石化旋转机械故障诊断及维修管理优化研究
|
摘要
旋转机械是石化企业生产中的核心设备，任何设备不可避免会出现故障，维修是保障其正常运行的重要手段。但石化旋转机械故障原因具有复杂性、多样性和不确性的特点，而且影响维修决策的因素很多，所以其维修决策管理过程非常复杂。为此，本文对石化旋转机械的故障诊断和维修管理优化进行了研究。
建立了基于贝叶斯网络的故障诊断模型。为防止或避免同类故障的再次发生，首先将故障原因进行分类分析，提出了相应的维修措施；然后将故障模式与影响分析与贝叶斯网络方法相结合，从而可定量地分析出影响设备运行的薄弱环节。以一台石化企业生产中的离心泵为应用实例，分析结果表明，该方法可为石化旋转机械的故障诊断提供一定的依据。
开发了计算机辅助设备维修管理系统。该系统使用VB语言编写，采用模块化设计框架。它能够对设备的基本信息进行管理，并且建立了故障模式数据库，同时实现了贝叶斯网络的自动化分析。该系统可提高设备信息管理和故障诊断的效率。
以单位时间内设备寿命管理费用最低为目标，建立了适用于各种寿命分布的基于几何过程维修优化模型，并引入了蒙特卡罗方法，运用Matlab编制了相应的仿真程序，对机械设备的最佳寿命周期进行预测。以服从二参数威布尔分布的离心泵作为分析实例。结果表明，所建立的模型具有一定的合理性，能够对机械设备的维修决策提供一定的指导。
Rotary machinery is the core equipment in the petrochemical production. The failurefor any equipment will occur inevitably and maintenance is an essential means forensuring the normal operation of rotary machinery. But the fault reason of petrochemicalrotating machinery has the complexity, diversity and uncertainty characteristics, andmaintenance decision-making is influenced by many factors, so the process ofmaintenance decision-making management is very complex. For this reason, themaking-decision management on the petrochemical rotary machine was investigated inthis paper.
The fault diagnosis model, which is based on Bayesian network, was established. Toprevent or avoid the recurrence of the similar fault again, first of all, the fault reasons areclassified and analyzed and the corresponding maintenance measures was put forward;Then the failure modes and effects analysis was combined with Bayesian networkmethods, so the weak link can be analyzed for the influence of the equipment operation. Apetrochemical enterprise of centrifugal pump was taken as an application example. Theresults show that the method can provide certain basis for fault diagnosis of thepetrochemical rotary machine fault diagnosis.
The computer of equipment maintenance management system was developed. VBlanguage was used and modular design framework was designed in this system. It canmanagement the basic information of the equipment can be managed through it, and afault model database was built. Meanwhile the automation analyze for the Bayesiannetworks analyze was realized. The system can improve the efficiency of equipmentinformation management and the fault diagnosis.
Taking the least equipment management cost within unit time as the target, amaintenance optimization model based on monotonous geometric process wasestablished，which can be applied to many types of life distribution. And Monte Carlosimulation method was introduced into this mode. The simulation program by Matlab wasobtained and can predict the optimum life cycle of the mechanical equipment of. The centrifugal pump for Weibull distribution of two parameters was analyzed as a study case.Results show that the model is reasonable, which can supply a scientific guideline formaintenance making-decision.
建立了基于贝叶斯网络的故障诊断模型。为防止或避免同类故障的再次发生，首先将故障原因进行分类分析，提出了相应的维修措施；然后将故障模式与影响分析与贝叶斯网络方法相结合，从而可定量地分析出影响设备运行的薄弱环节。以一台石化企业生产中的离心泵为应用实例，分析结果表明，该方法可为石化旋转机械的故障诊断提供一定的依据。
开发了计算机辅助设备维修管理系统。该系统使用VB语言编写，采用模块化设计框架。它能够对设备的基本信息进行管理，并且建立了故障模式数据库，同时实现了贝叶斯网络的自动化分析。该系统可提高设备信息管理和故障诊断的效率。
以单位时间内设备寿命管理费用最低为目标，建立了适用于各种寿命分布的基于几何过程维修优化模型，并引入了蒙特卡罗方法，运用Matlab编制了相应的仿真程序，对机械设备的最佳寿命周期进行预测。以服从二参数威布尔分布的离心泵作为分析实例。结果表明，所建立的模型具有一定的合理性，能够对机械设备的维修决策提供一定的指导。
Rotary machinery is the core equipment in the petrochemical production. The failurefor any equipment will occur inevitably and maintenance is an essential means forensuring the normal operation of rotary machinery. But the fault reason of petrochemicalrotating machinery has the complexity, diversity and uncertainty characteristics, andmaintenance decision-making is influenced by many factors, so the process ofmaintenance decision-making management is very complex. For this reason, themaking-decision management on the petrochemical rotary machine was investigated inthis paper.
