Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Reliability evaluation of bladder accumulator with no failure data

As a bladder accumulator is a high reliable and long life component in a hydraulic system,its cost is high and it takes a lot of time to test its reliability,therefore,a reliability test with small sample is performed,and no failure data is obtained using the method of fixed time truncation. In the case of Weibull distribution,a life reliability model of bladder energy storage is established by Bayesian method using the optimal confidence intervals method,a model of one-sided lower confidence intervals of the reliability and one-sided lower confidence intervals model of the reliability life are established. Results of experiments show that the evaluation method of no failure data under Weibull distribution is a good way to evaluate the reliability of the accumulator,which is convenient for engineering application,and the reliability of the accumulator has theoretical and practical significance.

Maximum Likelihood Estimation for the Pooled Repeated Partly Interval-Censored Observations Logistic Regression Model

Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.

Regression Analysis of Interval-Censored Data with Informative Observation Times Under the Accelerated Failure Time Model

This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.

计及维护操作影响的变压器内部潜伏性故障率预测模型

为了量化维护操作对变压器故障率的实际影响,文中通过深度挖掘短时间尺度下变压器油色谱监测数据,建立了一个基于实时运行工况的变压器内部潜伏性故障率预测模型。对传统马尔科夫故障率模型的建模过程进行了分析,指出该模型中的自变量状态持续时间无法响应维护操作影响,提出对状态持续时间参数进行修正;具体步骤为利用R型聚类-主成分分析算法提取关键溶解气体种类;通过径向基函数神经网络建立了关键溶解气体含量与修正状态持续时间之间的映射关系;推导变压器内部潜伏性故障率关于修正状态持续时间的解析表达式。结果表明,与马尔科夫故障率模型相比,所建立的模型具备表征维护操作影响与变压器实际故障率的能力。

Marginal Accelerated Hazard Model with Multivariate Failure Time Data

Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.

Discussion of Fan et al.’s paper “Gaining effciency via weighted estimators for multivariate failure time data”

In the analysis of correlated data, it is ideal to capture the true dependence structure to increase effciency of the estimation. However, for multivariate survival data, this is extremely

Discussion on “Gaining Effciency via Weight Estimators for Multivariate Failure Time Data” by Fan, Zhou and Chen

The survival analysis literature has always lagged behind the categorical data literature in developing methods to analyze clustered or multivariate data. While estimators based on

Rejoinder for Gaining effciency via weighted estimators for multivariate failure time data

We thank all the discussants for their interesting and stimulating contributions. They have touched various aspects that have not been considered by the original articles.

膨胀土边坡GNSS实时监测技术

膨胀土滑坡因其广泛性、破碎性、反复性、隐蔽性等特征,对工程危害严重。对其开展长期实时变形监测是实现膨胀土灾害防控的前提。在第一代膨胀土边坡GNSS监测技术试验的基础上,进行了两项改进:(1)设计了分层式GNSS/全站仪一体化监测装置,实现了膨胀土土质边坡分层式实时监测。(2)提出了综合多源参数的膨胀土边坡GNSS实时监测技术,提升了实时变形监测结果的可靠性和精度。相关技术和设备在广西宁明膨胀土公路边坡中进行了示范应用,分析了GNSS形变与多源监测数据的动态响应特征及诱滑原因,成功监测到一起膨胀土滑坡,研究成果具有可复制性和推广性。

区间删失数据下基于模型平均方法的估计及预测

将模型平均方法应用于区间删失数据下的加速失效时间模型中,并与模型选择方法进行了比较,模拟结果表明模型平均方法的估计要优于模型选择方法.将模型平均方法应用于心脏移植手术数据的实例分析中,结果表明相对于模型选择方法而言,模型平均方法的预测效果更好.

基于加速失效模型的子群分析方法

精准医疗强调了正确识别异质性子群的重要性,以发展可针对每个子群的个性化治疗方案.尽管近来在子群分析的方法上取得了一些进展,在数据存在删失时,如何有效识别子群仍然缺乏探索.在本文中,我们提出了一种基于加速失效模型的新的子群分析方法,其中由潜在因素导致的异质性可以用特定于个体的截距项来表示.我们考虑最常见的右删失情况,并利用平均插补法对删失数据进行处理.硬阈值惩罚函数被应用于配对截距项的成对差值,可以自动地将观察个体划分为不同的子群.我们也建立了所提出的估计量的理论性质.模拟研究和威斯康辛乳腺癌数据集分析进一步验证了所提方法的有效性.

一种新的无失效数据可靠性评估方法研究

为对高可靠性的电子与机电类产品进行可靠性评估,在无失效数据情况下,提出一种将失效信息引进置信限分析中的修正单侧置信限评估法。通过不同截尾时间对应的产品无失效数据,采用Bayes参数估计得到产品失效率,并根据所提出的方法将时间系数进行等效替换,得到产品可靠度与平均故障间隔时间。以某型机器人步进电机的无失效数据为例,将该方法与其他单侧置信限方法的计算结果进行对比分析,结果表明:在置信水平为0.99条件下,产品平均故障间隔时间为37 080 h,小于其他置信限方法的计算结果;当任务时间达到1 000 h时,采用新方法得到的产品可靠度估计值为0.973.实例分析验证了新方法在无失效数据产品可靠性评估中的有效性,且可防止产品可靠度与平均故障间隔时间估计结果冒进现象的发生。

小样本及无失效数据时电器产品可靠性特征量的估计

随着电器产品可靠性的提高 ,其可靠性试验一般采用定时截尾寿命试验 ,若产品的可靠性很高 ,则可能会出现失效样品个数较少甚至无失效样品产生 ,即出现了小样本及无失效数据的情况 ,若采用经典方法估计其可靠性特征量会引入较大误差 ,本文对于小样本时可靠性特征量的估计引入了平均寿命的修正极大似然估计公式 ,研究结果表明 ,修正后的平均寿命估计量的相对误差比极大似然估计法的相对误差有了明显减小 ,且截尾程度越高 ,修正效果越明显 ,而对试验中出现无失效数据时的可靠性特征量应用Bayes理论进行估计。

基于网络的远程监测和故障诊断系统的数据库系统

随着信息技术和网络化向各个行业的延伸,实时监测已经成为机械设备监测的一种需要。而实时数据如何存取,采集后的这些历史数据如何为以后的故障诊断服务,在企业中如何来组织这些数据,是我们所要思考和解决的问题。所提出的基于网络的远程监测和故障诊断系统的数据库系统,正是基于上述的考虑,介绍了三个层次的数据库系统:企业监测站、企业监测中心和远程故障诊断中心。解决了实时数据的存取,历史数据的压缩,企业信息的管理等问题。整个库系统使用UML建模,对数据库系统进行了细致的分析和构造。

大型雷达装备使用可靠性评估

为查找制约大型雷达装备可靠性提高的薄弱环节,提高雷达战备完好性,建立了一种基于重要性的可靠性评估模型,并依据外场统计故障数据,利用该模型对某大型雷达发射系统的使用可靠性进行了评估。首先,针对大型雷达装备的特点,采用层次分析方法(AHP),建立了具有3层评估指标的整机使用可靠性评估系统;其次,运用数理统计的方法,根据相同类型故障的寿命分布模型计算其可靠性指标;最后运用加权融合的方法获得部件系统及整机的平均故障间隔时间,用以评估雷达的使用可靠性,为大型雷达装备科学维修、设计改进以及新装备研制提供了参考。

移动实时数据库系统综述

移动实时数据库系统涉及到信息处理技术、分布式计算技术、移动通讯技术、实时处理技术等多个学科领域,是当前信息领域中前沿、开创性的课题。论文对移动实时数据库系统的体系结构、系统需求及移动实时环境下事务模型、数据复制和缓存、位置相关查询、数据广播、故障恢复等相关研究做了一个综述。

卧式加工中心现场失效数据分析实例研究

基于某大型汽车发动机厂14台卧式加工中心10个月的现场失效采集数据,首先采用卡普兰-梅尔和威布尔变换等方法,得到数据集的威布尔概率纸图;然后根据威布尔概率纸图的曲线形状,采用二重混合威布尔模型作为拟合方程;为了避免直接求解多维非线性似然方程组,将原问题转化为有约束非线性最优化问题,并采用BFGS算法和内点法求解,得到了置信度为95%的模型参数的置信区间;模型的拟合优度检验采用K-S检验方法,结论为在5%的显著性水平下接受这样的寿命分布拟合模型。得到了卧式加工中心失效率函数,为一浴盆曲线,且失效率一直下降,因此设备处于早期故障阶段。计算了卧式加工中心的平均故障间隔时间值。

含有Ⅱ型区间删失数据的回归模型参数估计

目的比较四种方法估计含有Ⅱ型区间删失数据的回归模型参数最大似然估计值,探讨在不同样本量情况下四种方法估计结果的准确性和稳定性。方法对含有Ⅱ型区间删失的生存数据分别拟合Weibull参数回归模型和Cox-PH半参数回归模型,并结合EMICM算法得到模型参数的最大似然估计值;应用组均值插补法将区间删失数据填补为右删失数据,进一步用传统的Cox回归以及建立伪观察值的方法估计生存函数,模拟样本量分别为50、200、500例。结果Weibull回归模型的参数最大似然估计值分别为β_1=0.496,β_2=-0.366;β_1=0.680,β_2=-0.586;β_1=0.620,β_2=-0.504。Cox-PH半参数回归模型的参数最大似然估计值为β_1=0.652,β_2=-0.469;β_1=0.683,β_2=-0.538;β_1=0.629,β_2=-0.511。填补为右删失数据后传统Cox回归方法得到的参数最大似然估计值分别为β_1=0.203,β_2=-0.227;β_1=0.641,β_2=-0.514;β_1=0.545,β_2=-0.446。用Pseudo-observations得到的参数最大似然估计值分别为β_1=0.217,β_2=-0.275;β_1=0.796,β_2=-0.601;β_1=0.561,β_2=-0.468。结论在不同样本量情况下,拟合Weibull参数回归模型,Cox-PH半参数回归模型结合EMICM算法估计的参数最大似然估计更准确更稳定。