Variables inspection for SPC‐quarantined production

When a control chart signals an out‐of‐control condition for a production process, it may be desirable to “quarantine” all units produced since the last in‐control SPC sample. This paper presents an efficient procedure for variables inspection of such “SPC quarantined” product. A Bayesian sequential inspection procedure is developed which determines whether the out of control production is of acceptable quality. By inspecting the units in reverse of the order in which they were produced, the procedure is also capable of detecting the point at which the process went out of control, thus eliminating the need to inspect units produced prior to the onset of the out of control condition. Numerical examples are presented, and the performance characteristics of the procedure are demonstrated using Monte Carlo simulation. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 159–171, 2001     

Bayesian testing strategies for software with an operational profile

We consider a software reliability model where the failure rate of each fault depends on the specific operation performed. The software is tested in a given sequence of test cases for fixed durations of time to collect data on failure times. We present a Bayesian analysis of software failure data by treating the initial number of faults as a random variable. Our analysis relies on the Markov Chain Monte Carlo methods and is used for developing optimal testing strategies in an adaptive manner. Two different models involving individual and common faults are analyzed. We illustrate an implementation of our approach by using some simulated failure data. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:747–763, 2001     

初始先验分布未知条件下的DLMR及其Bayes预测

讨论了具有无信息初始先验分布的矩阵变量动态线性模型 (简记为DLMR)及其Bayes预测 ,利用MDIP方法 ,就尺度方差阵∑已知和未知两种情况 ,分别给出模拟初始状态参数(θ1 |D0 )和 (θ1 ,∑ |D0 )的无信息先验分布 ,进而给出其Bayes预测。     

Testing or fault-finding for reliability growth: A missile destructive-test example

A new piece of equipment has been purchased in a lot of size m. Some of the items can be used in destructive testing before the item is put into use. Testing uncovers faults which can be removed from the remaining pieces of equipment in the lot. If t < m pieces of equipment are tested, then those that remain, m₁ = m − t, have reduced fault incidence and are more reliable than initially, but m₁ may be too small to be useful, or than is desirable. In this paper models are studied to address this question: given the lot size m, how to optimize by choice of t the effectiveness of the pieces of equipment remaining after the test. The models used are simplistic and illustrative; they can be straightforwardly improved. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 623–637, 1997     

基于物元贝叶斯网的舰船损伤定位

在分析了物元理论和贝叶斯网理论的基础上,提出了结合主观概率信息和客观状态信息的物元贝叶斯网模型,并给出了物元贝叶斯网在损伤定位中的推理算法.舰船损伤定位的案例分析表明,该方法可以更加全面地融合战损装备的状态信息,使损伤定位结果更为准确.     

飞机结构疲劳可靠度贝叶斯组合预测

在实际工程中,常常需要利用模型去描述和分析问题。然而模型亦存在不确定性,即可能存在多个描述同一现象的模型,例如多个疲劳分析的模型。针对飞机结构的疲劳可靠性问题,提出在考虑三种裂纹扩展模型下基于贝叶斯公式的疲劳可靠度组合预测方法。针对不同应力水平下飞机结构试件的裂纹扩展数据建立了三种随机裂纹扩展模型;在考虑模型参数不确定性条件下,采用贝叶斯模型平均方法对三种模型进行组合;基于组合模型分析结构的可靠度。所提方法在分析飞机结构疲劳可靠度上,采用了组合模型,能够最大限度保障结果的稳定性。此外,考虑了模型参数的不确定性,能够得到更为合理的裂纹扩展预测分布和可靠度预测值。给出的实例及分析结果表明所提方法可行。     

Stochastic call center staffing with uncertain arrival,service and abandonment rates: A Bayesian perspective

In this article, we introduce staffing strategies for the Erlang‐A queuing system in call center operations with uncertain arrival, service, and abandonment rates. In doing so, we model the system rates using gamma distributions that create randomness in operating characteristics used in the optimization formulation. We divide the day into discrete time intervals where a simulation based stochastic programming method is used to determine staffing levels. More specifically, we develop a model to select the optimal number of agents required for a given time interval by minimizing an expected cost function, which consists of agent and abandonment (opportunity) costs, while considering the service quality requirements such as the delay probability. The objective function as well as the constraints in our formulation are random variables. The novelty of our approach is to introduce a solution method for the staffing of an operation where all three system rates (arrival, service, and abandonment) are random variables. We illustrate the use of the proposed model using both real and simulated call center data. In addition, we provide solution comparisons across different formulations, consider a dynamic extension, and discuss sensitivity implications of changing constraint upper bounds as well as prior hyper‐parameters. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 460–478, 2016     

发动机传感器数据证实的贝叶斯信度网络方法

针对发动机及其部件试验传感器数据证实的多源证据融合问题,描述了建立贝叶斯信度网络的方法,给出了传感器状态和检验关系式不确定性信息表达方法,发展了自动建立贝叶斯信度网络、计算可信度概率及更新网络的算法;给出了贝叶斯信度网络方法在模型发动机上的应用示例。     

基于贝叶斯网络的自动装弹机维修决策

在分析贝叶斯网络的结构和特点的基础上,以自动装弹机的部件输弹机为例,构建了输弹机不旋转故障模式的贝叶斯网络模型,并对网络模型进行训练和学习,在计算网络模型相关参数的基础上进行维修决策分析,梳理出排除输弹机不旋转故障的一般性方法,为研究自动装弹机的维修决策提供参考。     

改进Laplace先验下的复数域多任务贝叶斯压缩感知方法

为了将现有的实数域贝叶斯压缩感知方法推广至复数域,利用改进Laplace先验假设,提出了一种复数域多任务贝叶斯压缩感知(complex multitask Bayesian compressive sensing using modified Laplace priors, CMBCS-MLP)方法,消除了测量噪声方差的影响,并推导了一种基于递归操作的快速算法。数值仿真表明：针对复数域稀疏信号重构问题,相比于现有方法,所提CMBCS-MLP方法具有更好的精确性和鲁棒性。