The fault diagnosis model, which is based on Bayesian network, was established. Toprevent or avoid the recurrence of the similar fault again, first of all, the fault reasons areclassified and analyzed and the corresponding maintenance measures was put forward;Then the failure modes and effects analysis was combined with Bayesian networkmethods, so the weak link can be analyzed for the influence of the equipment operation. Apetrochemical enterprise of centrifugal pump was taken as an application example. Theresults show that the method can provide certain basis for fault diagnosis of thepetrochemical rotary machine fault diagnosis.
The computer of equipment maintenance management system was developed. VBlanguage was used and modular design framework was designed in this system. It canmanagement the basic information of the equipment can be managed through it, and afault model database was built. Meanwhile the automation analyze for the Bayesiannetworks analyze was realized. The system can improve the efficiency of equipmentinformation management and the fault diagnosis.
Taking the least equipment management cost within unit time as the target, amaintenance optimization model based on monotonous geometric process wasestablished，which can be applied to many types of life distribution. And Monte Carlosimulation method was introduced into this mode. The simulation program by Matlab wasobtained and can predict the optimum life cycle of the mechanical equipment of. The centrifugal pump for Weibull distribution of two parameters was analyzed as a study case.Results show that the model is reasonable, which can supply a scientific guideline formaintenance making-decision.
引文
[1]李葆文.现代设备资产管理[M].北京:机械工业出版社. 2006: 12-14.
[2]张晓峰,汪彤,谭家磊,吕良海.石油化工企业本质安全评价方法研究[C].中国职业安全健康协会2008年学术年会. 2008: 33-38.
[3]沈庆根.化工机器故障诊断技术[M].杭州:浙江大学出版社, 1994: 18-21.
[4]沈庆根,郑水英.设备故障诊断[M].北京:化学工业出版社, 2007: 10-11.
[5]刘有才,刘增良.模糊专家系统原理与设计[M].北京:北京航空航天大学出版社, 1995:11-12.
[6]虞和济,陈长征,张省,周建男.基于神经网络的智能诊断[M].北京:冶金工业出版杜,2000: 20-23.
[7]王广彦,马志军,胡起伟.基于贝叶斯网络的故障树分析[J].系统工程理论与实践, 2004,32(6): 78-83.
[8]李俭川,胡茑庆,秦国军,温熙森.基于故障树的贝叶斯网络建造方法与故障诊断应用[J].计算机工程与应用, 2003, 39(24): 225-228.
[9] Bobbin A, Portinale L, Minichino M, Ciancamerla E. Improving the analysis of dependablesystems by mapping fault trees into Bayesian networks[J]．Reliability Engineering and SystemSafety, 2001, 71(19): 249-260.
[10]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: 12-13.
[11]李葆文,徐保强.规范化的设备维修管理--SOON[M].北京:机械工业出版社, 2006: 3-13.
[12]蒋太立.基于RCM理论的维修决策研究[D].武汉理工大学, 2006: 2-6.
[13]何国伟.系统可靠性设备分析[M],国防工业出版社, 1997: 58-60.
[14] Moubray J. Reliability-Centered Maintenance. Second edition[M]. New York: Industrial Press Inc,1997: 18-24.
[15]赵艳萍,姚冠新,陈骏.设备管理与维修[M].北京:化学工业出版社, 2007: 19-23.
