基于健康评估和劣化趋势预测的水电机组故障诊断系统研究
|
摘要
水电机组运行状态的实时诊断直接关系到水电站的安全稳定运行、电力质量和电力生产成本等重要的经济效益指标,其社会效益巨大。随着电站规模和监测辅助系统的不断扩大,机组的控制和监测数据信息量越来越大,运行操作人员对机组运行状态的实时有效监控、对设备故障做出迅速而准确地判断变得越来越困难,因此,研究水电机组运行状态健康评估和性能劣化趋势预测是非常必要的。
考虑到水电机组状态监测技术已得到广泛应用,但现阶段水电机组故障样本较少,现有的诊断技术无法满足工程应用等实际情况,提出基于健康评估和劣化趋势预测的水电机组故障诊断新思路。从研究机组运行正常特征入手,通过建立健全的监测特征量健康样本来实现水电机组的健康诊断,诊断方法侧重于设备运行状态的实时健康诊断,重点在于监测异常和预测异常,其诊断方法与传统的基于故障征兆的诊断方法有较大差异。鉴于水电机组实际运行中,出现的故障概率较小,基于该诊断理念开发的诊断系统工程实用性很强,且系统具备实时健康诊断和量化的性能退化趋势预测功能,既能实时监测异常,又能预测异常,可满足现阶段的工程应用需求。本文从运行状态特征提取、健康标准及健康样本的建立、基于特征样本的健康评估、基于时序分解模型的状态趋势预测、基于LS-SVM的参数性能退化评估以及集成化故障诊断系统的研究与应用六个方面,系统开展了基于健康评估和劣化趋势预测的水电机组故障诊断技术与应用研究。
以水电机组的水轮机和发电机为研究对象,在归纳总结水电机组运行异常时可能出现的各种特征表现的基础上,提出了表征水电机组运行状态的特征参数,以及特征参数三种可以量化的特征指标：幅值、频率、波形形状,并给出了相应的计算方法。
提出了可用于水电机组运行状态健康评估的三种评价标准：绝对评价标准、相对评价标准、类比评价标准,并给出了标准限值相应的取值计算方法。根据概率论与数理统计学的极限理论,以及休哈特控制图理论,提出了采用样本均值作为特征量的标准值,以3σ准则确定的X。=X±3σ为报警界限值的特征量健康标准。
以机组前期正常运行条件下的振动监测样本为例,分析了机组运行条件(功率、水头)对监测参数特征量指标的影响,提出以控制样本方差的方法来对运行条件进行分区,建立分区健康样本的具体方法和步骤,这样既保证了样本的判异准确性,同时也减低了样本空间维数(样本个数)。
建立了基于时间序列变化分解的水电机组特征量趋势预测模型,提出了基于时间序列分解模型的趋势预测和性能退化预测的算法。采用电站实际监测数据对分解模型和算法进行了验证,结果表明,预测趋势与实际监测趋势具有很好的吻合性,可满足水电机组监测特征量的趋势预测和性能退化预测,对早期预警机组潜在异常,具有很好的实用性。
以水电机组振动为例,提出了基于LS-SVM的水电机组振动参数性能退化评估三维标准模型(功率-水头-振动量)。将机组实时运行的有功功率和工作水头代入训练好的模型,即可获取当前工况下机组振动量是否偏离正常状态,实现机组运行状态的健康评估。基于振动参数性能退化时间序列,提出了基于LS-SVM的水电机组振动参数性能退化预测模型,采用上导摆度和上机架振动参数现场状态监测数据对所提模型进行验证。结果表明,该模型能较好的对水电机组振动参数性能退化进行评估和预测。
最后以三峡集团公司远程状态监测与故障诊断系统为例介绍了集成化故障诊断系统的研究与应用,提出了现地监测层—厂站集成层—中心诊断层分布式故障诊断系统的总体结构,以标准化的数据格式来实现不同监测设备之间的数据通信,通过标准化数据平台的集成,实现了状态信息的共享和多信息的融合诊断。通过建立机组运行状态健康样本库,实现了机组运行状态的实时健康评估和性能退化预测,达到了设备健康状态定量评估的目的,可为指导机组状态检修提供技术依据。
The real-time diagnosis of hydropower units operating state is directly related to the important economic and social benefits of the hydropower station, such as security and stability running, power quality and electricity production costs, etc.. With the continuous expansion of power plant scale and monitoring auxiliary systems, information of unit's control and monitoring data is growing significantly, making it more and more difficult to the real-time monitoring of unit operating status and the quick and accurate determination of equipment failure for the operating personnels. Therefore, it's of great necessarity for us to research on health assessment of operating status and performance degradation trend forecast of hydropower units.
Taking into account the actual situation that hydropower units condition monitoring technology has been widely used while hydropower units fault samples are lack currently, and existing diagnostic technology can not meet the project applications, a new idea for hydropower units fault diagnosis based on health assessment and deterioration trend forecastideas was proposed. Starting from the normal feature research of unit operation, we achieved the health diagnosis of hydropower units by establishing health samples with sound monitoring features. Diagnostic methods focus on real-time health diagnosis of the equipment operating status, with emphasis on monitoring anomalies and predicting abnormal. This diagnostic method is quite different from traditional fault symptom-based methods. Since the probability of failure is small in the actual operation of hydropower units, diagnostic system developed based on this diagnostic concept is very practical. Moreover, the system with functions of real-time health diagnosis and quantified degradation trend forecasting, can not only monitor abnormal real-time, but also predict abnormalities, which meets the engineering applications at the present stage. Research on fault diagnosis of hydropower units based on health assessment and deterioration trend predicted was carried out in the paper from the study of the following six aspects including the running state feature extraction, establishment of health standards and health samples, feature sample based health assessment, state trend forecast based on timing decomposition model, LS-SVM based parameters performance degradation assessment and the research and application of integrated fault diagnosis system.
With hydraulic turbine and generator as the object of study, and on the basis of summarizing various feature performances in operation abnormal of hydropower units, the paper put forward characteristic parameters to characterize the operational status of the hydropower units, as well as its three characteristic indexes that could be quantified: amplitude, frequency and waveform shape, and the corresponding calculation methods were given.
