基于风险和状态的智能维修决策优化系统及应用研究
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摘要
中国过程工业企业设备管理基本属于传统的事后维修模式,设备基础管理薄弱,依靠经验的、定性的方法确定设备检查/维护内容,缺乏对关键设备的识别和分类,维修资源不能合理分配,存在“维修不足”和“维修过剩”,系统安全生产隐患大、事故多,设备可靠性、可用性和安全性难以控制和保证。为保证过程工业安全生产减少安全事故和环境事故,建立了过程工业基于风险和状态的设备智能维修决策及任务优化系统,它以基于风险和状态的设备完整性管理系统为架构,利用物联网技术和面向服务的架构技术综合集成了企业资源计划系统(Enterprise Resource Planning, ERP)、企业生产执行系统(Manufacturing Executive System, MES)和基于风险的维修(Risk Based Maintenance, RBM)系统与状态监测预知维修信息系统(Predictive Maintenance Information Systems, PMIS),为企业提供设备动态风险等级数据、预知维修信息数据、设备绩效指标数据、可靠性预测数据、设备剩余工作寿命数据,使各级人员能够通过网络平台及时准确地掌握设备风险状态和优化的维修任务排程,为设备维修决策提供科学支持。主要研究工作概括如下：
(1)基于风险的维修研究和软件开发
根据石油炼制、石油化工等过程工业设备管理特点,研究了适合过程工业设备管理特点的基于风险的维修风险评价技术,开发了基于风险的维修(RBM)软件,建立了基于风险的维修决策模型。
(2)基于风险和状态的智能维修决策系统优化研究
利用物联网技术搭建了基于风险和状态的设备智能维修决策系统。利用计算机技术、服务接口技术、数据库技术、有线或无线网络技术,基于面向服务的架构(Service Oriented Architecture, SOA)综合集成了PMIS、MES、RBM等系统模块,搭建了“以风险管理和核心,以专业管理为主线”的设备智能维修决策平台。该系统能够提供设备预知维修决策指标、动态风险等级指标、关键绩效指标,为基于风险和状态的维修决策提供定量分析数据。
(3)基于风险的维修动态评估和设备管理绩效指标研究
针对具体设备类型研究了可靠性数据、维修数据采集和交换内容；针对过程工业设备管理特点研究了设备动态风险变化影响因子(管理因子、个别设备修正因子)和动态风险评价技术；在对设备故障数据、维修数据进行分析的基础上,研究了过程工业设备绩效指标评估体系(设备、装置和公司三级)和设备管理绩效指标决策模型、绩效指标可靠性预测模型。
(4)基于风险和状态的智能维修任务优化研究
利用威布尔分布分析工具对平均故障间隔时间(Mean Time Between Failure, MTBF)、可靠性(Reliability)等动态数据进行分析,实现了设备的可靠性预测；利用主元分析方法确定设备故障特征参数,基于人工神经网络、灰色理论、曲线回归拟合和时间序列建模等方法跟踪设备故障特征参数劣化趋势,实现了设备剩余工作寿命预测；利用可靠性预测和剩余工作寿命预测实现了设备维修内容和故障维修间隔周期的优化；在设备动态风险分析和预知维修决策指标模型、管理绩效决策指标模型、设备可靠性预测模型、剩余工作寿命预测模型建立的基础上,以基于风险和状态的智能维修决策系统为平台,建立了基于风险和状态的设备维修任务优化模型。
(5)基于风险和状态的设备智能维修决策系统工程应用
基于风险和状态的设备智能维修决策系统综合集成了企业现有的ERP、MES、PMIS等设备管理信息资源,既考虑企业传统的设备管理现状,又引进了先进的RBM等设备风险管理技术；既考虑到设备定量风险分析缺乏可靠性数据和维修数据,又考虑到建立设备管理绩效指标的重要性；既强调建立的系统要具有动态风险等级指标、预知维修指标、设备管理绩效指标、可靠性和剩余工作寿命预测指标决策模型实现智能维修决策,又强调领导的强力支持、持续的培训和教育是基于风险和状态的维修管理模式成功应用的保证。锦州石化公司工程应用实践表明：建立的设备维修智能决策信息系统对于提高设备可靠性、可用性和安全性产生了积极效果,它使设备故障频率降低、故障后果减小,维修资源得到了合理利用。
Equipment management in Chinese process industry mostly belongs to the traditional breakdown maintenance pattern, and the basic inspection/maintenance decision-making is insufficient. Equipment inspection/maintenance tasks are mainly based on empirical or qualitative methods, which usually lack identification and classification of critical equipment, so that maintenance resources can't be reasonably allocated. Reliability, availability and safety of equipment are difficult to control and guarantee due to the existing maintenance deficiencies, maintenance surplus, potential danger and possible accidents. In order to ensure stable manufacturing and reduce operation cost, a risk & condition based equipment intelligent maintenance and decision-making optimization system is established in this paper, which utilizes risk & condition based equipment integrity management system as the architecture, and integrates ERP, MES (Manufacturing Executive System), RBM (Risk Based Maintenance)and PMIS (Predictive Maintenance Information System) through IOT (Internet Of Things) and SOA. This system can provide dynamic risk rank data, predictive maintenance information data, equipment performance index data, reliability prediction data and equipment residual life data, thus making personnel at all levels master equipment risk rank and optimized maintenance tasks in time and providing scientific support to maintenance decision-making. The main contents in the paper include:
(1) Research on risk-based maintenance and software development
According to the characteristics of equipment management in process industry such as petroleum refining and petrochemical enterprises, this paper investigates relevant risk-based maintenance risk evaluation methods, develops risk-based maintenance (RBM) software and establishes risk-based maintenance and decision-making model.
(2) Research on risk & condition based intelligent maintenance and decision-making system optimization
Using IOT, a risk & condition based intelligent maintenance and decision-making system is set up. Meanwhile, PMIS, MES and RBM modules are integrated on the basis of SOA by adopting computer technology, interface technology, database technology and cable/wireless technology, so that the equipment intelligent maintenance and decision-making platform (characterized by "risk management as the core, professional management as the main line") is formulated. This platform can provide predictive maintenance and decision-making indicator, dynamic risk rank indicator, key performance indicator as well as quantitative analysis data.
(3) Research on risk-based dynamic maintenance evaluation and equipment management performance index
For specific equipment types, data collection and data exchange of reliability data and maintenance data are researched; for the management characteristics in process industry equipment, dynamic risk variation influence factors (including management factor and individual equipment modifying factor) and dynamic risk evaluation techniques are investigated; on the basis of failure data and maintenance data, equipment performance indicator evaluation system (comprising equipment level, device level and company level), equipment management performance indicator decision-making model and performance indicator reliability prediction model are all studied.
(4) Research on risk & condition based intelligent maintenance task optimization
Using Weibull Distribution probability analyzing tool, analysis of MTBF (mean time between failure) and reliability data are carried out, thus realizing reliability prediction; using PCA (Principal Component Analysis), equipment failure features are determined; moreover, we trace the degradation trend of failure features by neural network, grey theory, curvilinear regression and time series modeling, thus realizing equipment residual life prediction; equipment maintenance content and maintenance period are optimized thanks to reliability prediction and residual life prediction; based on dynamic risk analysis and predictive maintenance and decision-making indicator model, management performance decision-making indicator model, equipment reliability prediction model and residual life prediction model, with risk & condition based intelligent maintenance and decision-making system as the platform, a risk & condition based intelligent maintenance task optimization system is established.