[16] Kamran S, Moghaddam J, John S. Sesitivity Analysis and Comparison of Algorithms inPreventive Maintenance and Replacement Scheduling Optimization Models[J]. Computers &Industrial Engineering, 2011, 61(16): 64-75.
[17]戴旭东,赵三星,谢友柏,杨兆建,马砸铃.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, 33(21): 89-91.
[18]刘承元. TPM活动挑战企业生产效率极限的武器[M].深圳:深圳海天出版社, 2002: 23-30.
[19] Eti M C, Ogaji S O T, Probert S D. Implementing Total Productive Maintenance in NigerianManufacturing Industries[J]. Applied Energy, 2004, 79(4): 385-401.
[20]赵晨光.将军集团制丝车间深入推行TPM的应用研究[D].济南:山东大学, 2005: 2-3.
[21]曹志娟,李磊山.煤矿设备全员生产维修管理研究[J].产业与科技论坛, 2009, 8(8): 218-220.
[22]凌峰,曹卫.全员生产维修在船舶航政管理中的运用[J].水运管理, 2010, 32(5): 25-26.
[23] Tian Zhigang, Liao Haitao. Condition based maintenance optimization for multi-componentsystems using proportional hazards model[J]. Reliability Engineering & System Safety, 2011,96(5): 581-589.
[24]金祖荣.石化行业电气设备状态监测与故障诊断[J].甘肃科技, 2010,26(4): 66-68.
[25]夏良华,贾希胜,徐英.设备维修策略的合理选择与决策流程[J].火炮发射与控制学报,2006, 23(4): 63-67.
[26]张耀辉,郭金茂,徐宗昌,田松柏.基于故障风险的状态维修检测间隔期的确定[J].中国机械工程, 2008, 19(5): 555-558.
[27]袁志坚,孙才新,李剑,杜林,廖瑞金.基于模糊多属性群决策的变压器状态维修策略研究[J].电力系统自动化, 2004, 28(11): 66-70.
[28]叶小松.回转大轴承的状态维修及其最佳检测周期仿真研究[D].武汉:武汉理工大学, 2005:8-9.
[29]程志君,高大化,黄卓,郭波.不完全维修条件下的视情维修优化模型[J].系统工程与电子技术, 2006, 28(7): 1106-1108.
[30]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策[J].航空动力学报,2006, 21(4): 716-721.
[31]王长高.以可靠性为中心的维修思想研究[J].北京航空航天大学学报, 1991, 13(1): 36-39.
[32] Cheng Zhonghua, Jia Xisheng, Gao Ping, Wu Su, Wang Jianzhao. A Franmework for IntelligentReliability Centered Maintenance Analysis[J]. Reliability Engineering & System System Safety,2008, 93(6): 806-814.
[33]马芙蓉,张治军.关于状态监测与RCM的发展思考[J].科技信息, 2009, 33(8): 304-306.
[34]刘涛,蔡增杰. RCM技术研究及定量分析方法[J].科技信息, 2009, 36(12): 105-107
[35] Li Dacheng, Gao Jinji. Study and application of Reliability-centered Maintenance consideringRadical Maintenance [J]. Journal of Loss Prevention in the Process Industries,2010, 17(9):622-629.
[36]莫布雷著,石磊,谷宁昌译.以可靠性为中心的维修[M].北京:机械工业出版社, 2000:33-38.
[37]张翠凤.设备管理与维修模式探讨[J].设备管理与维修, 2000, 18(2): 7-9.
[38] Crocker J, Kumar U D. Age-related Maintenance Versus Reliability Centred Maintenance: A CaseStudy on Aero-engines[J]. Reliability Engineering and System Safety 2000, 67(22): 113–118.
[39] Deshpande V S, Modak J P. Application of RCM for Safety Considerations in A Steel Plant[J].Reliability Engineering and System Safety, 2002, 78(8): 325–334.
[40] Deshpande V S, Modak J P. Application of RCM to A Medium Scale Industry [J]. ReliabilityEngineering and System Safety, 2002, 77(8): 31-43.
[41]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: 9-11.
[42]孙有朝,樊蔚勋.以可靠性为中心的维修(RCM)维修科学的发展趋势[J].机械工程师, 1997,7(4): 55-56.