Three evaluation criteria for operating state health assessment of hydropower units were proposed:absolute evaluation criteria, relative evaluation standard and analog evaluation criteria, and the corresponding value calculation methods of the standard limit given. According to Probability Theory and Limit theory of mathematical statistics, also the Shewhart control charts theory, we suggested the health standards for feature quantity that take the sample mean as the feature's standard value, and Xc=X±3σ determined by3a criteria to as alarm limit value.
In the paper, we analyzed the influences from unit operating conditions (power, head) to monitoring parameters characteristic indices illustrated by the case of vibration monitoring samples under normal operating conditions in unit's prophase, proposed to partition the operating condition s adopting the method to control the sample variance, and established specific methods and procedures for health samples of each part. Thus to ensure the accuracy of the sample abnormal judgment, and to reduce the sample space dimension (the number of samples) at the same time.
Also, characteristics trend forecasting model of hydropower units based on time series variation decomposition was established, and algorithms of trend forecast and performance degradation prediction based on this model were proposed as well. We verified this decomposition model and algorithms on power station monitoring data, results showing that the forecasting trends are in good agreement with monitoring trends, can meet the needs of trend forecasting and performance degradation prediction of monitor characteristic quantities in hydropower units, and are with good usability on the early warning of unit potential abnormalities.
Taking vibration of hydropower units as an example,3D standard model (power-head-vibration) for vibration parameters performance degradation assessment of hydropower unit based on LS-SVM was proposed. We can get the judgement whether the unit vibration under current conditions has deviated from normal state once the run-time active power and water head are written into the trained model, and achieve health assessment of unit's operating status. On the basis of performance degradation time series of vibration parameters, we established LS-SVM based vibration parameters performance degradation prediction model of hydropower unit, and employed the on-site monitoring data of upper guide swing and upper bracket vibration parameters to validate the proposed model. The results showed that the model could assess and predict vibration parameters performance degradation of hydropower units well.
In the end, the research and application of integrated fault diagnosis system was introduced illustrated by the case of remote status monitoring and fault diagnosis system of Three Gorges Corporation, and proposed the overall structure of the distributed fault diagnosis system, composed of on site monitoring layer-plant and substation integration layer-the central diagnostic layer. To achieve data communication between different monitoring devices, we standardized the data format. Through the integration of standardized data platform, state information sharing and multi-information fusion diagnosis were achieved. Real-time health assessment and performance degradation prediction of unit operating status can be achieved through the establishment of its healthy sample library, realizing the purpose of the quantitative assessment of equipment health status, and can provide technical basis for the guidance of condition-based maintenance of the unit.
考虑到水电机组状态监测技术已得到广泛应用,但现阶段水电机组故障样本较少,现有的诊断技术无法满足工程应用等实际情况,提出基于健康评估和劣化趋势预测的水电机组故障诊断新思路。从研究机组运行正常特征入手,通过建立健全的监测特征量健康样本来实现水电机组的健康诊断,诊断方法侧重于设备运行状态的实时健康诊断,重点在于监测异常和预测异常,其诊断方法与传统的基于故障征兆的诊断方法有较大差异。鉴于水电机组实际运行中,出现的故障概率较小,基于该诊断理念开发的诊断系统工程实用性很强,且系统具备实时健康诊断和量化的性能退化趋势预测功能,既能实时监测异常,又能预测异常,可满足现阶段的工程应用需求。本文从运行状态特征提取、健康标准及健康样本的建立、基于特征样本的健康评估、基于时序分解模型的状态趋势预测、基于LS-SVM的参数性能退化评估以及集成化故障诊断系统的研究与应用六个方面,系统开展了基于健康评估和劣化趋势预测的水电机组故障诊断技术与应用研究。
以水电机组的水轮机和发电机为研究对象,在归纳总结水电机组运行异常时可能出现的各种特征表现的基础上,提出了表征水电机组运行状态的特征参数,以及特征参数三种可以量化的特征指标：幅值、频率、波形形状,并给出了相应的计算方法。
提出了可用于水电机组运行状态健康评估的三种评价标准：绝对评价标准、相对评价标准、类比评价标准,并给出了标准限值相应的取值计算方法。根据概率论与数理统计学的极限理论,以及休哈特控制图理论,提出了采用样本均值作为特征量的标准值,以3σ准则确定的X。=X±3σ为报警界限值的特征量健康标准。
以机组前期正常运行条件下的振动监测样本为例,分析了机组运行条件(功率、水头)对监测参数特征量指标的影响,提出以控制样本方差的方法来对运行条件进行分区,建立分区健康样本的具体方法和步骤,这样既保证了样本的判异准确性,同时也减低了样本空间维数(样本个数)。
建立了基于时间序列变化分解的水电机组特征量趋势预测模型,提出了基于时间序列分解模型的趋势预测和性能退化预测的算法。采用电站实际监测数据对分解模型和算法进行了验证,结果表明,预测趋势与实际监测趋势具有很好的吻合性,可满足水电机组监测特征量的趋势预测和性能退化预测,对早期预警机组潜在异常,具有很好的实用性。
以水电机组振动为例,提出了基于LS-SVM的水电机组振动参数性能退化评估三维标准模型(功率-水头-振动量)。将机组实时运行的有功功率和工作水头代入训练好的模型,即可获取当前工况下机组振动量是否偏离正常状态,实现机组运行状态的健康评估。基于振动参数性能退化时间序列,提出了基于LS-SVM的水电机组振动参数性能退化预测模型,采用上导摆度和上机架振动参数现场状态监测数据对所提模型进行验证。结果表明,该模型能较好的对水电机组振动参数性能退化进行评估和预测。
最后以三峡集团公司远程状态监测与故障诊断系统为例介绍了集成化故障诊断系统的研究与应用,提出了现地监测层—厂站集成层—中心诊断层分布式故障诊断系统的总体结构,以标准化的数据格式来实现不同监测设备之间的数据通信,通过标准化数据平台的集成,实现了状态信息的共享和多信息的融合诊断。通过建立机组运行状态健康样本库,实现了机组运行状态的实时健康评估和性能退化预测,达到了设备健康状态定量评估的目的,可为指导机组状态检修提供技术依据。
The real-time diagnosis of hydropower units operating state is directly related to the important economic and social benefits of the hydropower station, such as security and stability running, power quality and electricity production costs, etc.. With the continuous expansion of power plant scale and monitoring auxiliary systems, information of unit's control and monitoring data is growing significantly, making it more and more difficult to the real-time monitoring of unit operating status and the quick and accurate determination of equipment failure for the operating personnels. Therefore, it's of great necessarity for us to research on health assessment of operating status and performance degradation trend forecast of hydropower units.
