基于时间延迟维修理论检查模型的比较研究

主要解决设备故障停机时间较长情况下如何修正基本检查模型并优化维修间隔期的决策问题.首先阐述了时间延迟概念及其原理,建立了基于时间延迟维修理论的单元件系统基本检查模型,该基本检查模型的建立是基于设备故障停机时间相对检查周期较短的情况,提出了缺陷在故障停机期间继续发生的假定条件,该假定条件并不适合故障停机时间较长的情况,为此提出了只有在设备运行期间才会发生缺陷的假定条件并修正了基本检查模型,根据修正后的检查模型估算出检查周期内故障次数期望值.通过案例比较基本检查模型与修正模型之间的差异,计算结果表明,修正后的模型在检查周期的确定、故障停机时间期望值的计算方面具有更高的精度.     

利用主观数据建立预防维修的优化模型

本文利用维修工程师的经验数据建立了一个较实用的预防维修模型.首先对预防维修进行技术经济分析,在此基础上建立了目标函数;然后阐述了用主观数据建立预防维修优化模型的原理,详细说明了缺陷发生率估计和延迟时间分布函数确定的方法.该模型描述了总停机时间与不同维修间隔期之间的关系,利用该模型求出合理的维修间隔期,使得单位时间内总的停机时间期望值最小.最后是案例分析.     

运用延迟时间理论建立汽车维修优化模型

本文在对车队维修现状及维修数据进行分析的基础上,阐述了包括缺陷、故障两个因素的时间延迟模型的概念以及延迟时间建模的原理,提出了检查的时间和次数影响汽车故障的关系,进而建立了汽车维修优化模型.在满足一些假定的检查原则下,这一模型能够确定设备的维修周期等重要维修保养要素.文中最后运用这一优化模型对汽车维修进行了实例研究,得出了优化的汽车维修周期.     

自动生产线的同步维修模型及实例研究

自动生产线故障停机时间长、停机费用高,其原因在于:设备种类多且相关联,存在各设备协同维修的难题。首先,寻求生产线故障时间和检查时间之间的平衡点,建立检查周期与总停机时间之间关系的目标函数。其次,借鉴同步维修的"对故障隐患集中式批处理,减少分散维修的停机时间"思想,以及时间延迟维修理论,建立生产线的同步维修模型,该模型利用生产线每天停机次数、每次停机时间及检查时发现的缺陷数,计算出合理的生产线检查周期,并以此为依据制定维修计划,实现"提高维修批量,减少生产线停机时间"的目标。最后,实例研究。收集某生产线近十年的故障记录数据,在分析生产流程、设备布局、各设备停机影响的基础上,提出对策指导生产线维修管理。     

考虑故障停机的生产控制与维修计划联合决策模型

通过生产控制与维修计划协同决策,降低生产成本。首先描述生产过程,分析各项费用。其次,建立了考虑生产过程失控、故障率和故障停时间的生产过程控制、生产计划优化和维修管理联合优化决策的模型。通过模型求解,联合制定出生产过程检查策略、生产计划（经济生产批量、生产批次）以及维修计划（PM间隔期）,实现单位时间内总费用最低的目标。再次,案例研究,分析生产过程失控、故障率和故障停机时间对于经济生产批量、生产过程检查策略和生产系统维修计划的影响。该模型从理论上解决了生产过程控制、生产计划优化和维修管理联合优化决策难题,对于指导企业制定生产计划和生产系统的检修计划,进而提高产品质量、降低生产成本、确保准时交货都具有指导意义和实用价值。     

灰色周期关联度模型及其应用研究

灰色关联模型从统计序列几何形状的接近性和相似程序来判断其紧密程度,传统的灰色关联模型从统计序列的面积、斜率、变化速率等角度来计算其关联程度,这些关联模型受到统计序列横纵坐标(周期和振幅)的双重影响,这在分析统计序列间的单个因素间的关系时会产生其它因素的干扰。针对这一缺陷,本文提出了周期关联度模型,研究仅受周期因素影响的关联模型,证明该模型只与波形的波动周期和方向有关,不受曲线振幅的影响,且能表示出正负相关性,并应用该模型研究了GDP与居民消费间的周期关联性,取得了良好的效果。     

中国宏观经济的计量经济学模型实证研究

宏观经济学模型，是计量经济学模型研究中的一个重要领域，具有很强的实用价值。本文中，我们首先选取了四部门构成的宏观经济理论，并根据现实的经济情况，构造出了一个联立方程的计量经济学模型，并通过了识别。接下来，我们从2007年的《中国统计年鉴》中，筛选出需要的数据，进行实证分析，采用最小二乘法，对模型参数进行了估计，并对估计出的参数进行了四步的检验。最后，我们得出了这个方程，并对这个模型的优缺点进行了讨论。     