[43]姚吴.浅谈RCM应用在核电领域的优势及在秦山二期的前景[J]. 2009核能行业可靠性维修(RCM)研讨会(专刊), 2009,2: 133-138.
[44]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: 8-10.
[45]杨景辉,康建设. RCM维修管理模式及其应用分析[J].科学技术与工程, 2007, 7(15):3881-3885.
[46] Adrews J D, Morgan J M. Application of The Digraph Method of Fault Tree Construction toProcess Plant[J]. Reliability Engineering and System Safety, 2003, 14(7): 85-106.
[47] Pearl J. Probabilistic Reasoning in Intelligent Systems: Netowrks of Plausible Inference[M]. SanMateo, CA: Morgan Kaufmann, 1988: 48-53.
[48] Euqene, Charniak. Bayesian networks without tears[J]. AI Magazine, 1991,12(4): 50-63.
[49] D`Ambrosio, Bruce. Inference in Bayesian networks[J]. AI Magazine, 1999,20(2): 21-36.
[50]徐斌刚.转子故障贝叶斯诊断网络的研究[J].机械工程学报, 2004, 40(1): 66-72.
[51] Lam Y. A Note on The Optimal Replacement Problem[J]. Advances in Applied Probability, 1988,20(6): 479-482.
[52] Lam Y. A Geometric Process Maintenance Model[J]. Southeast Asian Bulletin of Mathematics,2003, 27(9): 295-305.
[53] Lam Y. A Geometric Process Maintenance Model with Preventive Repair[J]. European Journal ofOperational Research, 2007, 182(13): 806-819.
[54] Zhang Y L, Wang G J. A Deteriorating Cold Standby Repairable System with Priority in Use[J].European Journal of Operational Research, 2007, 183(13): 278-295.
[55] Zhang Y L, Wang G J, A Bivariate Optimal Replacement Policy for A Multistate RepairableSystem[J]. Reliability Engineering and System Safety, 2007, 92(12): 535-542.
[56] Zhang Y L, Wang G J. A Geometric Process Repair Model for A Series Repairable System with KDissimilar Components[J]. Applied Mathematical Modeling, 2007, 31(6): 1997-2007.
[57] Wang G J, Zhang Y L. An Optimal Replacement Policy for A Two-component Series SystemAssuming Geometric Process Repair[J].Computers and Mathematics with Applications, 2007,54(3): 1992-2002.
[58]贾积身.冷备可修系统最优更换策略下的数学模型[J].系统工程学报, 2003, 18(2): 177-182.
[59]张宝锋,夏虹.核电站设备的维修优化[J].核动力工程, 2007, 28(3): 117-120.
[60] Emanouil atanassov, Ivan dimov. What Monte Carlo Models Can Do and Cannot Do Efficiently[J].Applied Mathematical Modelling, 2008, 32(8): 1477-1500.
[61]张宁.设备预防维修的故障率与最小费用周期[J].系统工程理论与实践, 1998, 45(10): 33-37.
[62]张春香.可维修系统的可靠性研究[D].秦皇岛:燕山大学, 2001: 37-44.
[2]张晓峰,汪彤,谭家磊,吕良海.石油化工企业本质安全评价方法研究[C].中国职业安全健康协会2008年学术年会. 2008: 33-38.
[3]沈庆根.化工机器故障诊断技术[M].杭州:浙江大学出版社, 1994: 18-21.
[4]沈庆根,郑水英.设备故障诊断[M].北京:化学工业出版社, 2007: 10-11.
[5]刘有才,刘增良.模糊专家系统原理与设计[M].北京:北京航空航天大学出版社, 1995:11-12.
[6]虞和济,陈长征,张省,周建男.基于神经网络的智能诊断[M].北京:冶金工业出版杜,2000: 20-23.
[7]王广彦,马志军,胡起伟.基于贝叶斯网络的故障树分析[J].系统工程理论与实践, 2004,32(6): 78-83.
[8]李俭川,胡茑庆,秦国军,温熙森.基于故障树的贝叶斯网络建造方法与故障诊断应用[J].计算机工程与应用, 2003, 39(24): 225-228.