(5) Engineering application of risk & condition based intelligent maintenance and decision-making system
The system combines the existing ERP, MES, EAM and PMIS resources, and pays special attention to many aspects. Firstly, it considers traditional equipment management status, and introduces advanced RBM, RBI and SIL risk management techniques. Secondly, it takes into account the lack of reliability data and maintenance data in quantitative risk analysis, and gives attention to the importance of setting up management performance indicator. Thirdly, it emphasizes that the system should have the ability to realize intelligent decision-making based on dynamic risk rank indicator, preventive maintenance indicator, management performance indicator and residual life prediction indicator model, while advocating firm support from the leaders and continual training and education, which are important to the successful application of the system. The engineering application case in Petrochina Jinzhou Petrochemical shows that the establishment of the risk & condition based intelligent maintenance and decision-making system has brought about positive effect on the reliability, availability and safety of the equipment, lowering failure frequency, minimizing failure consequence and reasonably allocating maintenance resources.
(1)基于风险的维修研究和软件开发
根据石油炼制、石油化工等过程工业设备管理特点,研究了适合过程工业设备管理特点的基于风险的维修风险评价技术,开发了基于风险的维修(RBM)软件,建立了基于风险的维修决策模型。
(2)基于风险和状态的智能维修决策系统优化研究
利用物联网技术搭建了基于风险和状态的设备智能维修决策系统。利用计算机技术、服务接口技术、数据库技术、有线或无线网络技术,基于面向服务的架构(Service Oriented Architecture, SOA)综合集成了PMIS、MES、RBM等系统模块,搭建了“以风险管理和核心,以专业管理为主线”的设备智能维修决策平台。该系统能够提供设备预知维修决策指标、动态风险等级指标、关键绩效指标,为基于风险和状态的维修决策提供定量分析数据。
(3)基于风险的维修动态评估和设备管理绩效指标研究
针对具体设备类型研究了可靠性数据、维修数据采集和交换内容；针对过程工业设备管理特点研究了设备动态风险变化影响因子(管理因子、个别设备修正因子)和动态风险评价技术；在对设备故障数据、维修数据进行分析的基础上,研究了过程工业设备绩效指标评估体系(设备、装置和公司三级)和设备管理绩效指标决策模型、绩效指标可靠性预测模型。
(4)基于风险和状态的智能维修任务优化研究
利用威布尔分布分析工具对平均故障间隔时间(Mean Time Between Failure, MTBF)、可靠性(Reliability)等动态数据进行分析,实现了设备的可靠性预测；利用主元分析方法确定设备故障特征参数,基于人工神经网络、灰色理论、曲线回归拟合和时间序列建模等方法跟踪设备故障特征参数劣化趋势,实现了设备剩余工作寿命预测；利用可靠性预测和剩余工作寿命预测实现了设备维修内容和故障维修间隔周期的优化；在设备动态风险分析和预知维修决策指标模型、管理绩效决策指标模型、设备可靠性预测模型、剩余工作寿命预测模型建立的基础上,以基于风险和状态的智能维修决策系统为平台,建立了基于风险和状态的设备维修任务优化模型。
(5)基于风险和状态的设备智能维修决策系统工程应用
基于风险和状态的设备智能维修决策系统综合集成了企业现有的ERP、MES、PMIS等设备管理信息资源,既考虑企业传统的设备管理现状,又引进了先进的RBM等设备风险管理技术；既考虑到设备定量风险分析缺乏可靠性数据和维修数据,又考虑到建立设备管理绩效指标的重要性；既强调建立的系统要具有动态风险等级指标、预知维修指标、设备管理绩效指标、可靠性和剩余工作寿命预测指标决策模型实现智能维修决策,又强调领导的强力支持、持续的培训和教育是基于风险和状态的维修管理模式成功应用的保证。锦州石化公司工程应用实践表明：建立的设备维修智能决策信息系统对于提高设备可靠性、可用性和安全性产生了积极效果,它使设备故障频率降低、故障后果减小,维修资源得到了合理利用。
Equipment management in Chinese process industry mostly belongs to the traditional breakdown maintenance pattern, and the basic inspection/maintenance decision-making is insufficient. Equipment inspection/maintenance tasks are mainly based on empirical or qualitative methods, which usually lack identification and classification of critical equipment, so that maintenance resources can't be reasonably allocated. Reliability, availability and safety of equipment are difficult to control and guarantee due to the existing maintenance deficiencies, maintenance surplus, potential danger and possible accidents. In order to ensure stable manufacturing and reduce operation cost, a risk & condition based equipment intelligent maintenance and decision-making optimization system is established in this paper, which utilizes risk & condition based equipment integrity management system as the architecture, and integrates ERP, MES (Manufacturing Executive System), RBM (Risk Based Maintenance)and PMIS (Predictive Maintenance Information System) through IOT (Internet Of Things) and SOA. This system can provide dynamic risk rank data, predictive maintenance information data, equipment performance index data, reliability prediction data and equipment residual life data, thus making personnel at all levels master equipment risk rank and optimized maintenance tasks in time and providing scientific support to maintenance decision-making. The main contents in the paper include:
(1) Research on risk-based maintenance and software development
According to the characteristics of equipment management in process industry such as petroleum refining and petrochemical enterprises, this paper investigates relevant risk-based maintenance risk evaluation methods, develops risk-based maintenance (RBM) software and establishes risk-based maintenance and decision-making model.
(2) Research on risk & condition based intelligent maintenance and decision-making system optimization
Using IOT, a risk & condition based intelligent maintenance and decision-making system is set up. Meanwhile, PMIS, MES and RBM modules are integrated on the basis of SOA by adopting computer technology, interface technology, database technology and cable/wireless technology, so that the equipment intelligent maintenance and decision-making platform (characterized by "risk management as the core, professional management as the main line") is formulated. This platform can provide predictive maintenance and decision-making indicator, dynamic risk rank indicator, key performance indicator as well as quantitative analysis data.
(3) Research on risk-based dynamic maintenance evaluation and equipment management performance index
For specific equipment types, data collection and data exchange of reliability data and maintenance data are researched; for the management characteristics in process industry equipment, dynamic risk variation influence factors (including management factor and individual equipment modifying factor) and dynamic risk evaluation techniques are investigated; on the basis of failure data and maintenance data, equipment performance indicator evaluation system (comprising equipment level, device level and company level), equipment management performance indicator decision-making model and performance indicator reliability prediction model are all studied.
(4) Research on risk & condition based intelligent maintenance task optimization
Using Weibull Distribution probability analyzing tool, analysis of MTBF (mean time between failure) and reliability data are carried out, thus realizing reliability prediction; using PCA (Principal Component Analysis), equipment failure features are determined; moreover, we trace the degradation trend of failure features by neural network, grey theory, curvilinear regression and time series modeling, thus realizing equipment residual life prediction; equipment maintenance content and maintenance period are optimized thanks to reliability prediction and residual life prediction; based on dynamic risk analysis and predictive maintenance and decision-making indicator model, management performance decision-making indicator model, equipment reliability prediction model and residual life prediction model, with risk & condition based intelligent maintenance and decision-making system as the platform, a risk & condition based intelligent maintenance task optimization system is established.