Taking into account the actual situation that hydropower units condition monitoring technology has been widely used while hydropower units fault samples are lack currently, and existing diagnostic technology can not meet the project applications, a new idea for hydropower units fault diagnosis based on health assessment and deterioration trend forecastideas was proposed. Starting from the normal feature research of unit operation, we achieved the health diagnosis of hydropower units by establishing health samples with sound monitoring features. Diagnostic methods focus on real-time health diagnosis of the equipment operating status, with emphasis on monitoring anomalies and predicting abnormal. This diagnostic method is quite different from traditional fault symptom-based methods. Since the probability of failure is small in the actual operation of hydropower units, diagnostic system developed based on this diagnostic concept is very practical. Moreover, the system with functions of real-time health diagnosis and quantified degradation trend forecasting, can not only monitor abnormal real-time, but also predict abnormalities, which meets the engineering applications at the present stage. Research on fault diagnosis of hydropower units based on health assessment and deterioration trend predicted was carried out in the paper from the study of the following six aspects including the running state feature extraction, establishment of health standards and health samples, feature sample based health assessment, state trend forecast based on timing decomposition model, LS-SVM based parameters performance degradation assessment and the research and application of integrated fault diagnosis system.
With hydraulic turbine and generator as the object of study, and on the basis of summarizing various feature performances in operation abnormal of hydropower units, the paper put forward characteristic parameters to characterize the operational status of the hydropower units, as well as its three characteristic indexes that could be quantified: amplitude, frequency and waveform shape, and the corresponding calculation methods were given.
Three evaluation criteria for operating state health assessment of hydropower units were proposed:absolute evaluation criteria, relative evaluation standard and analog evaluation criteria, and the corresponding value calculation methods of the standard limit given. According to Probability Theory and Limit theory of mathematical statistics, also the Shewhart control charts theory, we suggested the health standards for feature quantity that take the sample mean as the feature's standard value, and Xc=X±3σ determined by3a criteria to as alarm limit value.
In the paper, we analyzed the influences from unit operating conditions (power, head) to monitoring parameters characteristic indices illustrated by the case of vibration monitoring samples under normal operating conditions in unit's prophase, proposed to partition the operating condition s adopting the method to control the sample variance, and established specific methods and procedures for health samples of each part. Thus to ensure the accuracy of the sample abnormal judgment, and to reduce the sample space dimension (the number of samples) at the same time.
Also, characteristics trend forecasting model of hydropower units based on time series variation decomposition was established, and algorithms of trend forecast and performance degradation prediction based on this model were proposed as well. We verified this decomposition model and algorithms on power station monitoring data, results showing that the forecasting trends are in good agreement with monitoring trends, can meet the needs of trend forecasting and performance degradation prediction of monitor characteristic quantities in hydropower units, and are with good usability on the early warning of unit potential abnormalities.
Taking vibration of hydropower units as an example,3D standard model (power-head-vibration) for vibration parameters performance degradation assessment of hydropower unit based on LS-SVM was proposed. We can get the judgement whether the unit vibration under current conditions has deviated from normal state once the run-time active power and water head are written into the trained model, and achieve health assessment of unit's operating status. On the basis of performance degradation time series of vibration parameters, we established LS-SVM based vibration parameters performance degradation prediction model of hydropower unit, and employed the on-site monitoring data of upper guide swing and upper bracket vibration parameters to validate the proposed model. The results showed that the model could assess and predict vibration parameters performance degradation of hydropower units well.
In the end, the research and application of integrated fault diagnosis system was introduced illustrated by the case of remote status monitoring and fault diagnosis system of Three Gorges Corporation, and proposed the overall structure of the distributed fault diagnosis system, composed of on site monitoring layer-plant and substation integration layer-the central diagnostic layer. To achieve data communication between different monitoring devices, we standardized the data format. Through the integration of standardized data platform, state information sharing and multi-information fusion diagnosis were achieved. Real-time health assessment and performance degradation prediction of unit operating status can be achieved through the establishment of its healthy sample library, realizing the purpose of the quantitative assessment of equipment health status, and can provide technical basis for the guidance of condition-based maintenance of the unit.
引文
[1]刘娟,潘罗平,桂中华,等.国内水电机组状态监测和故障诊断技术现状[J].大电机技术,2010,(2)：45-49.
[2]安学利,潘罗平,张飞.基于三维曲面的抽水蓄能电站机组故障预警模型[J].水力发电,2013,39(1)：71-74.
[3]安学利,潘罗平,桂中华,周叶.抽水蓄能电站机组异常状态检测模型研究[J].水电能源科学,2013,31(1)：157-160.
[4]卢进玉,马龙,肖艳风.水电站检修维护管理现状及趋势[C].第十八次中国水电设备学术讨论会论文集,2011.10：375-379.
[5]李平诗.浅谈水电厂的状态检修[J].水力发电,2002.28(6)：1-3.
[6]张风鸣,惠晓滨.航空设备故障诊断学[M].北京,国防工业出版社,2010.6：6-9.
[7]温熙森.模式识别与状态监控[M].北京,科学出版社,2007.10：2-4.
[8]潘罗平,桂中华.水轮发电机组状态监测技术[M].广州,华南理工大学出版社,2008.12：5-8.
[9]刘晓亭,冯辅周.水电机组运行设备监测诊断技术及应用[M].北京,中国水利水电出版社,2010.12：10-15.
[10]余涛,王晶,高峰,束洪春等.水电机组故障诊断专家系统研究现状与发展前景[J].云南电力技术,1999(2)：50-53.
[11]Beard R V. Failure Accommodation in Linear Systems through Self-reorganization [D]. Department of Aeronautics and Astronautics, MIT, USA,1971.
[12]Willsky A S. A survey of design methods for failure detection in dynamic systems[J]. Automatica,1976,12(6):601-611.
[13]Young P. Parameter estimation for continuous time models--a survey[J]. Automatica, 1981,17(1):23-39.
[14]Isermann R. Process fault detection based on modelling and estimation method--a survey[J]. Automatica,1984,20(4):387-404.
[15]Chen C, Zhang B, Vachtsevanos G, Orchard M. Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering[J]. IEEE Transactions on Industrial Electronics,2011,58(9):4353-4364.