[9] Bobbin A, Portinale L, Minichino M, Ciancamerla E. Improving the analysis of dependablesystems by mapping fault trees into Bayesian networks[J]．Reliability Engineering and SystemSafety, 2001, 71(19): 249-260.
[10]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: 12-13.
[11]李葆文,徐保强.规范化的设备维修管理--SOON[M].北京:机械工业出版社, 2006: 3-13.
[12]蒋太立.基于RCM理论的维修决策研究[D].武汉理工大学, 2006: 2-6.
[13]何国伟.系统可靠性设备分析[M],国防工业出版社, 1997: 58-60.
[14] Moubray J. Reliability-Centered Maintenance. Second edition[M]. New York: Industrial Press Inc,1997: 18-24.
[15]赵艳萍,姚冠新,陈骏.设备管理与维修[M].北京:化学工业出版社, 2007: 19-23.
[16] Kamran S, Moghaddam J, John S. Sesitivity Analysis and Comparison of Algorithms inPreventive Maintenance and Replacement Scheduling Optimization Models[J]. Computers &Industrial Engineering, 2011, 61(16): 64-75.
[17]戴旭东,赵三星,谢友柏,杨兆建,马砸铃.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, 33(21): 89-91.
[18]刘承元. TPM活动挑战企业生产效率极限的武器[M].深圳:深圳海天出版社, 2002: 23-30.
[19] Eti M C, Ogaji S O T, Probert S D. Implementing Total Productive Maintenance in NigerianManufacturing Industries[J]. Applied Energy, 2004, 79(4): 385-401.
[20]赵晨光.将军集团制丝车间深入推行TPM的应用研究[D].济南:山东大学, 2005: 2-3.
[21]曹志娟,李磊山.煤矿设备全员生产维修管理研究[J].产业与科技论坛, 2009, 8(8): 218-220.
[22]凌峰,曹卫.全员生产维修在船舶航政管理中的运用[J].水运管理, 2010, 32(5): 25-26.
[23] Tian Zhigang, Liao Haitao. Condition based maintenance optimization for multi-componentsystems using proportional hazards model[J]. Reliability Engineering & System Safety, 2011,96(5): 581-589.
[24]金祖荣.石化行业电气设备状态监测与故障诊断[J].甘肃科技, 2010,26(4): 66-68.
[25]夏良华,贾希胜,徐英.设备维修策略的合理选择与决策流程[J].火炮发射与控制学报,2006, 23(4): 63-67.
[26]张耀辉,郭金茂,徐宗昌,田松柏.基于故障风险的状态维修检测间隔期的确定[J].中国机械工程, 2008, 19(5): 555-558.
[27]袁志坚,孙才新,李剑,杜林,廖瑞金.基于模糊多属性群决策的变压器状态维修策略研究[J].电力系统自动化, 2004, 28(11): 66-70.
[28]叶小松.回转大轴承的状态维修及其最佳检测周期仿真研究[D].武汉:武汉理工大学, 2005:8-9.
[29]程志君,高大化,黄卓,郭波.不完全维修条件下的视情维修优化模型[J].系统工程与电子技术, 2006, 28(7): 1106-1108.
[30]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策[J].航空动力学报,2006, 21(4): 716-721.
[31]王长高.以可靠性为中心的维修思想研究[J].北京航空航天大学学报, 1991, 13(1): 36-39.
[32] Cheng Zhonghua, Jia Xisheng, Gao Ping, Wu Su, Wang Jianzhao. A Franmework for IntelligentReliability Centered Maintenance Analysis[J]. Reliability Engineering & System System Safety,2008, 93(6): 806-814.
[33]马芙蓉,张治军.关于状态监测与RCM的发展思考[J].科技信息, 2009, 33(8): 304-306.
[34]刘涛,蔡增杰. RCM技术研究及定量分析方法[J].科技信息, 2009, 36(12): 105-107
[35] Li Dacheng, Gao Jinji. Study and application of Reliability-centered Maintenance consideringRadical Maintenance [J]. Journal of Loss Prevention in the Process Industries,2010, 17(9):622-629.