(5) Engineering application of risk & condition based intelligent maintenance and decision-making system
The system combines the existing ERP, MES, EAM and PMIS resources, and pays special attention to many aspects. Firstly, it considers traditional equipment management status, and introduces advanced RBM, RBI and SIL risk management techniques. Secondly, it takes into account the lack of reliability data and maintenance data in quantitative risk analysis, and gives attention to the importance of setting up management performance indicator. Thirdly, it emphasizes that the system should have the ability to realize intelligent decision-making based on dynamic risk rank indicator, preventive maintenance indicator, management performance indicator and residual life prediction indicator model, while advocating firm support from the leaders and continual training and education, which are important to the successful application of the system. The engineering application case in Petrochina Jinzhou Petrochemical shows that the establishment of the risk & condition based intelligent maintenance and decision-making system has brought about positive effect on the reliability, availability and safety of the equipment, lowering failure frequency, minimizing failure consequence and reasonably allocating maintenance resources.
引文
[1]McNEENEY A, Meriduium, Roanoke. Improve asset performance management [J]. Hydrocarbon Processing,2005(12),61-67
[2]Gao J J, Yang J F, Jiang Z N, et al. Risk based dynamic intelligent maintenance system for process industry in China. The third world conference on engineering assets management and intelligent maintenance system [C].2008:76-87
[3]Kwon H-m. The effectiveness of process safety management regulation for chemical industry in Korea [J]. Journal of Loss Prevention in the Process Industries,2006,19,13-16
[4]Rausand M. Reliability centered maintenance [J]. Reliability Engineering and System Safety.1998,60:121-132
[5]Moubray J,石磊译.以可靠性为中心的维修[M].北京：机械工业出版社,1995.1-9
[6]Pujadas W, Chen F F. A Reliability Centered Maintenance strategy for a discrete part manufacturing facility [J]. Computers ind. Engng,1996,31:241-244
[7]Richet D, Cotaina N, Gabriel M, et al. Application of Reliability Centered Maintenance in the foundry sector [J]. Control Eng. Practice,1995,3:1029-1034
[8]贾希胜,程中华.以可靠性为中心的维修(RCM)发展动态[J].军械工程学院学报,2002,14(3)：29-32
[9]戴树和.工程风险分析技术[M].北京：化学工业出版社,2007
[10]CEN Workshop 24. Risk-Based Inspection and Maintenance Procedures for European Industry[S].2007
[11]NAVAIR 00-25-403. Guidelines for the NAVAL AVIATION Reliability-Centered Maintenance Process[S].2003
[12]SAE JA1012. A Guide to the Reliability-Centered Maintenance (RCM) Standard[S].2002
[13]MSG-3. Maintenance Program Development Document[S].1993
[14]GJB/Z 1391-2006.故障模式、影响及危害分析指南[S].2006
[15]赵爱国.设备运行阶段风险管理研究[D].天津：天津大学,2006
[16]陆东,牟善军.欧洲设备风险检查技术发展及应用概况[J].安全、健康和环境,2004,4(1)：36-39
[17]Bareib J, Buck P, Matschecko B, et al. RIMAP demonstration project. Risk-based life management of piping system in power plant Heilbronn [J]. International Journal of Pressure Vessels and Piping,2004,81:807-813
[18]ISO 14224:1999, IDT. Petroleum and natural gas industries-Collection and exchange of reliability and maintenance data for equipment [S].1999
[19]ReliaSoft.RCM++5 Training Guide[M].RSP ReliaSoft Publishing,2010
[20]徐习东,朱峻永,吕一农,等.一种改进的RCM方法-精简型RCM(SRCM) [J].华北电力技术,2006,3：43-45.
[21]SKF SRCM流程模型.SKF资产管理服务维修策略评估[EB/OL].http://www.skf.com /files/690347.pdf
[22]Delta consult B.V..ASMT5.0用户手册[EB/OL].http://www.AssetMST.com
[23]徐小力,徐勇,王信义.机器工作状态趋势预测技术的研究[J].工业仪表与自动化装置,1998(3)：9-12
[24]徐小力,梁福平,许宝杰,等.旋转机械状态监测及预测技术的发展与研究[J].建设机械技术与管理,2003,7：20-24
[25]Gupta M M, Rao D H. On the principles of fuzzy neural networks [J]. Fuzzy Sets and Systems,1994,61(1):1-8
[26]Tzafestas S G, Dalianis P J. Fault diagnosis in complex systems using artificial neural networks [J]. Proc. Of the IEEE Conf. on Control Application,1994(2):877-882
[27]王信义,董卫平,朱小燕,等.生产系统中的监控检测技术[M].北京：北京理工大学出版社,1998
[28]李葆文.国际设备管理与维修工程的最新进展综述(十二)[J].中国设备工程,2008,11：66-68
[29]Deshpande V S, Modak J P. Maintenance strategy for tilting table of rolling mill based on reliability consideration [J]. Reliability Engineering and System Safety.2003,80(1):1-18
[30]EPRI. Use of RCM for McGuire nuclear station feed-water system [J].1986, NP-4795
[31]峁定远.概率风险评价技术在以可靠性为中心的维修中的应用研究[D].北京：清华大学,2000
[32]Tong J, Mao D, Xue D. A genetic algorithm solution for a nuclear power plant risk-cost maintenance model [J]. Nuclear Engineering and Design.2004,229(1):81-89
[33]曹钟中,杨昆,傅钟广,等.汽轮机及其辅助设备系统实施RCM的基本策略[J].汽轮机技术,2002,44(2)：106-108
[34]Endrenyi J, Aboresheid S, Allan R N. The present status of maintenance strategies and the impact of maintenance on reliability [J]. IEEE Transaction on power systems.2001, 16(4):638-646
[35]Michel H, Mufeed A. Improving industrial process safety & availability [J].Reliability Engineering,2008,1021-1026
[36]Rodney B. Reliability Program for Plant maintenance [J]. IEEE Industry Applications Magazine,2000,7(5):29-32
[37]王庆锋,杨剑锋,刘文彬,等.过程工业设备维修智能决策系统的开发与应用[J].机械工程学报.2010,46(24)：168-177.