财务风险模拟的可行性分析

计算财务风险的传统方法往往是利用统计模型估计出风险的概率分布,本文提出了使用模拟模型的方法来进行财务风险的模拟,并论证了其可行性和优缺点。     

工程机械故障检查判断的基本程序和方法

机械在使用过程中总会发生一些故障。笔者在工作实践中总结出以下经验，只有维修人员深入了解机械发生故障的机理规律和影响因素，才能科学地采取维修技术，才能有效地排除故障和预防同样故障再次出现，保持和恢复机械在使用过程中的良好技术状况。     

基于非参数回归的遗漏变量检验

基于非参数回归提出了同时适用于横截面和时间序列数据的遗漏变量检验统计量.与现有文献相比,该统计量不仅避免了模型设定偏误问题,而且具有更高的局部检验功效,能够识别出速度更快的收敛到原假设的局部备择假设.该文选择单一带宽估计条件联合期望和条件边际期望,允许二者的非参数估计误差共同决定统计量的渐近分布,不仅改善了统计量的有限样本性质,而且避免了选择多个带宽和计算多个偏差项产生的繁杂工作.蒙特卡洛模拟结果表明该统计量具有良好的有限样本性质以及比Ait-Sahalia等更高的检验功效.实证分析采用该统计量捕获了F统计量无法识别的产出缺口与通胀之间关系,验证了非线性“产出一通胀”型菲利普斯曲线在中国的适用性.     

具有隐藏故障的多状态竞争失效系统检测策略研究

复杂工业系统的故障中有40%属于隐藏故障,若隐藏故障得不到及时消除,则可能导致巨大的经济损失。本文针对具有隐藏故障的多状态竞争失效系统,在考虑不完全检测的基础上,对其进行了维修建模。首先,基于制定的视情维修策略,分析了系统的更新情形;然后,基于所推导出的各种系统更新情形的发生概率,建立了以期望成本率为目标函数,以检测间隔时间为决策变量的维修模型。最后,以电容器组为例对所构建维修模型的适用性和有效性进行了验证,研究结果表明,检测质量对检测策略的制定有重要影响,因此研究具有隐藏故障的多状态竞争失效系统的检测策略时,有必要考虑检测误差。本文提出的维修建模方法不仅能在一定程度上丰富多状态竞争失效系统的维修策略理论与方法,还能为工程实际中复杂工业系统的维修工作提供理论基础与决策依据。     

Risk-Based Integrity and Inspection Modeling (RBIIM) of Process Components/System

Process plants deal with hazardous (highly flammable and toxic) chemicals at extreme conditions of temperature and pressure. Proper inspection and maintenance of these facilities is paramount for the maintenance of safe and continuous operation. This article proposes a risk-based methodology for integrity and inspection modeling (RBIIM) to ensure safe and fault-free operation of the facility. This methodology uses a gamma distribution to model the material degradation and a Bayesian updating method to improve the distribution based on actual inspection results. The method deals with the two cases of perfect and imperfect inspections. The measurement error resulting from imperfect inspections is modeled as a zero-mean, normally distributed random process. The risk is calculated using the probability of failure and the consequence is assessed in terms of cost as a function of time. The risk function is used to determine an optimal inspection and replacement interval. The calculated inspection and replacement interval is subsequently used in the design of an integrity inspection plan. Two case studies are presented: the maintenance of an autoclave and the maintenance of a pipeline segment. For the autoclave, the interval between two successive inspections is found to be 19 years. For the pipeline, the next inspection is due after 5 years from now. Measurements taken at inspections are used in estimating a new degradation rate that can then be used to update the failure distribution function.     

Testing Branching Process Estimators of Cascading Failure with Data from a Simulation of Transmission Line Outages

We suggest a statistical estimator to quantify the propagation of cascading transmission line failures in large blackouts of electric power systems. We use a Galton‐Watson branching process model of cascading failure and the standard Harris estimator of the mean propagation modified to work when the process saturates at a maximum number of components. If the mean number of initial failures and the mean propagation are estimated, then the branching process model predicts the distribution of the total number of failures. We initially test this prediction on failure data generated by a simulation of cascading transmission line outages on two standard test systems. We discuss the effectiveness of the estimator in terms of how many cascades need to be simulated to predict the distribution of the total number of line outages accurately.     