[16]Caceres S, Henley E J. Process failure analysis by block diagrams and fault trees[J]. Industrial and Engineering Chemistry, Fundamentals,1976,15(2):128-134.
[17]Ulerich N H, Powers G A. Online hazard aversion and fault diagnosis in chemical processes:the digraph--fault tree method[J]. IEEE Transactions on Reliability,1988, 37(2):171-177.
[18]Dokas I M, Karras D A, Panagiotakopoulos D C. Fault tree analysis and fuzzy expert systems:early warning and emergency response of landfill operations[J]. Environmental Modelling and Software,2008,24(1):8-25.
[19]Iri M, Aoki K, O'Shima E, Matsuyama H. An algorithm for diagnosis of system failures in the chemical process[J]. Computers and Chemical Engineering,1979,3(1-4): 489-493.
[20]Umeda T, Kuriyama T, O'Shima E, Matsuyama H. A graphical approach to cause and effect analysis of chemical processing systems[J]. Chemical Engineering Science,1980, 35(12):2379-2388.
[21]Kramer M A, Palowitch B L. A rule based approach to fault diagnosis using the signed directed graph[J]. American Institute of Chemical Engineers Journal,1987,33(7): 1067-1078.
[22]Han C, Shih R, Lee L. Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement[J]. Industrial and Engineering Chemistry Research, 1994,33(8):1943-1954.
[23]Shih R, Lee L. Use of fuzzy cause--effect digraph for resolution fault diagnosis for process plants Ⅰ:Fuzzy cause--effect digraph[J]. Industrial and Engineering Chemistry Research,1995,34(5):1688-1702.
[24]Shih R, Lee L. Use of fuzzy cause--effect digraph for resolution fault diagnosis for process plants II:Diagnostic algorithm and applications[J]. Industrial and Engineering Chemistry Research,1995,34(5):1703-1717.
[25]Vedam H, Venkatasubramanian V. Signed digraph based multiple fault diagnosis[J]. Computers and Chemical Engineering,1997,21:655-660.
[26]Tarifa E, Scenna N. Fault diagnosis, directed graphs, and fuzzy logic[J]. Computers and Chemical Engineering,1997,21:649-654.
[27]Kim H, Lee K. Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship[J]. Fuzzy Sets and Systems, 1998,97(3):303-313.
[28]Yang F, Xiao D Y. Model and fault inference with the frame work of probabilistic SDG. In:Proceedings of the 9th International Conference on Control, Automation, Robotics, and Vision. Piscataway, USA:IEEE,2006.1-6.
[29]Venkatasubramanian V. Towards a specialized shell for diagnostic expert systems for chemical process plants[J]. Journal of Loss Prevention in the Process Industries,1988, 1(2):84-91.
[30]Ungar L H, Venkatasubramanian V. Artificial intelligence in process systems engineering:knowledge representation[M]. Austin, TX:CACHE.1990.
[31]Basila M Jr, Stefanek G, Cinar A. A model-object based supervisory expert system for fault tolerant chemical reactor control[J]. Computers and Chemical Engineering,1990, 14(4-5):551-560.
[32]Zhao J, Chen B, Shen J. A hybrid ANN-ES system for dynamic fault diagnosis of hydrocracking process[J]. Computers and Chemical Engineering,1997, (21):929-933.
[33]Leung D, Romagnoli J. Dynamic probabilistic model-based expert system for fault diagnosis[J]. Computers and Chemical Engineering,2000,24(11):2473-2492.
[34]Abbas M, Vachtsevanos G. An Energy Flow Approach to Fault Propagation Analysis[C].2009 IEEE Aerospace Conference Proceedings,2009, pp.1-13.
[35]Bae H, Kim S, Vachtsevanos G. Fault Detection and Diagnosis of Winding Short in BLDC Motors Based on Fuzzy Similarity[J]. International Journal of Fuzzy Logic and Intelligent Systems,2009,9(2):99-104.
[36]潘罗平,周叶,唐澍,桂中华.水轮发电机组状态监测技术的现状及展望[J].水电站机电技术,2008,(6)：1-6.
[37]潘罗平,唐波,周叶,唐澍.水电机组故障诊断技术的应用现状与技术发展[J].水电站机电技术,2010,(3)：107-110.
[38]张伟,褚福磊,张正松.300MW抽水蓄能机组振动状态监测分析诊断系统研究[J].清华大学学报(自然科学版),1998,(4)：108-112.
[39]梁武科,张彦宁,罗兴锜.水电机组故障诊断系统信号特征的提取[J].大电机技术,2003,(4)：53-56
[40]王清,潘宏侠,周传刚.机械故障诊断技术现状及趋势[J].机械管理开发,2005,(6)：49-51.
[41]中国科学技术协会.智能科学与技术学科发展报告[A].北京,中国科学技术出版社,2010.4：47-53.
[42]李伟.复杂系统的智能故障诊断技术现状及其发展趋势[J].计算机仿真,2004,(10)：4-7.
[43]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3)：1-14.
[44]胡桥,何正嘉,訾艳阳,张周锁,雷亚国.一种新的混合智能预测模型及其在故障诊断中的应用[J].西安交通大学学报,2005,39(9)：928-932.
[45]王奉涛,马孝江,张勇.基于局域波-粗糙集-神经网络的故障诊断方法研究[J].内燃机工程,2007,28(2)：80-84.
[46]沈仁发,祁彦洁,康海英,郑海起,关贞珍.基于阶次小波包与粗糙集的轴承复合故障诊断[J].轴承,2009(9)：53-56.
[47]刘斌,陈荣洲,贺秀儒.基于自适应神经网络模糊推理的水电机组故障诊断[J].水电站自动化与大坝监测,2005,29(2)：28-31.
[48]朱文.基于神经网络专家系统的电机故障诊断研究[D].天津科技大学,2002.
[49]刘忠,周建中,张勇传,邹敏.基于水电机组复合特征提取的RBFNN故障诊断[J].电力系统自动化,2007,31(11)：87-91.
[50]彭文季,郭鹏程,罗兴铸.基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断研究[J].水力发电学报,2007,26(6)：137-142.
[51]粱武科,赵道利,马薇,王荣荣,南海鹏,罗兴绮.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007,28(10)：1806-1810.
[52]陈喜阳,张克危,彭玉成.基于多种诊断方法的水电机组故障诊断系统的研究[J].水力发电,2003(5)：9-12.