[38]董绍华.管道完整性技术与管理实践[C].中国管道安全与高层管理国际研讨会论文集,北京,2005
[39]董绍华,王联伟,费凡.油气管道完整性管理体系[J].油气储运,2010,29(9)：641-647
[40]董绍华,杨祖佩.全球油气管道完整性技术与管理的最新进展[J].油气储运,2006,26(2)：1-17
[41]李葆文.国际设备管理与维修工程的最新进展综述(三)[J].国外设备工程,2008,27-29Procaccia H, Cordier R, Muller S. Application of Bayesian statistical decision theory for a
[42]maintenance optimization problem [J]. Reliability Engineering and System Safety,1997, 55(7):143-149
[43]Kallen M J, Noortwijk J M. Optimal Maintenance Decision under imperfect inspection [J]. Reliability Engineering and System Safety,2005,90(6):177-185
[44]Chen D Y, Kishor S. Optimization for condition based maintenance with Semi-Markov decision process [J]. Reliability Engineering and System Safety,2005,38(2):25-29
[45]Basim A, Imad A. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making [J]. Int. J. Production Economics,2003,84(12):85-100
[46]Dipark C. An Algorithm for maintenance and replacement policy using fuzzy set theory [J]. Reliability Engineering and System Safety,1995,50(2):444-451
[47]张永敢,蔡瑞英.基于变精度粗糙集的故障诊断应用研究[J].计算机工程与设计,2009,30(3)：657-659
[48]李葆文.国际设备管理与维修工程的最新进展综述(五)[J].国外设备工程,2008,23-25
[49]李葆文.国际设备管理与维修工程的最新进展综述(八)[J].国外设备工程,2008,43-45
[50]李葆文.国际设备管理与维修工程的最新进展综述(十二)[J].国外设备工程,2008,66-68
[51]Kobbacy KAH, Prodlove N L, Harper M A. Towards an intelligent maintenance optimization system [J]. J.Opl.Res.Soc.,1995(46):831-853
[52]Zhang F, Jardine A. A smart maintenance decision system [C]. In:Kobbacy KAH, Vadera S and Proudlove NC (eds). Proceedings of the First European Conference on Intelligent Management Systems in Operations. Operational Research Society:Birminham, 1997:79-85
[53]Kobbacy KAH, Jeon J. The development of a hybrid intelligent maintenance optimization system (HIMOS) [J]. Journal of the Operational Research Society,2001,52(7):762-778
[54]Kobbacy KAH. On the evolution of an intelligent maintenance optimization system [J]. Journal of the Operational Research Society,2004,55(2):139-146
[55]Daniel J, Fonseca. A knowledge-based system for preventive maintenance [J]. Expert System,2000,17(5):241-247
[56]吴凡,王国庆,傅国强.基于人工智能的机电设备智能保障系统研究[J].计算机测量与控制,2006,14(8)：1046-1048
[57]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究[J].设备管理与维修,2005,(8)：11-13
[58]姜运臣.智能维修决策支持系统的研究[J].锅炉制造,2006,(1)：75-76
[59]藏铁钢,黄金国,张淑猛,等.基于网络的智能维修信息系统的研究与开发[J].机械工程师,2003(5)：6-8
[60]孙建宇.输气管道完整性管理简介[J].石油库与加油站,2009,18(3)：14-17
[61]税碧垣,艾幕阳,冯庆善.油气站场完整性管理技术思路[J].油气储运,2009,28(7)：11-14
[62]牟善军,姜春明.过程安全与设备完整性管理技术[J].安全、健康与环境,2006,6(8)：2-5
[63]帅健.美国油气管道事故及其启示[J].油气储运,2010,29(11)：806-809
[64]张鹏,姜云,段永红,等.新建油气管道完整性管理系统模型设计与开发[J].天然气勘探与开发,2010,33(4)：77-82
[65]董绍华.全球油气管道完整性技术与管理的最新进展与中国管道的对策[J].油气储运,2007(2)：1-17
[66]严大凡,翁永基,董绍华.油气管道风险评价与完整性管理[C].北京：化学工业出版社,2005：9-16
[67]张大海,郭kun,丁继峰.长输天然气管道完整性管理与管道腐蚀检测技术[J].全面腐蚀控制,2010,24(11)：13-16
[68]韩小明,周利剑,冯庆善,等.管道数据的存储与管理[J].地理空间信息,2010,8(6)：86-88
[69]殷苏东,陈旭华.基于状态的维修研究现状与发展趋势[J].科学技术与工程,2008,15(3)：15-18
[70]周尚文.设备维修管理的智能化[J].钢铁技术,2006,14(2)：35-38
[71]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究[J].设备管理与维修,2005,37(1)：11-13
[72]朱清香.基于现代维修理念的决策框架[J].设备管理与维修,2004,25(1)：7-9
[73]陶基斌,郭应征,周太全.基于前馈式神经网络的化工设备维修决策[J].南京化工大学学报,2000,22(5)：11-14
[74]谢庆华,张琦,卢涌.航空发动机单部件视情况维修优化决策[J].解放军理工大学学报(自然科学版),2005,6(6)：575-578
[75]陈玉科.更新型预防维修周期的模糊决策[J].运筹与管理,1999,18(6)：71-75
[76]郭丽杰.基于风险的石化动设备智能维修决策研究[D].北京：北京化工大学,2009
[77]杨剑锋.炼化企业智能维修与安全保障信息系统及工程应用研究[D].北京：北京化工大学,2009
[78]温亮,王丹.基于RCM需求的CMMS系统设计[J].兵工自动化,2006,3：41-42
[79]童晟.构建RCM与CMMS的集成系统[J].现代制造工程,2007,8：41-43
[80]Gao J J, Yang J F, Jiang Z N, et al. Risk based dynamic intelligent maintenance system for process industry in China [C]. The third world conference on engineering assets management and intelligent maintenance system.2008:76-87
[81]高金吉.机泵群实时监测网络和故障诊断专家系统[J].中国工程科学,2001,3(9)：41-47
[82]高金吉.石化设备以可靠性为中心的智能维修系统[J].中国设备工程,2008,(1)：2-4
[83]周洪波.数据交换标准是物联网产业发展的关键[J].信息技术与标准化,2010,(8)：26-29
[84]江泽民.新时期我国信息技术产业的发展[J].上海交通大学学报,2008,42(10)：1589-1607
[85]陈积明,林瑞仲,孙优贤.无线传感器网络的信息处理研究[J].仪器仪表学报,2006,9：129-133
[86]Kwon H-m. The effectiveness of process safety management (PSM) regulation for chemical industry in Korea [J]. Journal of Loss Prevention in the Process Industries,2006, 19:13-16
[87]Deshpande V S, Modak J P. Application of RCM to a medium scale industry [J]. Reliability Engineering and System Safety 2002;77(1):31-43
[88]Rausand M. Reliability centered maintenance [J]. Reliability Engineering and System Safety.1998,60:121-132
[89]Moubary J,石磊译.以可靠性为中心的维修[M].北京：机械工业出版社,1995：1-9
[90]贾希胜.以可靠性为中心的维修决策模型[M].北京：国防工业出版社,2007：1-3
[91]Pujadas W, Chen F F. A Reliability Centered Maintenance strategy for a discrete part manufacturing facility [J]. Computers ind. Engng,1996,31:241-244
[92]Richet D, Cotaina N, Gabriel M, et al. Application of Reliability Centered Maintenance in the foundry sector [J]. Control Eng. Practice,1995,3:1029-1034
[93]Cheng Z, Jia X, Wu S, et al. A framework for intelligent Reliability Centered Maintenance Analysis [J]. Reliability Engineering and System Safety,2008,93:806-814
[94]苏春,黄茁.以可靠性为中心的维修成本优化模型及其应用[J].机械科学与技术,2007,26(12)：1556-1559
[95]左洪福,蔡景,王华伟.维修决策理论与方法[M].北京：航空工业出版社,2008：5-12
[96]李葆文.国外设备管理模式及发展趋势(四)[J].设备管理与维修,2000.10：40-41
