武器装备基于状态的维修系统设计

为了减少武器装备的故障以及维修时间,提高武器装备的可用度和重要部件的使用寿命,采用基于状态的维修技术与方法已成为当前维修领域研究与应用的热点.从武器装备的维修需求与技术出发,分析了现行装备维修的主要方式及其优缺点,总结了当前基于状态的维修(CBM)研究与应用现状,在此基础上提出了武器装备基于案例的CBM系统框架,给出了CBM适用的条件,并对CBM系统的核心模块进行了分析,给出了CBM系统工作的流程.最后,结合某装备中一齿轮箱的状态检测信息,进行了基于声音的装备故障诊断与案例分析与决策.分析结果表明:基于案例的CBM系统简单实用,能够满足装备维修需求.     

考虑生产任务的制造设备关键部件的机会维修优化

为解决制造设备关键部件的维修决策问题,将生产任务信息、视情维修以及机会维修相结合,考虑设备关键部件的剩余价值以及可靠性风险建立维修决策优化模型.在已有关于视情维修和机会维修成果的基础上,考虑关键部件的剩余寿命与下一阶段生产任务时长间的关系,以总成本最小化为目标确定是否在相邻生产任务间利用维修机会,使得在任务顺利进行的条件下降低成本.基于逆高斯过程进行部件退化建模,计算不同维修组合对应的失效概率,进而构建成本最小化目标规划函数并通过仿真算法得到预防性维修的最优值.最后通过数值实验验证所提出维修策略的有效性.     

An overview of time-based and condition-based maintenance in industrial application

This paper presents an overview of two maintenance techniques widely discussed in the literature: time-based maintenance (TBM) and condition-based maintenance (CBM). The paper discusses how the TBM and CBM techniques work toward maintenance decision making. Recent research articles covering the application of each technique are reviewed. The paper then compares the challenges of implementing each technique from a practical point of view, focusing on the issues of required data determination and collection, data analysis/modelling, and decision making. The paper concludes with significant considerations for future research. Each of the techniques was found to have unique concepts/principles, procedures, and challenges for real industrial practise. It can be concluded that the application of the CBM technique is more realistic, and thus more worthwhile to apply, than the TBM one. However, further research on CBM must be carried out in order to make it more realistic for making maintenance decisions. The paper provides useful information regarding the application of the TBM and CBM techniques in maintenance decision making and explores the challenges in implementing each technique from a practical perspective.     

基于蒙特卡洛法的保障方案评估研究

为保证经费使用效益，针对目前装备保障缺乏有效的评估方法，且制定保障方案时随意性较大的问题，提出了一种基于蒙特卡洛法的装备保障方案评估方法。根据现有装备保障的运行体制和机制，通过构建装备保障评估仿真模型，建立相对应的预防性维修过程子模型、修复性维修过程子模型和保障资源配置子模型，计算出基于现有保障方案的优化费效曲线及相关参数。通过某型飞机的保障方案实际计算，该方法能够对保障方案的备件、资源、设备利用率等方面进行评估，为任务执行前的保障方案评估提供了有效手段。提出的模型和软件能为装备保障人员提供有力的决策支持。     

装备维修保障模型适用性的评价方法

针对装备维修保障分析中模型选择缺乏考虑模型适用性的问题,论文提出一种评价模型适用性的方法。从模型的输人、输出和模型本身三个不同的角度,分别研究数据的可采集性、结果的不确定性和模型的成熟度,得到装备维修保障模型适用性评价的决断图。该方法有助于在维修保障分析中选取适用性更好的装备维修保障模型。     

Condition based maintenance optimization considering multiple objectives

In condition based maintenance (CBM) optimization, the main optimization objectives include maximizing reliability and minimizing maintenance costs, which are often times conflicting to each other. In this work, we develop a physical programming based approach to deal with the multi-objective condition based maintenance optimization problem. Physical programming presents two major advantages: (1) it is an efficient approach to capture the decision makers’ preferences on the objectives by eliminating the iterative process of adjusting the weights of the objectives, and (2) it is easy to use in that decision makers just need to specify physically meaningful boundaries for the objectives. The maintenance cost and reliability objectives are calculated based on proportional hazards model and a control limit CBM replacement policy. With the proposed approach, the decision maker can systematically and efficiently make good tradeoff between the cost objective and reliability objective. An example is used to illustrate the proposed approach.     