[53]Rasmussen J. The role of hierarchical knowledge representation in decision making-and system management[J]. IEEE Transactions on Systems, Man and Cybernetics 15(2):234-243.
[54]Rasmussen J. Information processing and human-machine interaction[M]. New York, North Holland,1986.
[55]Kramer M A, Mah R S H. Model-based monitoring. In:Rippin D, Hale J, Davis J(Eds), Proceedings of the second international conference on'foundations of computer-aided process operations'(FOCAPO),1993, pp:45-68.
[56]Frank P M. Fault diagnosis in dynamics systems using analytical and knowledge-based redundancy-a survey and some new results[J]. Automatica,1990,26(3):459-474.
[57]Yoshimura M, Frank P M, Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems[J]. Journal of Process Control,1997, 7(6):403-424.
[58]Patton R J, Frank P M, Clark R N. Issues of Fault Diagnosis for Dynamic Systems[M]. London:Springer-Verlag,2000.
[59]Frank P M, Schrier G, Alcorta Garcia E. Nonlinear observer for fault detection and isolation[M]. London:Springer-Verlag,1999:399-422.
[60]周东华,叶银忠.现代故障诊断与容错控制[M].北京：清华大学出版,2000.
[61]薛光辉,吴淼.机电设备故障诊断方法研究现状与发展趋势[J].煤炭工程,2010(5)：103-105.
[62]贾庆贤,张迎春,管宇,陈雪芹.基于解析模型的非线性系统故障诊断方法综述[J].信息与控制,2012,41(3)：356-264
[63]Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri Surya N. A review of process fault detection and diagnosis part Ⅰ:quantitative model-based methods[J]. Computers and Chemical Engineering,2003,27(3):293-311.
[64]Venkatasubramanian V, Rengaswamy R, Kavuri Surya N. A re-view of process fault detection and diagnosis part Ⅱ:qualitative models and search strategies[J]. Computers and Chemical Engineering,2003,27(3):313-326.
[65]Venkatasubramanian V, Rengaswamy R, Kavuri Surya N, Yin K. A review of process fault detection and diagnosis part Ⅲ:process history based methods[J]. Computers and Chemical Engineering,2003,27(3):327-346.
[66]周东华,胡艳艳.动态系统的故障诊断技术[J].自动化学报,2009,35(6)：748-758.
[67]张朋友,吕明.近代信号处理理论和方法[M].北京：国防工业出版社,2005.
[68]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[D].华中科技大学,2008.
[69]彭文季,罗兴椅.基于小波包分析和支持向量机的水电机组振动故障诊断研究[J].中国电机工程学报,2006,26(24)：164-168.
[70]彭文季.水电机组振动故障的智能诊断方法研究[D].西安理工大学,2007.
[71]Mehra R K, Peschon J. An innovation approach to fault detection and diagnosis in dynamics systems. Automatica,1971,7(5):637-640.
[72]Chen J, Lopez-Toribio C L, Patton R J. Non-linear dynamic systems fault detection and isolation using fuzzy observers. Proceedings of the Institution of Mechanical Engineers, Part I:Journal of Systems and Control Engineering,1999,213(6):467-476.
[73]贾庆贤,张迎春,管宇,陈雪芹.基于解析模型的非线性系统故障诊断方法综述[J]. 信息与控制,2012,41(3)：356-364.
[74]Orchard M, Vachtsevanos G. A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis[J]. Transactions of the Institute of Measurement and Control,2009,31(3-4):221-246.
[75]Chang C C, Yu C C. On-line fault diagnosis using the signed directed graph[J]. Industrial and Engineering Chemistry Research,1990,29(7):1290-1299.
[76]吴军强,梁军.基于图论的故障诊断技术及其发展[J].机电工程,2003,20(5)：188-190.
[77]Yager R R, Filev D P. Gereration of fuzzy rules by mountain clustering[J]. Journal of Intelligent and Fuzzy Systems,1994(2):209-219.
[78]戴其兵,傅行军.旋转机械故障诊断方法概述[0L].中国科技论文在线,2007.06.04
[79]王毅.风机故障诊断的集成神经网络信息融合法[J].风机技术,2005(2)：35-37.
[80]孙卫祥,陈进,伍星,等.基于信息融合的支撑座早期松动故障诊断[J].上海交通大学学报,2006,40(2)：239-243.
[81]姜万录,李冲祥.神经网络和证据理论融合的故障诊断方法研究[J].中国机械工程,2004,15(9)：760-764.
[82]Chen C, Zhang B, Vachtsevanos G. Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms[J]. IEEE Transactions on Instrumentation and Measurement,2012,61(1):297-306.
[83]Venkatasubramanian V. Inexact reasoning in expert systems:a stochastic parallel network approach[C]. In Proceedings of the second conference on artificial intelligence applications, Miami, FL.1985, pp:191-195.
[84]Venkatasubramanian V, Chan K. A neural network methodology for process fault diagnosis[J]. American Institute of Chemical Engineers Journal,1989,35(12): 1993-2002.
[85]Watanabe K, Matsura I, Abe M, Kubota M, Himmelblau D M. Incipient fault diagnosis of chemical processes via artificial neural networks[J]. American Institute of Chemical Engineers Journal,1989,35(11):1803-1812.
[86]Ungar L H, Powell B A, Kamens S N. Adaptive networks for fault diagnosis and process control[J]. Computers and Chemical Engineering,1990,14(4-5):561-572.
[87]Hoskins J C, Kaliyur K M, Himmelblau D M. Fault diagnosis in complex chemical plants using artificial neural networks[J]. American Institute of Chemical Engineers Journal,1991,37(1):137-141.
[88]Venkatasubramanian V, Vaidyanathan R, Yamamoto Y. Process fault detection and diagnosis using neural networks I:steady state processes[J]. Computers and Chemical Engineering,1990,14(7):699-712.
[89]Vaidyanathan R, Venkatasubramanian V. Representing and diagnosing dynamic process data using neural networks[J]. Engineering Applications of Artificial Intelligence,1992, 5(1):11-21.
[90]Watanabe K, Hirota S, Iloa L, Himmelblau D M. Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks[J]. American Institute of Chemical Engineers Journal,1994,40(5):839-848.
[91]Tsai C S, Chang C T. Dynamic process diagnosis via integrated neural networks[J]. Computers and Chemical Engineering,1995(19):S747-S752.
[92]Becraft W, Lee P. An integrated neural network/expert system approach for fault diagnosis[J]. Computers and Chemical Engineering,1993,17(10):1001-1014.
[93]Leonard J A, Kramer M A. Limitations of backpropagation approach to fault diagnosis and improvements with radial basis functions[C]. In AIChE annual meeting, Chicago, 1990.
[94]Bakshi B R, Stephanopoulos G. Wave-net:a multiresolution, hierarchical neural network with localized learning[J]. American Institute of Chemical Engineers Journal, 1993,39(1):57-81.
[95]张君,韩璞,董泽等.基于小波变换的振动信号分析中能量泄漏的研究[J].中国电机工程学报,2004,24(10)：238-243
[96]洪冶,李国宏,蔡维由等.基于小波包分析的水轮发电机组振动的故障诊断[J].武汉大学学报,2002,35(1)：65-68.
[97]张周锁,李凌均,何正嘉.基于支持向量机的机械故障诊断方法研究[J].西安交通大学学报,2002,36(12)：1303-130
[98]翟永杰,韩璞,王东风等.基于损失函数的SVM算法及其在轻微故障诊断中的应用[J1.中国电机工程学报,2005,23(9)：198-203
[99]何学文,赵海鸣.支持向量机及其在机械故障诊断中的应用[J].中南大学学报(自然科学版),2005,36(1)：97-101.
[100]邓慧琼,艾欣,刘昊.基于支持向量机的电力系统连锁故障评估方法研究[J].中国电机工程学报,2005,25(25)：178-183.
[101]孙斌,周云龙.基于支持向量机和小波包能量特征的气液两相流流型识别方法[J].中国电机工程学报,2005,25(17)：93-99
[102]徐启华,师军.基于支持向量机的航空发动机故障诊断[J].航空动力学报,2005,20(2)：298-302.
[103]Hsu Chihwei, Lin Chihjen. A comparison of methods for multi-classsupport vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[104]Scholkopf B, Simard P, Smola A, Vapnik V N. Prior knowledge in support vector kernels[J]. Advances in Neural Information Processing Systems,1998,10:37-49.
[105]Amari S, Wu S. Improving support vector machine classifiers by modifying kernel function[J]. Journal of Neural Networks,1999,12:783-789.
[106]邓乃扬,田英杰.数据挖掘中的新方法：支持向量机[M].北京,科学出版社,2004.307-339.
[107]赵玲玲,杨奎河,任晓鹏,单甘霖.支持向量机在机械故障诊断中的应用[J].河北工业科技,2007,24(1)：37-39.
[108]Camil Fuchs, Ron S Kenett. Multivariate quality control:Theory and application[M]. New York:Marcel Dekker,1998.
[109]Necip Doganaksoy, Frederick W Faltin, William T Tucker. Identification of out of control quality characteristics in a multivariate manufacturing environment[J]. Communication in Statistics-Theory and Methods,1991,20(9):2775-2790.
[110]Lee J M, Yoo C K, Choi S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[111]Lee JM, Yoo C K, Lee IB. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers and Chemical Engineering,2004,28(9): 1837-1840.
[112]Pearson K. On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine,1901, Series B(2):559-572.
[113]Hotelling H. Multivariate quality control illustrated by the testing of sample bombsights[M]. In O. Eisenhart (Ed.), Selected techniques of statistical analysis, New York:McGraw-Hill,1947, pp:113-184.
[114]Kresta J V, MacGregor J F, Marlin T E. Multivariate statistical monitoring of processes[J]. Canadian Journal of Chemical Engineering,1991,69(1):35-47.
[115]Nomikos P, MacGregor J. Monitoring batch processes using multiway principal component analysis[J]. American Institute of Chemical Engineers Journal,1994,40(8): 1361-1375.
[116]Qin S J, McAvoy T J. Nonlinear PLS modeling using neural networks[J]. Computers and Chemical Engineering,1992,16(4):379-391.
[117]Qin S. Recursive PLS algorithm for adaptive data monitoring[J]. Computers and Chemical Engineering,1998,22(4/5):503-514.
[118]Bakshi B. Multiscale PCA with application to multivariate statistical process monitoring[J]. American Institute of Chemical Engineers Journal,1998,44(7): 1596-1610.
[119]王俊国,闽松,赵金,万淑芸.基于模糊Petri网的故障诊断方法[J].自动检测技术,2002,21(6)：18-21.
[120]杨蓓,侯西梅,艾艳.专家系统中的模糊知识表示及推理研究[J].郑州大学学报,2004,36(2)：31-33.
[121]王奉涛,马孝江,邹岩琨.智能故障诊断技术综述[J].机床与液压,2003(4)：6-8.
[122]Bocanialad, Costab J S D. Application of a novel fuzzy classifier to fault detection and isolation of the DAMADICS benchmark problem Cosmin[J]. Control Engineering Practice,2006(14):653-669.
[123]禹柳飞,张友斌.基于模糊逻辑的旋转机组故障诊断方法[J].微计算机信息,2008,24(9)：225-227.
[124]苏鹏声,王欢.电力系统设备状态监测与故障诊断技术分析[J].电力系统自动化,2003,27(1)：61-65.
[125]梁武科,张彦宁,罗兴铸,水电机组故障诊断系统信号特征的提取,大电机技术,2003.No4,53-56
[126]潘罗平等,大型水电机组状态监测与诊断检修系统总体规划报告[R],中国水利水电科学研究,2008.
[127]DL/T 11972012水轮发电机组状态在线监测系统技术条件.国家能源局发布,2012.
[128]GB/T 28570-2012水轮发电机组状态在线监测系统技术导则.中华人民共和国国家质量监督检验检疫总局发布,2012.
[129]白恺,白亚民.发电机定子绕组端部机械振动模态的测量[J].华北电力技术,2001(8)：5-10.
[130]张润时,贺建华,郑松远.发电机空气间隙在线监测技术在三峡右岸电站的应用[J].水电站机电技术,2008(06)：11-16.
[131]王伯雄.测试技术基础[M].北京,清华大学出版社,2003：103-115.
[132]杨桂元,李天胜,徐军.数学模型应用实例[M].合肥,合肥工业大学出版社,2007：200-211.
[133]王承双.3σ准则与测量次数n的关系[J].长沙电力学院学报,1996(2)：73-74.
[134]齐民友.概率论与数理统计[M].北京,高等教育出版社,2004：171-183.
[135]GB/T 4091-2001常规控制图.国家质量技术监督局发布,2001.
[136]Tipping M E, Bishop C M. Probabilistic principal component analysis[J]. Journal of the Royal Statistical Society, SeriesB,1999,61(3):611-622.
[137]李晗,萧德云.基于数据驱动的故障诊断方法综述[M].控制与决策,2011,26(1)：2-9.
[138]Kaneko H, Arakawa M, Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. American Institute Chemical Engineers Journal,2009,55(1):87-98.
[139]赵旭.基于统计学方法的过程监控与质量控制研究[D].上海交通大学,2007.3.
[140]GB/T7894-2009水轮发电机基本技术条件.中华人民共和国国家质量监督检验检疫总局发布,2009.
[141]IEC60034-1 Rotating electrical machines Part1:Rating and performance. International Electrotechnical Commission(IEC),2010.
[142]SL321-2005大中型水轮发电机基本技术条件.中华人民共和国水利部发布,2005.
[143]丰田利夫,顾力刚.设备劣化倾向管理[R].中国设备管理,2000.8-2000.11.
[144]GB/T 6075.5-2002在非旋转部件上测量和评价机器的机械振动第五部分：水力发电厂和泵站机组.中华人民共和国国家质量监督检验检疫总局发布,2002.
[145]GB/T 15468-2006水轮机基本技术条件.中华人民共和国国家质量监督检验检疫总局发布,2006.
[146]GB/T 8564-2003水轮发电机组安装技术规范.中华人民共和国国家质量监督检验检疫总局发布,2003.
[147]谢宝义.工程机械状态监测与故障诊断技术[M].中国设备工程,2002：8-9
[148]吕林涛,王鹏,李军怀等.基于时间序列的趋势性分析及其预测算法研究[J].计算 机工程与应用.2004.19：173.
[149]宋仙磊,刘业政,陈思凤.基于周期项方法选择的季节性时序预测[J].计算机工程,2011,37(21)：132-135.
[150]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备,2005,25(8)：32-34.
[151]V. Tran, H. Pham, B. Yang, T. Nguyen. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing,2012,32:320-330.
[152]Pan Y, Chen J, Li X. Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means[J]. Mechanical Systems and Signal Processing,2010,24:559-566.
[153]安学利,潘罗平,张飞.基于三维曲面的抽水蓄能电站机组故障预警模型[J].水力发电,2013,39(1)：71～74.
[154]安学利,潘罗平,桂中华,周叶.抽水蓄能电站机组异常状态检测模型研究[J].水电能源科学,2013,31(1)：157-160.
[155]周淙,孙威,任海军,等.基于最小二乘支持向量机和负荷密度指标法的配电网空间负荷预测[J].电网技术,2011,35(1)：66-71.
[156]Xueli An, Dongxiang Jiang, Chao Liu, etc. Wind farm power prediction based on wavelet decomposition and chaotic time series[J]. Expert Systems with Applications, 2011,38(9):11280-11285.
[157]Cao L. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D,1997,110:43-50.
[158]潘罗平,周叶,安学利.水电站主设备状态集中监测与诊断系统的研发与应用[C].第十八次中国水电设备学术讨论会论文集,2011.10：380-385.
[159]潘家才,纪浩.智能电站建设的展望及思考[C].水电开发与经济社会可持续发展论坛论文集,2011：5-9.
[2]安学利,潘罗平,张飞.基于三维曲面的抽水蓄能电站机组故障预警模型[J].水力发电,2013,39(1)：71-74.
[3]安学利,潘罗平,桂中华,周叶.抽水蓄能电站机组异常状态检测模型研究[J].水电能源科学,2013,31(1)：157-160.
[4]卢进玉,马龙,肖艳风.水电站检修维护管理现状及趋势[C].第十八次中国水电设备学术讨论会论文集,2011.10：375-379.
[5]李平诗.浅谈水电厂的状态检修[J].水力发电,2002.28(6)：1-3.
[6]张风鸣,惠晓滨.航空设备故障诊断学[M].北京,国防工业出版社,2010.6：6-9.
[7]温熙森.模式识别与状态监控[M].北京,科学出版社,2007.10：2-4.
[8]潘罗平,桂中华.水轮发电机组状态监测技术[M].广州,华南理工大学出版社,2008.12：5-8.
[9]刘晓亭,冯辅周.水电机组运行设备监测诊断技术及应用[M].北京,中国水利水电出版社,2010.12：10-15.
[10]余涛,王晶,高峰,束洪春等.水电机组故障诊断专家系统研究现状与发展前景[J].云南电力技术,1999(2)：50-53.
[11]Beard R V. Failure Accommodation in Linear Systems through Self-reorganization [D]. Department of Aeronautics and Astronautics, MIT, USA,1971.
[12]Willsky A S. A survey of design methods for failure detection in dynamic systems[J]. Automatica,1976,12(6):601-611.
[13]Young P. Parameter estimation for continuous time models--a survey[J]. Automatica, 1981,17(1):23-39.
[14]Isermann R. Process fault detection based on modelling and estimation method--a survey[J]. Automatica,1984,20(4):387-404.
[15]Chen C, Zhang B, Vachtsevanos G, Orchard M. Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering[J]. IEEE Transactions on Industrial Electronics,2011,58(9):4353-4364.
[16]Caceres S, Henley E J. Process failure analysis by block diagrams and fault trees[J]. Industrial and Engineering Chemistry, Fundamentals,1976,15(2):128-134.
[17]Ulerich N H, Powers G A. Online hazard aversion and fault diagnosis in chemical processes:the digraph--fault tree method[J]. IEEE Transactions on Reliability,1988, 37(2):171-177.
[18]Dokas I M, Karras D A, Panagiotakopoulos D C. Fault tree analysis and fuzzy expert systems:early warning and emergency response of landfill operations[J]. Environmental Modelling and Software,2008,24(1):8-25.
[19]Iri M, Aoki K, O'Shima E, Matsuyama H. An algorithm for diagnosis of system failures in the chemical process[J]. Computers and Chemical Engineering,1979,3(1-4): 489-493.
[20]Umeda T, Kuriyama T, O'Shima E, Matsuyama H. A graphical approach to cause and effect analysis of chemical processing systems[J]. Chemical Engineering Science,1980, 35(12):2379-2388.