[97]温亮,贾希胜.RCM模型决策系统设计[J].计算机工程设计,2006,27(23)：4537-4539
[98]John M. Reliability-centered maintenance[M]. UK:Oxford,1997
[99]贾希胜,贾云献,温亮.以可靠性为中心的维修及其模型支持[J].军械工程学院学报,2004,16(1)：15-18
[100]程中华,贾云献.基于C/S模式的RCM分析决策系统的设计与实现[J].计算机工程,2004,30(13)：174-177
[101]ETI M C, OGAJI S O T, PROBERT S D. Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture [J]. Applied Energy,2006, 83:1235-1248
[102]MASTERS M. Reliability, key to competitive advantage? [J]. PaperAge,1999,9:15-16
[103]ETI M C, OGAJI S O T, PROBERT S D. Integrating reliability, availability, maintainability and supportability with risk analysis for improved operation of the Afam thermal power-station [J]. Applied Energy,2007,84:202-221
[104]姜春明,李奇.过程安全管理与技术的发展与应用[J].中国石油和化工标准与质量,2007：5(45-49)
[105]Gao Jinji, Yang Jianfeng. Research on disaster formation in complex engineering system and self-recovery precaution [J]. China Safety Science Journal,2006,16(9):15-22
[106]Ray S., FIEAust, CPEng. Vibration analysis of pumps-basic [J]. Predicitive Maintenance of Pumps Using Condition Monitoring,2004:83-100
[107]DESHPANDE V S, MODAK J P. Application of RCM to a medium scale industry [J]. Reliability Engineering and System Safety,2002(77):31-43
[108]GAO Jinji. New progress in plant diagnosis engineering-distributed monitoring system and remote diagnosis system [C]. The proceedings of the International Conference on Plant Engineering Guangzhou'97,1997:53-59
[109]高金吉.设备诊断工程与集散监测系统[J].石油化工设备技术,1996,17(3)：11-14
[110]GAO Jinji. A real-time monitoring network and fault diagnosis expert system for compressors and pumps [J]. Engineering Science,2001,3(9):41-47
[111]MARTORELL S, VILLANUEVA J F, CARLOS S et al. RAMS informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms [J]. Reliability Engineering and System Safety, 2005(87):65-75
[112]BLANCHARD B S, VERNA D F, PETERSON E L. Maintainability:A key to effective serviceability and maintenance management [M]. New York:John While and sons,1995
[113]HERDER P M, VAN LUIJK J A, BRUIJNOOGE J. Industrial application of RAM modeling [J]. Reliability Engineering and System Safety,2008(93):501-508
[114]Paul Barringer H, Funkhouser J. Improving refinery reliability, performance and utilization [J]. Hydrocarbon processing,2007(10):73-76
[115]WARBURTON D, STRUTT J E, ALLSOP K. Reliability-prediction procedures for mechanical components at design stage [J]. Proc. Inst. Mech. Eng.,1998(24):212-213
[116]MARTORELL S, SANCHEZ A, MUNOZ A, et al. The use of maintenance indicators to evaluate the effects of maintenance programs on NPP performance and safety [J]. Reliability Engineering and System Safety,1999(65):85-94
[117]Technical Committee CENELEC TC 9X. EN50126-1 Railway applications-the specification and demonstration of reliability, availability, maintainability and safety (RAMS) [S]. London:the authority of the Standards Policy and Strategy Committee,2006
[118]CARMEN C M. An evaluation system of the setting up of predictive maintenance programmes [J]. Reliability Engineering and System Safety,2006(9):1945-963
[119]WARBURTON D, STRUTT J E, ALLSOP K. Reliability-prediction procedures for mechanical components at design stage [J]. Proc. Inst. Mech. Eng. (Part E),1998 (212):213-224
[120]Barringer & Associate, Inc. Reliability engineering principles [EB/OL].2007-11-20. http://www.barringerl.com/training.htm
[121]KUMAR D, KLEFSJO B, KUNAR U. Reliability analysis of power-transmission cables of electric loaders using a proportional-hazard model [J]. Reliability Engineering and System Safety,1992(37):217-222
[122]BARRINGER P E, WEBER P D. Life-cycle cost [C]. Fifth International Conference on Process-plant Reliability, Gulf Publishing Company, Houston, Texas, October 2-4,1996 (3):1-58
[123]CHRISTER A H, WANG W, SHARP J M. A state space condition monitoring model for furnace erosion prediction and replacement [J]. Eur. J. Oper. Res.,1997(101):1-14
[124]Carnero M C. Selection of diagnostic techniques and instrumentation in a predictive maintenance program [J]. Decision support systems,2005(38):539-555
[125]KENNETH S., GRANT W.. The Maintenance Requirements System:Risk-Based Resource Programming at Work [J]. Naval Engineers Journal,1994,106(3):279-284
[126]BARBERA F, SCHNEIDER H, WATSON E. A condition based maintenance model for two-unit series systems [J]. Eur. J. Oper. Res.,1999(113):315-335
[127]CLIFTON R.H.. Principles of planned maintenance [D]. London:Edward Arnold,1974
[128]EDWARDS D J, HOLT G D, HARRIS F C. Predictive maintenance technigques and their relevance to construction plant [J]. J. Qual. Maint. Eng.1998,4(1):25-37
[129]ANDREW C K, TOSHIHIRO M. The nuclear industry's transition to risk-informed regulation and operation in the United States [J]. Reliability Engineering and System Safety,2007(92):609-618
[130]JESUS C, JOSE M. P, FELIX G C, et al. Applying RCM in larger scale systems:A case study with railway networks [J]. Reliability Engineering and System Safety,2003(82): 257-273
[131]Barringer HP. Practical reliability of refinery and chemical plants [J]. H.Paul Barringer & Associate.1999
[132]戴旭东,赵三星,谢友柏,等.以可靠性为中心的机械设备针对性维修策略研究[J].机械科学与技术,2002,21(01)：89～91