无线传感器网络设备维修周期决策及支持软件研究*

无线传感器网络设备因为数量庞大,维修维护事项庞杂,维修周期需要决策优化。基于状态维修理论研究了无线传感器网络设备面向节能减排的维修周期决策问题,给出了三类事项的费用模型和基于粒子群算法的智能优化方法;基于WinCC开发了维修决策支持软件系统、软件所集成的事项知识和所决策的维修时机,可以有效地指导无线传感器网络设备的维修工作。     

Industrial maintenance decision-making: A systematic literature review

The increasing competition among industries has leveraged the emergence of various tools and methods for maintenance decision-making support. This paper identifies in literature the application areas of industrial maintenance decision-making, the relationships between these areas and the ways in which authors integrate tools and methods. This information makes it possible to identify trends and deficiencies in this context, helping to centralize the efforts required for future work. This work follows a series of structured steps for a systematic literature review of papers related to the main topic available in online databases. The selected papers are subject to a content assessment and grouped according to the application areas. The direct comparison between these areas and the construction of a relational matrix provide a quantitative interpretation of the results and well-structured information. Additionally, this paper proposes a framework based on information from the literature, which summarizes the origin and flow of information used in the development of models, showing the relationship among application areas of decision making. The research undertaken identifies trends focused on joint production systems optimization and increasing the deployment of methods for autonomous equipment predictions.     

SOAP技术在OSA-CBM系统中的应用

基于状态的维护(CBM)是一种新的设备维护方式,国际上的研究小组正致力于CBM系统开发标准的制定,提出了一种开放式体系结构OSA-CBM。本文探讨了SOAP技术在开放式CBM系统中的应用,设计了基于SOAP的CBM系统通信模型,并给出了实现该通信模型的具体方法和步骤。     

不完美维护下基于剩余寿命预测信息的设备维护决策模型

基于剩余寿命预测信息进行设备维护决策的研究中,现有方法通常仅考虑不完美维护对退化量或退化率的单一影响,忽略了不完美维护对两者的双重影响.鉴于此,针对随机退化设备,提出一种考虑不完美维护影响的性能退化模型与维护决策模型,融合了维护活动对设备退化量和退化率的双重影响.首先基于Wiener过程分阶段构建存在不完美维护干预的随机退化模型,在首达时间的意义下推导出剩余寿命的解析概率分布;然后基于剩余寿命的预测结果,以检测间隔和预防性维护阈值为决策变量建立维护决策模型;最后数值仿真实验验证了本文模型的有效性,并对费用参数进行了敏感性分析.实验结果表明本文模型具有潜在的工程应用价值.     

基于GQFD的无人智能化保障装备能力需求分析

着眼无人智能化保障装备发展问题,为构建智能高效的保障装备体系,在对智能化战争条件下装备保障“任务—能力”进行分析,确定感知战场态势、辅助指挥决策、装备检测维修、物资精准保障4项智能化保障任务和大数据分析处理能力、智能决策能力、多源数据采集能力等9种无人智能化保障装备能力的基础上,运用基于灰色关联分析的质量功能展开方法,建立包含灰色综合关联矩阵的无人智能化保障装备能力需求分析质量屋,得到无人智能化保障装备能力指标重要度排序,大数据分析处理能力重要度为6.890 6,居于首位,该能力是保障装备开展所有保障活动的基础,符合智能化装备保障“以智取胜”的致胜机理;结果表明该模型具有较好的可行性和适用性,验证了该方法在无人智能化保障装备能力需求分析中的可行性和有效性,为下一步无人智能化保障装备建设规划提供了有力参考。     

Modeling risk based maintenance using fuzzy analytic network process

The study deals with the problem of maintenance policy selection for an industrial unit. Maintenance policy selection is a multiple criteria decision making problem. Criteria considered here are ‘risk of equipment failure’ and the ‘cost of maintenance’. The maintenance policies considered are Corrective Maintenance (CM), Time Based Maintenance (TBM), Condition Based Maintenance (CBM) and Shutdown Maintenance (SM). For modeling, fuzzy analytic network process (FANP) has been employed. Chang’s extended analysis has been applied to deal with the fuzzy variables and the preferred maintenance policy alternative is found out using FANP analysis. The methodology was applied to a unit of a chemical plant and the suitable maintenance policy was found out for each of the 13 equipment of the unit. The results were compared to the earlier study using Analytic Hierarchal Process and Goal Programming (Arunraj and Maiti, 2010) vis-a-vis the existing practices. The results show that CBM is preferred when the risk possessed by an equipment is very high while CM is preferred in those cases where risk is low and cost is the main consideration. But in cases where both cost and risk are somewhat equally important, TBM is the preferred option.     

