelihood, and does not require knowledge of gradients. The procedure adds potential parameter values by maximizing the so-called expected improvement, leveraging the fact that the likelihood function is assumed to be smooth. Our simulations demonstrate that our method has significant computational and efficiency gains over existing grid- and gradient-based techniques. Our method is applied to the estimation of ocean circulation from Lagrangian drift data in the South Atlantic ocean.     

基于生命周期和模块化相结合的液压缸节能设计

为了量化分析液压缸的节能设计,以其全生命周期的能源消耗为研究对象,将生命周期和模块化分析相结合,根据可拓学原理构建液压缸拓扑结构,提出了节能设计元的概念,综合考虑其成本、环境、效率3项指标,逐级进行节能设计元的分析和评价,得出最优的节能设计方案。结果表明,该方法能够准确地评价各级节能设计元的综合指标,并有效地控制其能源消耗。最后以某型号液压缸零部件加工阶段中结构模块的缸筒为例,验证了该方法的可行性和实用性。     

Robust Estimation for Weibull Distribution in Partially Accelerated Life Tests with Early Failures

Maximum likelihood estimation (MLE) is a frequently used method for estimating distribution parameters in constant stress partially accelerated life tests (CS‐PALTs). However, using the MLE to estimate the parameters for a Weibull distribution may be problematic in CS‐PALTs. First, the equation for the shape parameter estimator derived from the log‐likelihood function is difficult to solve for the occurrence of nonlinear equations. Second, the sample size is typically not large in life tests. The MLE, a typical large‐sample inference method, may be unsuitable. Test items unsuitable for stress conditions may become early failures, which have extremely short lifetimes. The early failures may cause parameter estimate bias. For addressing early failures in the Weibull distribution in CS‐PALTs, we propose an M‐estimation method based on a Weibull Probability Plot (WPP) framework, which leads a closed‐form expression for the shape parameter estimator. We conducted a simulation study to compare the M‐estimation method with the MLE method. The results show that, with early‐failure samples, the M‐estimation method performs better than the MLE does. Copyright © 2015 John Wiley & Sons, Ltd.     

Reliability analysis of exponentiated Poisson-exponential constant stress accelerated life test model

Accelerated life testing is an efficient tool frequently adopted for obtaining failure time data of test units in a lesser time period as compared to normal use conditions. We assume that the lifetime data of a product at constant level of stress follows an exponentiated Poisson-exponential distribution and the shape parameter of the model has a log-linear relationship with the stress level. Model parameters, the reliability function (RF), and the mean time to failure (MTTF) function under use conditions are estimated based on eight frequentist methods of estimation, namely, method of maximum likelihood, method of least square and weighted least square, method of maximum product of spacing, method of minimum spacing absolute-log distance, method of Cramér-von-Mises, method of Anderson–Darling, and Right-tail Anderson–Darling. The performance of the different estimation methods is evaluated in terms of their mean relative estimate and mean squared error using small and large sample sizes through a Monte Carlo simulation study. Finally, two accelerated life test data sets are considered and bootstrap confidence intervals for the unknown parameters, predicted shape parameter, predicted RF, and the MTTF at different stress levels, are obtained.     

Different Estimation Methods for Constant Stress Accelerated Life Test under the Family of the Exponentiated Distributions

The Accelerated Life Testing (ALT) has been used for a long time in several fields to obtain information on the reliability of product components and materials under operating conditions in a much shorter time. One of the main purposes of applying ALT is to estimate the failure time functions and reliability performance under normal conditions. This paper concentrates on the estimation procedures under ALT and how to select the best estimation method that gives accurate estimates for the reliability function. For this purpose, different estimation methods are used, such as maximum likelihood, least squares (LS), weighted LS, and probability weighted moment. Moreover, the reliability function under usual conditions is predicted. The estimation procedures are applied under the family of the exponentiated distributions in general, and for the exponentiated inverted Weibull (EIW) as a special case. Numerical analysis including simulated data and a real life data set is conducted to compare the performances between these four methods. It is found that the ML method gives the best results among other estimation methods. Finally, a comparison between the EIW and the Inverted Weibull (IW) distributions based on a real life data set is made using a likelihood ratio test. It is observed that the EIW distribution can provide better fitting than the IW in case of ALT. Copyright © 2015 John Wiley & Sons, Ltd.