[133]戎翔,左洪福,张海军.视情况维修策略下的民航发送机拆换时间预测研究[J].机械科学与技术,2008,27(5)：584-587
[134]苏春,黄茁.以可靠性为中心的维修成本优化模型及其应用[J].机械科学与技术,2007,26(12)：1556-1559
[135]Scarf P. On the application of mathematical models in maintenance [J]. European Journal of Operational Research.1997,99(3):493-506
[136]Christer A, Wang A. A simple condition monitoring model for a direct monitoring process [J]. European Journal of Operational Research.1995,82:258-269
[137]Wang W. A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance [J]. Int J Prod Res 2000,38(6):1425-1436
[138]Grall A, Castanier B. A condition-based maintenance policy for stochastically deteriorating systems [J]. Reliability Engineering & System Safety.2002,76:167-180
[139]吴祖堂,李岳,李琦,等.复杂机械设备运行状态趋势的灰色预测[J].机械研究与应用,1998(1)：44-45
[140]任若思,王慧文.多元统计数据分析[M].北京：国防工业出版社,1993
[141]成明利.基于状态监测的机械剩余寿命预测系统[J].水利电力机械,2004,4：45-49
[142]刘惟信.机械可靠性设计[M].北京：清华大学出版社,1996.74-80
[143]丁湛,黄双华.基于威布尔分布的可靠性寿命分布模型的建立[J].2007.30(3)：34-35
[144]于晓红.基于新的威布尔分布参数估计法的设备寿命可靠性分析[J].机械强度,2007,29(6)：932-936
[145]L.F.zhang,M.Xie,L.C.Tang.A study of two estimation approaches for parameters of weibull distribution based on WPP [J].Reliability Engineering and System safety, 2007,92:360-368
[146]游达章.最小二乘法在威布尔分布的可靠性评估[J].湖北工业大学学报,2009,24(4)：34-36
[147]A. Saghafi,A.R. Mirhabibi, G.H. Yari.Improved linear regression method for estimating Weibull parameters [J]. Theoretical and Applied Fracture Mechanics,2009,52:180-182
[148]李勇,曹祖庆.火电机组状态监测、预测及故障诊断与状态维修[J].发电设备,1999,(3)：25-28
[2]Gao J J, Yang J F, Jiang Z N, et al. Risk based dynamic intelligent maintenance system for process industry in China. The third world conference on engineering assets management and intelligent maintenance system [C].2008:76-87
[3]Kwon H-m. The effectiveness of process safety management regulation for chemical industry in Korea [J]. Journal of Loss Prevention in the Process Industries,2006,19,13-16
[4]Rausand M. Reliability centered maintenance [J]. Reliability Engineering and System Safety.1998,60:121-132
[5]Moubray J,石磊译.以可靠性为中心的维修[M].北京：机械工业出版社,1995.1-9
[6]Pujadas W, Chen F F. A Reliability Centered Maintenance strategy for a discrete part manufacturing facility [J]. Computers ind. Engng,1996,31:241-244
[7]Richet D, Cotaina N, Gabriel M, et al. Application of Reliability Centered Maintenance in the foundry sector [J]. Control Eng. Practice,1995,3:1029-1034
[8]贾希胜,程中华.以可靠性为中心的维修(RCM)发展动态[J].军械工程学院学报,2002,14(3)：29-32
[9]戴树和.工程风险分析技术[M].北京：化学工业出版社,2007
[10]CEN Workshop 24. Risk-Based Inspection and Maintenance Procedures for European Industry[S].2007
[11]NAVAIR 00-25-403. Guidelines for the NAVAL AVIATION Reliability-Centered Maintenance Process[S].2003
[12]SAE JA1012. A Guide to the Reliability-Centered Maintenance (RCM) Standard[S].2002
[13]MSG-3. Maintenance Program Development Document[S].1993
[14]GJB/Z 1391-2006.故障模式、影响及危害分析指南[S].2006
[15]赵爱国.设备运行阶段风险管理研究[D].天津：天津大学,2006
[16]陆东,牟善军.欧洲设备风险检查技术发展及应用概况[J].安全、健康和环境,2004,4(1)：36-39
[17]Bareib J, Buck P, Matschecko B, et al. RIMAP demonstration project. Risk-based life management of piping system in power plant Heilbronn [J]. International Journal of Pressure Vessels and Piping,2004,81:807-813
[18]ISO 14224:1999, IDT. Petroleum and natural gas industries-Collection and exchange of reliability and maintenance data for equipment [S].1999
[19]ReliaSoft.RCM++5 Training Guide[M].RSP ReliaSoft Publishing,2010
[20]徐习东,朱峻永,吕一农,等.一种改进的RCM方法-精简型RCM(SRCM) [J].华北电力技术,2006,3：43-45.
[21]SKF SRCM流程模型.SKF资产管理服务维修策略评估[EB/OL].http://www.skf.com /files/690347.pdf
[22]Delta consult B.V..ASMT5.0用户手册[EB/OL].http://www.AssetMST.com
[23]徐小力,徐勇,王信义.机器工作状态趋势预测技术的研究[J].工业仪表与自动化装置,1998(3)：9-12
[24]徐小力,梁福平,许宝杰,等.旋转机械状态监测及预测技术的发展与研究[J].建设机械技术与管理,2003,7：20-24
[25]Gupta M M, Rao D H. On the principles of fuzzy neural networks [J]. Fuzzy Sets and Systems,1994,61(1):1-8
[26]Tzafestas S G, Dalianis P J. Fault diagnosis in complex systems using artificial neural networks [J]. Proc. Of the IEEE Conf. on Control Application,1994(2):877-882
[27]王信义,董卫平,朱小燕,等.生产系统中的监控检测技术[M].北京：北京理工大学出版社,1998
[28]李葆文.国际设备管理与维修工程的最新进展综述(十二)[J].中国设备工程,2008,11：66-68
[29]Deshpande V S, Modak J P. Maintenance strategy for tilting table of rolling mill based on reliability consideration [J]. Reliability Engineering and System Safety.2003,80(1):1-18
[30]EPRI. Use of RCM for McGuire nuclear station feed-water system [J].1986, NP-4795
[31]峁定远.概率风险评价技术在以可靠性为中心的维修中的应用研究[D].北京：清华大学,2000
[32]Tong J, Mao D, Xue D. A genetic algorithm solution for a nuclear power plant risk-cost maintenance model [J]. Nuclear Engineering and Design.2004,229(1):81-89
[33]曹钟中,杨昆,傅钟广,等.汽轮机及其辅助设备系统实施RCM的基本策略[J].汽轮机技术,2002,44(2)：106-108
[34]Endrenyi J, Aboresheid S, Allan R N. The present status of maintenance strategies and the impact of maintenance on reliability [J]. IEEE Transaction on power systems.2001, 16(4):638-646
[35]Michel H, Mufeed A. Improving industrial process safety & availability [J].Reliability Engineering,2008,1021-1026
[36]Rodney B. Reliability Program for Plant maintenance [J]. IEEE Industry Applications Magazine,2000,7(5):29-32
[37]王庆锋,杨剑锋,刘文彬,等.过程工业设备维修智能决策系统的开发与应用[J].机械工程学报.2010,46(24)：168-177.