战时装备维修保障能力和保障程度计算方法研究

研究了适应装备保障体系的维修保障能力分析方法,提出了维修保障力量多余舍去的维修保障程度计算方法,并以实例对相关理论和算法进行了论证。该方法结合部队实际情况对传统计算方法进行了改进和完善,适应了装备保障体系的需要,为战时维修保障部门科学决策提供了理论依据。     

认知不确定性条件下轨道交通信号系统关键设备维修方案决策

基于Vague集的模糊决策在处理不确定及不精确信息时考虑了方案满足目标的不可能性因而优于传统的Fuzzy集。本文针对轨道交通信号系统中的无线闭塞中心系统设备的复杂性与故障相关信息的不确定性和模糊性问题,提出了一种认知不确定性条件下基于Vague集的设备维修策略决策方法。该方法以立即停车维修、优先安排维修、视情维修、定期维修和延迟维修等5种典型维修方案作为无线闭塞中心关键设备维修的对策集;在兼顾安全性和经济性的基础上,给出了安全性、可靠性、维修性、经济性以及可监测性等5个定性的决策评价语言变量集及相应的Vague数转换方法;构造了维修策略的Vague决策局势效果向量,并以接近正负理想Vague局势效果向量的相对贴近度为决策依据进行最优维修策略的决策。决策实例说明了该方法的具体实现过程,并验证了其有效性。     

An intelligent condition-based maintenance platform for rotating machinery

Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.     

Equipment PHM using non-stationary segmental hidden semi-Markov model

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains where safety, reliability, and availability of the systems are considered mission critical. As a key complement to CBM, prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient faults. Conducting successful prognosis, however, is more difficult than conducting fault diagnosis. A much broader range of asset health related data, especially those related to the failures, shall be collected. The asset health progression can then be possibly extracted from the congregated data, which has proved to be very challenging. This paper presents a non-stationary segmental hidden semi-Markov model (NSHSMM) based prognosis method to predict equipment health. Unlike previous HSMMs, the proposed NSHSMM no longer assumes that the state-dependent transition probabilities keep the same value all the time. That is, the probability of transiting to a less healthy state does not increase with the age. “Non-stationary” means the transition probabilities will change with time. In the proposed method, in order to characterize a deteriorating equipment, three kinds of aging factor that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are introduced. The performances of these aging factors are compared by using historical data colleted from three hydraulic pumps. The hazard function (h.f.) has been introduced to analyze the distribution of lifetime with a combination of historical failure data and on-line condition monitoring data. Using h.f., PHM is based on a failure rate that is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in PHM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The estimated state duration probability distributions can be used to predict the remaining useful life of the systems. With the equipment PHM, the behavior of the equipment condition can be predicted.     

考虑设备随机故障的装备维修作业调度方法

装备维修过程中,维修设备随机故障这一不确定因素易对正常的维修计划造成影响。为了优化制定维修设备随机故障条件下的维修作业调度决策方案,依据维修设备的故障特点,提出一种基于组合调度策略的维修作业调度方法。调度方法兼顾完成时间与鲁棒性两方面目标,通过调度优化模型生成调度决策方案,并采用一种组合调度策略进行方案调整以改善决策方案的鲁棒性。通过随机故障事件模拟实验与分析,验证了调度方法能有效地减少设备随机故障造成的工序延误。     

集成状态监测和故障诊断的设备管理平台的建立

针对目前设备状态维修中故障诊断和维修管理相互脱节的问题，在状态维修思想的指导下，构建了实施状态维修的设备管理平台，给出了系统平台的集成架构。结合发电企业设备维修的实际，提出了状态维修实施的信息模型。运用IDEF方法对整个设备管理平台的总体功能进行了详细分析，建立了系统的功能模型。在构建系统模型的基础上，综合采用网络技术、计算机技术和数据库技术等开发出了集成状态监测和故障诊断的设备管理系统，为企业成功开展状态维修提供了有效的管理平台。     

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model （AR-HSMM）

As a new maintenance method, CBM （condition based maintenance） is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac- tice in industry is effective diagnostics and prognostics. Recently, a pattern recog- nition technique called HMM （hidden Markov model） was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip- ment health management based on auto-regressive hidden semi-Markov model （AR-HSMM）. Compared with HMM, AR-HSMM has three advantages： 1） It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2） It can relax observations＇ independence assumption by accom- modating a link between consecutive observations. 3） It does not follow the unre- alistic Markov chain＇s memoryless assumption and therefore provides more pow- erful modeling and analysis capability for real problems. To facilitate the computa- tion in the proposed AR-HSMM-based diagnostics and prognostics, new forward- backward variables are defined and a modified forward-backward algorithm is de- veloped. The evaluation of the proposed methodology was carried out through a real world application case study： health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision- making in equipment health management.     

军械装备维修资源需求仿真系统研究与实现

从军械装备维修保障的实际需求出发,在对维修保障系统及其运行过程分析的基础上,利用面向事件的仿真时钟推进机制建立了仿真模型,并给出了相关算法.介绍了软件系统的结构组成和数据库设计方法,并利用VB6.0和ACCESS2000实现了该系统.该系统可实现对一定编制装备群平时的维修保障过程进行快速仿真,得到各保障级别上维修资源的需求以及装备完好率信息,为维修保障决策提供科学依据.最后的案例应用验证了系统的有效性与准确性.