Reliability Analysis Based on the Losses from Failures

The conventional reliability analysis is based on the premise that increasing the reliability of a system will decrease the losses from failures. On the basis of counterexamples, it is demonstrated that this is valid only if all failures are associated with the same losses. In case of failures associated with different losses, a system with larger reliability is not necessarily characterized by smaller losses from failures. Consequently, a theoretical framework and models are proposed for a reliability analysis, linking reliability and the losses from failures. Equations related to the distributions of the potential losses from failure have been derived. It is argued that the classical risk equation only estimates the average value of the potential losses from failure and does not provide insight into the variability associated with the potential losses. Equations have also been derived for determining the potential and the expected losses from failures for nonrepairable and repairable systems with components arranged in series, with arbitrary life distributions. The equations are also valid for systems/components with multiple mutually exclusive failure modes. The expected losses given failure is a linear combination of the expected losses from failure associated with the separate failure modes scaled by the conditional probabilities with which the failure modes initiate failure. On this basis, an efficient method for simplifying complex reliability block diagrams has been developed. Branches of components arranged in series whose failures are mutually exclusive can be reduced to single components with equivalent hazard rate, downtime, and expected costs associated with intervention and repair. A model for estimating the expected losses from early-life failures has also been developed. For a specified time interval, the expected losses from early-life failures are a sum of the products of the expected number of failures in the specified time intervals covering the early-life failures region and the expected losses given failure characterizing the corresponding time intervals. For complex systems whose components are not logically arranged in series, discrete simulation algorithms and software have been created for determining the losses from failures in terms of expected lost production time, cost of intervention, and cost of replacement. Different system topologies are assessed to determine the effect of modifications of the system topology on the expected losses from failures. It is argued that the reliability allocation in a production system should be done to maximize the profit/value associated with the system. Consequently, a method for setting reliability requirements and reliability allocation maximizing the profit by minimizing the total cost has been developed. Reliability allocation that maximizes the profit in case of a system consisting of blocks arranged in series is achieved by determining for each block individually the reliabilities of the components in the block that minimize the sum of the capital, operation costs, and the expected losses from failures. A Monte Carlo simulation based net present value (NPV) cash-flow model has also been proposed, which has significant advantages to cash-flow models based on the expected value of the losses from failures per time interval. Unlike these models, the proposed model has the capability to reveal the variation of the NPV due to different number of failures occurring during a specified time interval (e.g., during one year). The model also permits tracking the impact of the distribution pattern of failure occurrences and the time dependence of the losses from failures.     

Statistical Process Control and Condition‐Based Maintenance: A Meaningful Relationship through Data Sharing

This paper focuses on the close relationship between statistical process control and preventive maintenance (PM) of manufacturing equipment. The context is very general: a production process that is characterized by multiple distinct operational states and a failure state. The operational states differ in terms of operational/quality costs and/or the proneness to complete failure. The times of shift from the normal operational state to an inferior one and the times to failure are random variables, not necessarily exponentially distributed. The process is monitored with a control chart with the purpose of quickly detecting shifts to an inferior operational state due to the occurrence of some unobservable assignable cause. At the same time, the information collected from the process may be used to re‐schedule the planned PM, if there is evidence that a failure is imminent. The two mechanisms are obviously related, especially if they are based on measurements of the same critical process characteristic. Yet, they are typically treated independently. We develop a fairly general mathematical model for the joint optimization of the control chart parameters and the maintenance times. Numerical investigation using this model shows that ignoring the close relationship between process control and maintenance results in inefficiencies that may be substantial. It also provides practical insights about the effects of some key problem characteristics on the optimal joint design of process control and maintenance.     

基于贝叶斯网络的航空安全中不安全信息分析

针对航空安全中收集到的不安全信息,本文将安全检查发现的问题进行了分类,建立了民航安全检查的故障树模型,同时利用故障树模型进行了最小割集、结构重要度的定性分析。然后利用贝叶斯网络的学习能力结合故障树模型最终构建了民航安全检查贝叶斯网络模型,基于某航空公司安全检查发现问题的数据并利用贝叶斯网络的双向推理计算得到各影响因素对不安全事件的影响程度和不安全事件发生时各影响因素的重要程度。基于民航安全检查贝叶斯网络模型分析,本文得出个体原因、设施不完善、信息沟通因素最易导致工作流程管理中发现问题;管理流程、知识技能不足最易导致人员管理中发现问题;个体原因、培训、设备故障最易导致设备管理中发现问题。     

基于交叉效率DEA与群体共识的区间乘性语言偏好关系群决策

针对区间乘性语言偏好关系群决策问题，提出了一种基于交叉效率DEA和群体共识的群决策方法。首先，提出乘性语言偏好关系导出函数的定义，并构建产出导向的DEA模型，证明了一致性乘性语言偏好关系的DEA效率得分与排序向量之间存在比例关系。在此基础上，建立基于理想值的交叉效率DEA模型，提出乘性语言偏好关系的通用排序方法。同时，基于群体共识建立目标规划模型来计算各语言偏好关系的权重系数。最后，利用Monte Carlo随机模拟的方法对群体语言偏好空间进行统计分析，得到群决策期望排序向量及其可信度。算例分析表明本文方法能够有效的避免信息损失，具有较强的适用性和较高的可信度。     

Optimal policy for imperfect inspection in discrete time

Abstract

The problem of finding the optimal inspection interval for a fallible system that operates in discrete time when inspection is subject to errors was addressed. A Markov chain model of the system state was developed to formulate the objective function, which is the minimum expected total cost per lime unit. A numerical search procedure was used to find local minima for a representative range of parameter values. The objective function turned out to be quite insensitive to deviations in the inspection interval in the neighbourhood of the minimum, so an approximate method based on curve fitting was developed and tested, yielding good results in the representative range.