[38]董绍华.管道完整性技术与管理实践[C].中国管道安全与高层管理国际研讨会论文集,北京,2005
[39]董绍华,王联伟,费凡.油气管道完整性管理体系[J].油气储运,2010,29(9)：641-647
[40]董绍华,杨祖佩.全球油气管道完整性技术与管理的最新进展[J].油气储运,2006,26(2)：1-17
[41]李葆文.国际设备管理与维修工程的最新进展综述(三)[J].国外设备工程,2008,27-29Procaccia H, Cordier R, Muller S. Application of Bayesian statistical decision theory for a
[42]maintenance optimization problem [J]. Reliability Engineering and System Safety,1997, 55(7):143-149
[43]Kallen M J, Noortwijk J M. Optimal Maintenance Decision under imperfect inspection [J]. Reliability Engineering and System Safety,2005,90(6):177-185
[44]Chen D Y, Kishor S. Optimization for condition based maintenance with Semi-Markov decision process [J]. Reliability Engineering and System Safety,2005,38(2):25-29
[45]Basim A, Imad A. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making [J]. Int. J. Production Economics,2003,84(12):85-100
[46]Dipark C. An Algorithm for maintenance and replacement policy using fuzzy set theory [J]. Reliability Engineering and System Safety,1995,50(2):444-451
[47]张永敢,蔡瑞英.基于变精度粗糙集的故障诊断应用研究[J].计算机工程与设计,2009,30(3)：657-659
[48]李葆文.国际设备管理与维修工程的最新进展综述(五)[J].国外设备工程,2008,23-25
[49]李葆文.国际设备管理与维修工程的最新进展综述(八)[J].国外设备工程,2008,43-45
[50]李葆文.国际设备管理与维修工程的最新进展综述(十二)[J].国外设备工程,2008,66-68
[51]Kobbacy KAH, Prodlove N L, Harper M A. Towards an intelligent maintenance optimization system [J]. J.Opl.Res.Soc.,1995(46):831-853
[52]Zhang F, Jardine A. A smart maintenance decision system [C]. In:Kobbacy KAH, Vadera S and Proudlove NC (eds). Proceedings of the First European Conference on Intelligent Management Systems in Operations. Operational Research Society:Birminham, 1997:79-85
[53]Kobbacy KAH, Jeon J. The development of a hybrid intelligent maintenance optimization system (HIMOS) [J]. Journal of the Operational Research Society,2001,52(7):762-778
[54]Kobbacy KAH. On the evolution of an intelligent maintenance optimization system [J]. Journal of the Operational Research Society,2004,55(2):139-146
[55]Daniel J, Fonseca. A knowledge-based system for preventive maintenance [J]. Expert System,2000,17(5):241-247
[56]吴凡,王国庆,傅国强.基于人工智能的机电设备智能保障系统研究[J].计算机测量与控制,2006,14(8)：1046-1048
[57]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究[J].设备管理与维修,2005,(8)：11-13
[58]姜运臣.智能维修决策支持系统的研究[J].锅炉制造,2006,(1)：75-76
[59]藏铁钢,黄金国,张淑猛,等.基于网络的智能维修信息系统的研究与开发[J].机械工程师,2003(5)：6-8
[60]孙建宇.输气管道完整性管理简介[J].石油库与加油站,2009,18(3)：14-17
[61]税碧垣,艾幕阳,冯庆善.油气站场完整性管理技术思路[J].油气储运,2009,28(7)：11-14
[62]牟善军,姜春明.过程安全与设备完整性管理技术[J].安全、健康与环境,2006,6(8)：2-5
[63]帅健.美国油气管道事故及其启示[J].油气储运,2010,29(11)：806-809
[64]张鹏,姜云,段永红,等.新建油气管道完整性管理系统模型设计与开发[J].天然气勘探与开发,2010,33(4)：77-82
[65]董绍华.全球油气管道完整性技术与管理的最新进展与中国管道的对策[J].油气储运,2007(2)：1-17
[66]严大凡,翁永基,董绍华.油气管道风险评价与完整性管理[C].北京：化学工业出版社,2005：9-16
[67]张大海,郭kun,丁继峰.长输天然气管道完整性管理与管道腐蚀检测技术[J].全面腐蚀控制,2010,24(11)：13-16
[68]韩小明,周利剑,冯庆善,等.管道数据的存储与管理[J].地理空间信息,2010,8(6)：86-88
[69]殷苏东,陈旭华.基于状态的维修研究现状与发展趋势[J].科学技术与工程,2008,15(3)：15-18
[70]周尚文.设备维修管理的智能化[J].钢铁技术,2006,14(2)：35-38
[71]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究[J].设备管理与维修,2005,37(1)：11-13
[72]朱清香.基于现代维修理念的决策框架[J].设备管理与维修,2004,25(1)：7-9
[73]陶基斌,郭应征,周太全.基于前馈式神经网络的化工设备维修决策[J].南京化工大学学报,2000,22(5)：11-14
[74]谢庆华,张琦,卢涌.航空发动机单部件视情况维修优化决策[J].解放军理工大学学报(自然科学版),2005,6(6)：575-578
[75]陈玉科.更新型预防维修周期的模糊决策[J].运筹与管理,1999,18(6)：71-75
[76]郭丽杰.基于风险的石化动设备智能维修决策研究[D].北京：北京化工大学,2009
[77]杨剑锋.炼化企业智能维修与安全保障信息系统及工程应用研究[D].北京：北京化工大学,2009
[78]温亮,王丹.基于RCM需求的CMMS系统设计[J].兵工自动化,2006,3：41-42
[79]童晟.构建RCM与CMMS的集成系统[J].现代制造工程,2007,8：41-43
[80]Gao J J, Yang J F, Jiang Z N, et al. Risk based dynamic intelligent maintenance system for process industry in China [C]. The third world conference on engineering assets management and intelligent maintenance system.2008:76-87
[81]高金吉.机泵群实时监测网络和故障诊断专家系统[J].中国工程科学,2001,3(9)：41-47
[82]高金吉.石化设备以可靠性为中心的智能维修系统[J].中国设备工程,2008,(1)：2-4
[83]周洪波.数据交换标准是物联网产业发展的关键[J].信息技术与标准化,2010,(8)：26-29
[84]江泽民.新时期我国信息技术产业的发展[J].上海交通大学学报,2008,42(10)：1589-1607
[85]陈积明,林瑞仲,孙优贤.无线传感器网络的信息处理研究[J].仪器仪表学报,2006,9：129-133
[86]Kwon H-m. The effectiveness of process safety management (PSM) regulation for chemical industry in Korea [J]. Journal of Loss Prevention in the Process Industries,2006, 19:13-16
[87]Deshpande V S, Modak J P. Application of RCM to a medium scale industry [J]. Reliability Engineering and System Safety 2002;77(1):31-43
[88]Rausand M. Reliability centered maintenance [J]. Reliability Engineering and System Safety.1998,60:121-132
[89]Moubary J,石磊译.以可靠性为中心的维修[M].北京：机械工业出版社,1995：1-9
[90]贾希胜.以可靠性为中心的维修决策模型[M].北京：国防工业出版社,2007：1-3
[91]Pujadas W, Chen F F. A Reliability Centered Maintenance strategy for a discrete part manufacturing facility [J]. Computers ind. Engng,1996,31:241-244
[92]Richet D, Cotaina N, Gabriel M, et al. Application of Reliability Centered Maintenance in the foundry sector [J]. Control Eng. Practice,1995,3:1029-1034
[93]Cheng Z, Jia X, Wu S, et al. A framework for intelligent Reliability Centered Maintenance Analysis [J]. Reliability Engineering and System Safety,2008,93:806-814
[94]苏春,黄茁.以可靠性为中心的维修成本优化模型及其应用[J].机械科学与技术,2007,26(12)：1556-1559
[95]左洪福,蔡景,王华伟.维修决策理论与方法[M].北京：航空工业出版社,2008：5-12
[96]李葆文.国外设备管理模式及发展趋势(四)[J].设备管理与维修,2000.10：40-41
[97]温亮,贾希胜.RCM模型决策系统设计[J].计算机工程设计,2006,27(23)：4537-4539
[98]John M. Reliability-centered maintenance[M]. UK:Oxford,1997
[99]贾希胜,贾云献,温亮.以可靠性为中心的维修及其模型支持[J].军械工程学院学报,2004,16(1)：15-18
[100]程中华,贾云献.基于C/S模式的RCM分析决策系统的设计与实现[J].计算机工程,2004,30(13)：174-177
[101]ETI M C, OGAJI S O T, PROBERT S D. Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture [J]. Applied Energy,2006, 83:1235-1248
[102]MASTERS M. Reliability, key to competitive advantage? [J]. PaperAge,1999,9:15-16
[103]ETI M C, OGAJI S O T, PROBERT S D. Integrating reliability, availability, maintainability and supportability with risk analysis for improved operation of the Afam thermal power-station [J]. Applied Energy,2007,84:202-221
[104]姜春明,李奇.过程安全管理与技术的发展与应用[J].中国石油和化工标准与质量,2007：5(45-49)