[36]莫布雷著,石磊,谷宁昌译.以可靠性为中心的维修[M].北京:机械工业出版社, 2000:33-38.
[37]张翠凤.设备管理与维修模式探讨[J].设备管理与维修, 2000, 18(2): 7-9.
[38] Crocker J, Kumar U D. Age-related Maintenance Versus Reliability Centred Maintenance: A CaseStudy on Aero-engines[J]. Reliability Engineering and System Safety 2000, 67(22): 113–118.
[39] Deshpande V S, Modak J P. Application of RCM for Safety Considerations in A Steel Plant[J].Reliability Engineering and System Safety, 2002, 78(8): 325–334.
[40] Deshpande V S, Modak J P. Application of RCM to A Medium Scale Industry [J]. ReliabilityEngineering and System Safety, 2002, 77(8): 31-43.
[41]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: 9-11.
[42]孙有朝,樊蔚勋.以可靠性为中心的维修(RCM)维修科学的发展趋势[J].机械工程师, 1997,7(4): 55-56.
[43]姚吴.浅谈RCM应用在核电领域的优势及在秦山二期的前景[J]. 2009核能行业可靠性维修(RCM)研讨会(专刊), 2009,2: 133-138.
[44]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: 8-10.
[45]杨景辉,康建设. RCM维修管理模式及其应用分析[J].科学技术与工程, 2007, 7(15):3881-3885.
[46] Adrews J D, Morgan J M. Application of The Digraph Method of Fault Tree Construction toProcess Plant[J]. Reliability Engineering and System Safety, 2003, 14(7): 85-106.
[47] Pearl J. Probabilistic Reasoning in Intelligent Systems: Netowrks of Plausible Inference[M]. SanMateo, CA: Morgan Kaufmann, 1988: 48-53.
[48] Euqene, Charniak. Bayesian networks without tears[J]. AI Magazine, 1991,12(4): 50-63.
[49] D`Ambrosio, Bruce. Inference in Bayesian networks[J]. AI Magazine, 1999,20(2): 21-36.
[50]徐斌刚.转子故障贝叶斯诊断网络的研究[J].机械工程学报, 2004, 40(1): 66-72.
[51] Lam Y. A Note on The Optimal Replacement Problem[J]. Advances in Applied Probability, 1988,20(6): 479-482.
[52] Lam Y. A Geometric Process Maintenance Model[J]. Southeast Asian Bulletin of Mathematics,2003, 27(9): 295-305.
[53] Lam Y. A Geometric Process Maintenance Model with Preventive Repair[J]. European Journal ofOperational Research, 2007, 182(13): 806-819.
[54] Zhang Y L, Wang G J. A Deteriorating Cold Standby Repairable System with Priority in Use[J].European Journal of Operational Research, 2007, 183(13): 278-295.
[55] Zhang Y L, Wang G J, A Bivariate Optimal Replacement Policy for A Multistate RepairableSystem[J]. Reliability Engineering and System Safety, 2007, 92(12): 535-542.
[56] Zhang Y L, Wang G J. A Geometric Process Repair Model for A Series Repairable System with KDissimilar Components[J]. Applied Mathematical Modeling, 2007, 31(6): 1997-2007.
[57] Wang G J, Zhang Y L. An Optimal Replacement Policy for A Two-component Series SystemAssuming Geometric Process Repair[J].Computers and Mathematics with Applications, 2007,54(3): 1992-2002.
[58]贾积身.冷备可修系统最优更换策略下的数学模型[J].系统工程学报, 2003, 18(2): 177-182.
[59]张宝锋,夏虹.核电站设备的维修优化[J].核动力工程, 2007, 28(3): 117-120.
[60] Emanouil atanassov, Ivan dimov. What Monte Carlo Models Can Do and Cannot Do Efficiently[J].Applied Mathematical Modelling, 2008, 32(8): 1477-1500.
[61]张宁.设备预防维修的故障率与最小费用周期[J].系统工程理论与实践, 1998, 45(10): 33-37.
[62]张春香.可维修系统的可靠性研究[D].秦皇岛:燕山大学, 2001: 37-44.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |