The complementary exponential geometric distribution: Model, properties, and a comparison with its counterpart

In this paper, we proposed a new two-parameter lifetime distribution with increasing failure rate, the complementary exponential geometric distribution, which is complementary to the exponential geometric model proposed by Adamidis and Loukas (1998). The new distribution arises on a latent complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the maximum lifetime value among all risks. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulas for its reliability and failure rate functions, moments, including the mean and variance, variation coefficient, and modal value. The parameter estimation is based on the usual maximum likelihood approach. We report the results of a misspecification simulation study performed in order to assess the extent of misspecification errors when testing the exponential geometric distribution against our complementary one in the presence of different sample size and censoring percentage. The methodology is illustrated on four real datasets; we also make a comparison between both modeling approaches.     

A generalized modified Weibull distribution for lifetime modeling

A four parameter generalization of the Weibull distribution capable of modeling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone as well as non-monotone failure rates, which are quite common in lifetime problems and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh and modified Weibull distributions, among others. We derive two infinite sum representations for its moments. The density of the order statistics is obtained. The method of maximum likelihood is used for estimating the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the proposed distribution.     

A generalized modified Weibull distribution for lifetime modeling

A four parameter generalization of the Weibull distribution capable of modeling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone as well as non-monotone failure rates, which are quite common in lifetime problems and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh and modified Weibull distributions, among others. We derive two infinite sum representations for its moments. The density of the order statistics is obtained. The method of maximum likelihood is used for estimating the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the proposed distribution.     

Exponentiated Weibull–Poisson distribution: Model,properties and applications

In this paper we propose a new four-parameters distribution with increasing, decreasing, bathtub-shaped and unimodal failure rate, called as the exponentiated Weibull–Poisson (EWP) distribution. The new distribution arises on a latent complementary risk problem base and is obtained by compounding exponentiated Weibull (EW) and Poisson distributions. This distribution contains several lifetime sub-models such as: generalized exponential-Poisson (GEP), complementary Weibull–Poisson (CWP), complementary exponential-Poisson (CEP), exponentiated Rayleigh–Poisson (ERP) and Rayleigh–Poisson (RP) distributions.We obtain several properties of the new distribution such as its probability density function, its reliability and failure rate functions, quantiles and moments. The maximum likelihood estimation procedure via an EM-algorithm is presented in this paper. Sub-models of the EWP distribution are studied in details. In the end, applications to two real data sets are given to show the flexibility and potentiality of the new distribution.     

Delay‐distribution‐dependent robust stability of uncertain systems with time‐varying delay

By employing the information of the probability distribution of the time delay, this paper investigates the problem of robust stability for uncertain systems with time‐varying delay satisfying some probabilistic properties. Different from the common assumptions on the time delay in the existing literatures, it is assumed in this paper that the delay is random and its probability distribution is known a priori. In terms of the probability distribution of the delay, a new type of system model with stochastic parameter matrices is proposed. Based on the new system model, sufficient conditions for the exponential mean square stability of the original system are derived by using the Lyapunov functional method and the linear matrix inequality (LMI) technique. The derived criteria, which are expressed in terms of a set of LMIs, are delay‐distribution‐dependent, that is, the solvability of the criteria depends on not only the variation range of the delay but also the probability distribution of it. Finally, three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.     

The beta Burr XII distribution with application to lifetime data

For the first time, a five-parameter distribution, the so-called beta Burr XII distribution, is defined and investigated. The new distribution contains as special sub-models some well-known distributions discussed in the literature, such as the logistic, Weibull and Burr XII distributions, among several others. We derive its moment generating function. We obtain, as a special case, the moment generating function of the Burr XII distribution, which seems to be a new result. Moments, mean deviations, Bonferroni and Lorenz curves and reliability are provided. We derive two representations for the moments of the order statistics. The method of maximum likelihood and a Bayesian analysis are proposed for estimating the model parameters. The observed information matrix is obtained. For different parameter settings and sample sizes, various simulation studies are performed and compared in order to study the performance of the new distribution. An application to real data demonstrates that the new distribution can provide a better fit than other classical models. We hope that this generalization may attract wider applications in reliability, biology and lifetime data analysis.     

基于概率分布距离的多响应模型确认度量

针对模型确认中的确认度量问题,构造实验观测数据经验概率分布的置信包络。通过计算其与模型响应概率分布之间距离的上/下确界,给出基于概率分布距离确认度量的置信区间。通过构造与实验观测数据有关的协方差矩阵,给出基于概率分布距离的多响应模型确认度量及其置信区间的求解方式。该度量利用了模型输出与实验观测的完整概率分布信息,并且考虑了各模型响应间的相关性。算例仿真结果表明其确认错误率低于现有的其他两种确认度量。     

A new lifetime distribution

A new two-parameter distribution with decreasing failure rate is introduced. Various properties of the introduced distribution are discussed. The EM algorithm is used to determine the maximum likelihood estimates and the asymptotic variances and covariance of these estimates are obtained. Simulation studies are performed in order to assess the accuracy of the approximation of the variances and covariance of the maximum likelihood estimates and investigate the convergence of the proposed EM scheme. Illustrative examples based on real data are also given.     

The bivariate generalized linear failure rate distribution and its multivariate extension

The two-parameter linear failure rate distribution has been used quite successfully to analyze lifetime data. Recently, a new three-parameter distribution, known as the generalized linear failure rate distribution, has been introduced by exponentiating the linear failure rate distribution. The generalized linear failure rate distribution is a very flexible lifetime distribution, and the probability density function of the generalized linear failure rate distribution can take different shapes. Its hazard function also can be increasing, decreasing and bathtub shaped. The main aim of this paper is to introduce a bivariate generalized linear failure rate distribution, whose marginals are generalized linear failure rate distributions. It is obtained using the same approach as was adopted to obtain the Marshall-Olkin bivariate exponential distribution. Different properties of this new distribution are established. The bivariate generalized linear failure rate distribution has five parameters and the maximum likelihood estimators are obtained using the EM algorithm. A data set is analyzed for illustrative purposes. Finally, some generalizations to the multivariate case are proposed.     

基于Erlang-HSMM的设备剩余寿命预测研究

对于关键复杂设备进行健康诊断和设备剩余寿命预测,提出了一种基于爱尔朗分布和隐半马尔可夫模型的联合剩余寿命预测模型(Erlang-HSMM,E-HSMM)。首先,提出了改进的前后向算法、维特比算法和BaumWelch算法,有效地降低了模型的计算复杂度;其次,基于爱尔朗分布改进设备的健康状态逗留时间,将状态逗留时间分为已遍历和未遍历两个部分,提出新的健康状态逗留时间的概率分布;最后,针对状态监测数据,利用失效率理论构建设备剩余寿命预测模型。通过美国Caterpillar公司液压泵的状态监测实际数据进行评价与验证,实验结果表明,E-HSMM模型对设备的状态诊断和剩余寿命预测更加符合实际状况,比传统的隐半马尔可夫模型(HSMM)更有效。     

基于节点支路链矩阵的配电网动态拓扑分析方法研究与应用*

针对配电网中现有拓扑分析方法运算复杂、速度较慢、重用性较差,以及当配网规模扩大或开关状态改变时,不易及时地反映网络拓扑信息等问题,提出基于支路链矩阵的配电网动态拓扑分析方法。在系统正常运行时,采用特殊节点法将配电网划分多个支路链子图,以节点-支路链矩阵描述配网拓扑结构,借助邻接矩阵标记法与深度优先搜索原则结合完成更新后的拓扑网络的电气岛连通性分析。当支路动态变化时,根据支路属性利用方向深度优先搜索法快速地修改网络拓扑结构,局部更新节点-支路链矩阵。多个实例结果表明该方法可适应多种配电网接线结构,搜索过程中节点数量明显减少,降低了算法复杂度,提高了拓扑分析速度,为不同配网结构动态分析提供了一种有效的方法。     

基于分配系数矩阵的网损分摊方法探究

新电改"九号文"下发后,输配电成本中的网损分摊问题受到了广泛关注.为公平合理的将网损费用分摊给用户,论文提出了一种基于分配系数矩阵的网损分摊方法.首先进行全网潮流计算,得到各线路上的功率损耗;然后根据发电机对线路的分配系数矩阵计算各发电机应分摊的网损,再由发电机对负荷的分配系数矩阵计算得到各负荷应分摊的网损.最后,采用...     

四轮独立驱动电动汽车单轮失效稳定性控制

为了解决四轮独立驱动电动汽车驱动系统失效时的转矩分配问题,提出了一种基于规则的驱动力控制分配策略以保证车辆在出现单轮失效情况下的稳定性和动力性.控制器采用双层控制,包括上层控制和下层控制.在上层控制中,根据驾驶员的输入与车辆状态,采用滑模控制理论计算出控制横摆角速度和质心侧偏角的附加横摆力矩.在下层控制中,根据驱动电机...     

基于数据挖掘的智能配电站房运行状态评估方法

为解决智能配电站房的在线监测与预警评估问题,提出了一种基于数据挖掘的智能配电站房运行状态评估方法。首先建立配电站房多源数据信息阵列,对当前时刻之前一段时间内配电站房内部的温度信息、湿度信息、水侵信息、烟雾信息、开关遥测信息、开关遥控信息进行描述;其次通过配电站房多源数据评估阵对智能配电站房多源数据信息阵进行了量化评估;然后对配电站房多源数据评估阵进行每列量化综合以生成配电站房多源数据单项评估阵;最后基于加权平均方法对配电站房多源数据单项评估阵进行综合,生成智能配电站房运行状态评估指标。实例分析验证了本文所提方法的可行性,可满足现场工程实际需求。     

基于多邻域空间分布的图像检索

由于灰度共生矩阵及其改进算法存在计算复杂、且对于纹理分布信息缺乏计算以及忽略了图像相关特性等缺点,导致对于图像纹理的有效信息缺乏很好的描述,为此提出了一种新的纹理特征用于图像检索.该算法首先结合图像中像素的统计信息,针对不同的邻域范围提取图像的邻域相关矩阵,然后在此基础上构造多邻域空间分布特征用于图像检索.分析表明,该算法所提取的纹理特征计算量小,复杂度低,并且由于将纹理的结构特征和统计特征有效地结合起来,所以对图像的空间纹理分布特征可以较好地描述.为了证明新算法所提取纹理特征的有效性,将其用于图像检索实验.实验结果表明,新算法在检索精度上相比其他算法具有较大的提高.     

基于矩阵算法的主动配电网交直流混合网络供电恢复策略研究

考虑到直流输电技术的发展,直流配电应用于配电网中是未来配电网的研究重点。本文为解决主动配电网交直流混合网络的供电恢复问题,提出含直流配电线路的主动配电网供电恢复方案,当交流配电线路出现故障时,考虑直流侧配电线路网损、换流器以及分布式电源影响下的供电恢复方案;当直流配电线路出现故障时,考虑转换直流侧运行方式或让失电的直流负荷转入计划孤岛运行模式,以保证直流侧重要负荷的持续供电,分别提出交流侧和直流侧约束条件,并在约束条件下选取网络损耗、线路末端电压越限节点个数以及开关操作次数作为指标构建目标函数,依据矩阵算法对供电恢复过程的不同恢复方案进行大数据分析与处理,得出最优方案。通过改进的IEEE 123节点算例证明,提出的方案能够有效的解决主动配电网的供电恢复问题。     

基于故障概率的配电设备排查路径规划

配电系统设备种类繁多,故障概率较高,且作为直接面向用户的电网层级,发生故障对电力用户影响最大。然而,在配电系统实际故障发生时,故障点往往难以准确定位,仅被限定在某一区域内,且不同设备间故障概率存在差异,为故障排查工作带来了不小的困难。为解决配电系统故障情况下排查路径最优化的问题,保证排查工作的及时性和准确性,本文综合考虑排查时间的最小化以及大故障概率设备的优先性,以经典的旅行商问题为基础,考虑不同设备故障概率的差异,对求解方法作出优化改进,建立了基于故障概率的配电设备排查路径规划模型,并选择遗传算法对模型进行求解。最后,以某配电系统为例对设备排查路径进行规划,验证了模型及算法的有效性和实用性。     

基于随机森林算法的配网抢修故障量预测方法

配网抢修是电力系统运行环节中十分重要的一环,精益化的配网抢修管理不仅能提高电力系统的供电服务质量,也能减少电力公司的经济损失. 本文提出一种新的配网抢修故障数量预测的方法. 首先,基于历史数据,以气温、风力、前一天的故障量、最大最小负荷等作为因变量,对数据做了特征映射等预处理. 然后,应用随机森林算法建立配网抢修故障量预测模型,并预测不同区域、不同电网故障及非电网故障、不同电压维度下未来一天故障量. 在真实电力数据上进行了对比验证,实验结果表明提出的方法具有较好的预测效率和准确性.     

改进的二进制循环码盲识别方法

目前已有的循环码盲识别方法在低码率编码条件下效果较好,但在高误码率及高码率条件下不能高效识别,或者只针对循环码中某一子类。为有效解决高误码率以及高码率编码下的循环码盲识别问题,提出一种基于矩阵变换和码重分布的方法,首先对接收序列按估计码长构造矩阵,并对矩阵进行初等变换;然后利用改进的码重分布距离公式对循环码进行盲识别。仿真结果表明该方法在高误码率以及高码率编码时可实现高效的循环码盲识别。     

基于改进免疫遗传算法的电网故障诊断研究

针对电网故障的特点,应用一种改进的免疫遗传算法对电网故障进行研究。该算法能够较好地解决传统遗传算法的不足。通过引入新的交叉和变异率,更多考虑了种群的全局特征,采用动态自适应方式提取疫苗,避免了传统遗传算法收敛速度较慢的缺点。改进的算法本着优胜劣汰的思想,删除适值较低的抗体群,取而代之的是随机生成的部分新抗体,保持种群的多样性。建立一个新的目标函数,通过对一个电网的分析,验证了方法的有效性。