[105]Gao Jinji, Yang Jianfeng. Research on disaster formation in complex engineering system and self-recovery precaution [J]. China Safety Science Journal,2006,16(9):15-22
[106]Ray S., FIEAust, CPEng. Vibration analysis of pumps-basic [J]. Predicitive Maintenance of Pumps Using Condition Monitoring,2004:83-100
[107]DESHPANDE V S, MODAK J P. Application of RCM to a medium scale industry [J]. Reliability Engineering and System Safety,2002(77):31-43
[108]GAO Jinji. New progress in plant diagnosis engineering-distributed monitoring system and remote diagnosis system [C]. The proceedings of the International Conference on Plant Engineering Guangzhou'97,1997:53-59
[109]高金吉.设备诊断工程与集散监测系统[J].石油化工设备技术,1996,17(3)：11-14
[110]GAO Jinji. A real-time monitoring network and fault diagnosis expert system for compressors and pumps [J]. Engineering Science,2001,3(9):41-47
[111]MARTORELL S, VILLANUEVA J F, CARLOS S et al. RAMS informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms [J]. Reliability Engineering and System Safety, 2005(87):65-75
[112]BLANCHARD B S, VERNA D F, PETERSON E L. Maintainability:A key to effective serviceability and maintenance management [M]. New York:John While and sons,1995
[113]HERDER P M, VAN LUIJK J A, BRUIJNOOGE J. Industrial application of RAM modeling [J]. Reliability Engineering and System Safety,2008(93):501-508
[114]Paul Barringer H, Funkhouser J. Improving refinery reliability, performance and utilization [J]. Hydrocarbon processing,2007(10):73-76
[115]WARBURTON D, STRUTT J E, ALLSOP K. Reliability-prediction procedures for mechanical components at design stage [J]. Proc. Inst. Mech. Eng.,1998(24):212-213
[116]MARTORELL S, SANCHEZ A, MUNOZ A, et al. The use of maintenance indicators to evaluate the effects of maintenance programs on NPP performance and safety [J]. Reliability Engineering and System Safety,1999(65):85-94
[117]Technical Committee CENELEC TC 9X. EN50126-1 Railway applications-the specification and demonstration of reliability, availability, maintainability and safety (RAMS) [S]. London:the authority of the Standards Policy and Strategy Committee,2006
[118]CARMEN C M. An evaluation system of the setting up of predictive maintenance programmes [J]. Reliability Engineering and System Safety,2006(9):1945-963
[119]WARBURTON D, STRUTT J E, ALLSOP K. Reliability-prediction procedures for mechanical components at design stage [J]. Proc. Inst. Mech. Eng. (Part E),1998 (212):213-224
[120]Barringer & Associate, Inc. Reliability engineering principles [EB/OL].2007-11-20. http://www.barringerl.com/training.htm
[121]KUMAR D, KLEFSJO B, KUNAR U. Reliability analysis of power-transmission cables of electric loaders using a proportional-hazard model [J]. Reliability Engineering and System Safety,1992(37):217-222
[122]BARRINGER P E, WEBER P D. Life-cycle cost [C]. Fifth International Conference on Process-plant Reliability, Gulf Publishing Company, Houston, Texas, October 2-4,1996 (3):1-58
[123]CHRISTER A H, WANG W, SHARP J M. A state space condition monitoring model for furnace erosion prediction and replacement [J]. Eur. J. Oper. Res.,1997(101):1-14
[124]Carnero M C. Selection of diagnostic techniques and instrumentation in a predictive maintenance program [J]. Decision support systems,2005(38):539-555
[125]KENNETH S., GRANT W.. The Maintenance Requirements System:Risk-Based Resource Programming at Work [J]. Naval Engineers Journal,1994,106(3):279-284
[126]BARBERA F, SCHNEIDER H, WATSON E. A condition based maintenance model for two-unit series systems [J]. Eur. J. Oper. Res.,1999(113):315-335
[127]CLIFTON R.H.. Principles of planned maintenance [D]. London:Edward Arnold,1974
[128]EDWARDS D J, HOLT G D, HARRIS F C. Predictive maintenance technigques and their relevance to construction plant [J]. J. Qual. Maint. Eng.1998,4(1):25-37
[129]ANDREW C K, TOSHIHIRO M. The nuclear industry's transition to risk-informed regulation and operation in the United States [J]. Reliability Engineering and System Safety,2007(92):609-618
[130]JESUS C, JOSE M. P, FELIX G C, et al. Applying RCM in larger scale systems:A case study with railway networks [J]. Reliability Engineering and System Safety,2003(82): 257-273
[131]Barringer HP. Practical reliability of refinery and chemical plants [J]. H.Paul Barringer & Associate.1999
[132]戴旭东,赵三星,谢友柏,等.以可靠性为中心的机械设备针对性维修策略研究[J].机械科学与技术,2002,21(01)：89～91
[133]戎翔,左洪福,张海军.视情况维修策略下的民航发送机拆换时间预测研究[J].机械科学与技术,2008,27(5)：584-587
[134]苏春,黄茁.以可靠性为中心的维修成本优化模型及其应用[J].机械科学与技术,2007,26(12)：1556-1559
[135]Scarf P. On the application of mathematical models in maintenance [J]. European Journal of Operational Research.1997,99(3):493-506
[136]Christer A, Wang A. A simple condition monitoring model for a direct monitoring process [J]. European Journal of Operational Research.1995,82:258-269
[137]Wang W. A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance [J]. Int J Prod Res 2000,38(6):1425-1436
[138]Grall A, Castanier B. A condition-based maintenance policy for stochastically deteriorating systems [J]. Reliability Engineering & System Safety.2002,76:167-180
[139]吴祖堂,李岳,李琦,等.复杂机械设备运行状态趋势的灰色预测[J].机械研究与应用,1998(1)：44-45
[140]任若思,王慧文.多元统计数据分析[M].北京：国防工业出版社,1993
[141]成明利.基于状态监测的机械剩余寿命预测系统[J].水利电力机械,2004,4：45-49
[142]刘惟信.机械可靠性设计[M].北京：清华大学出版社,1996.74-80
[143]丁湛,黄双华.基于威布尔分布的可靠性寿命分布模型的建立[J].2007.30(3)：34-35
[144]于晓红.基于新的威布尔分布参数估计法的设备寿命可靠性分析[J].机械强度,2007,29(6)：932-936
[145]L.F.zhang,M.Xie,L.C.Tang.A study of two estimation approaches for parameters of weibull distribution based on WPP [J].Reliability Engineering and System safety, 2007,92:360-368
[146]游达章.最小二乘法在威布尔分布的可靠性评估[J].湖北工业大学学报,2009,24(4)：34-36
[147]A. Saghafi,A.R. Mirhabibi, G.H. Yari.Improved linear regression method for estimating Weibull parameters [J]. Theoretical and Applied Fracture Mechanics,2009,52:180-182
[148]李勇,曹祖庆.火电机组状态监测、预测及故障诊断与状态维修[J].发电设备,1999,(3)：25-28