[21]Kramer M A, Palowitch B L. A rule based approach to fault diagnosis using the signed directed graph[J]. American Institute of Chemical Engineers Journal,1987,33(7): 1067-1078.
[22]Han C, Shih R, Lee L. Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement[J]. Industrial and Engineering Chemistry Research, 1994,33(8):1943-1954.
[23]Shih R, Lee L. Use of fuzzy cause--effect digraph for resolution fault diagnosis for process plants Ⅰ:Fuzzy cause--effect digraph[J]. Industrial and Engineering Chemistry Research,1995,34(5):1688-1702.
[24]Shih R, Lee L. Use of fuzzy cause--effect digraph for resolution fault diagnosis for process plants II:Diagnostic algorithm and applications[J]. Industrial and Engineering Chemistry Research,1995,34(5):1703-1717.
[25]Vedam H, Venkatasubramanian V. Signed digraph based multiple fault diagnosis[J]. Computers and Chemical Engineering,1997,21:655-660.
[26]Tarifa E, Scenna N. Fault diagnosis, directed graphs, and fuzzy logic[J]. Computers and Chemical Engineering,1997,21:649-654.
[27]Kim H, Lee K. Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship[J]. Fuzzy Sets and Systems, 1998,97(3):303-313.
[28]Yang F, Xiao D Y. Model and fault inference with the frame work of probabilistic SDG. In:Proceedings of the 9th International Conference on Control, Automation, Robotics, and Vision. Piscataway, USA:IEEE,2006.1-6.
[29]Venkatasubramanian V. Towards a specialized shell for diagnostic expert systems for chemical process plants[J]. Journal of Loss Prevention in the Process Industries,1988, 1(2):84-91.
[30]Ungar L H, Venkatasubramanian V. Artificial intelligence in process systems engineering:knowledge representation[M]. Austin, TX:CACHE.1990.
[31]Basila M Jr, Stefanek G, Cinar A. A model-object based supervisory expert system for fault tolerant chemical reactor control[J]. Computers and Chemical Engineering,1990, 14(4-5):551-560.
[32]Zhao J, Chen B, Shen J. A hybrid ANN-ES system for dynamic fault diagnosis of hydrocracking process[J]. Computers and Chemical Engineering,1997, (21):929-933.
[33]Leung D, Romagnoli J. Dynamic probabilistic model-based expert system for fault diagnosis[J]. Computers and Chemical Engineering,2000,24(11):2473-2492.
[34]Abbas M, Vachtsevanos G. An Energy Flow Approach to Fault Propagation Analysis[C].2009 IEEE Aerospace Conference Proceedings,2009, pp.1-13.
[35]Bae H, Kim S, Vachtsevanos G. Fault Detection and Diagnosis of Winding Short in BLDC Motors Based on Fuzzy Similarity[J]. International Journal of Fuzzy Logic and Intelligent Systems,2009,9(2):99-104.
[36]潘罗平,周叶,唐澍,桂中华.水轮发电机组状态监测技术的现状及展望[J].水电站机电技术,2008,(6)：1-6.
[37]潘罗平,唐波,周叶,唐澍.水电机组故障诊断技术的应用现状与技术发展[J].水电站机电技术,2010,(3)：107-110.
[38]张伟,褚福磊,张正松.300MW抽水蓄能机组振动状态监测分析诊断系统研究[J].清华大学学报(自然科学版),1998,(4)：108-112.
[39]梁武科,张彦宁,罗兴锜.水电机组故障诊断系统信号特征的提取[J].大电机技术,2003,(4)：53-56
[40]王清,潘宏侠,周传刚.机械故障诊断技术现状及趋势[J].机械管理开发,2005,(6)：49-51.
[41]中国科学技术协会.智能科学与技术学科发展报告[A].北京,中国科学技术出版社,2010.4：47-53.
[42]李伟.复杂系统的智能故障诊断技术现状及其发展趋势[J].计算机仿真,2004,(10)：4-7.
[43]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述[J].化工自动化及仪表,2001,28(3)：1-14.
[44]胡桥,何正嘉,訾艳阳,张周锁,雷亚国.一种新的混合智能预测模型及其在故障诊断中的应用[J].西安交通大学学报,2005,39(9)：928-932.
[45]王奉涛,马孝江,张勇.基于局域波-粗糙集-神经网络的故障诊断方法研究[J].内燃机工程,2007,28(2)：80-84.
[46]沈仁发,祁彦洁,康海英,郑海起,关贞珍.基于阶次小波包与粗糙集的轴承复合故障诊断[J].轴承,2009(9)：53-56.
[47]刘斌,陈荣洲,贺秀儒.基于自适应神经网络模糊推理的水电机组故障诊断[J].水电站自动化与大坝监测,2005,29(2)：28-31.
[48]朱文.基于神经网络专家系统的电机故障诊断研究[D].天津科技大学,2002.
[49]刘忠,周建中,张勇传,邹敏.基于水电机组复合特征提取的RBFNN故障诊断[J].电力系统自动化,2007,31(11)：87-91.
[50]彭文季,郭鹏程,罗兴铸.基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断研究[J].水力发电学报,2007,26(6)：137-142.
[51]粱武科,赵道利,马薇,王荣荣,南海鹏,罗兴绮.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007,28(10)：1806-1810.
[52]陈喜阳,张克危,彭玉成.基于多种诊断方法的水电机组故障诊断系统的研究[J].水力发电,2003(5)：9-12.
[53]Rasmussen J. The role of hierarchical knowledge representation in decision making-and system management[J]. IEEE Transactions on Systems, Man and Cybernetics 15(2):234-243.
[54]Rasmussen J. Information processing and human-machine interaction[M]. New York, North Holland,1986.
[55]Kramer M A, Mah R S H. Model-based monitoring. In:Rippin D, Hale J, Davis J(Eds), Proceedings of the second international conference on'foundations of computer-aided process operations'(FOCAPO),1993, pp:45-68.
[56]Frank P M. Fault diagnosis in dynamics systems using analytical and knowledge-based redundancy-a survey and some new results[J]. Automatica,1990,26(3):459-474.
[57]Yoshimura M, Frank P M, Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems[J]. Journal of Process Control,1997, 7(6):403-424.
[58]Patton R J, Frank P M, Clark R N. Issues of Fault Diagnosis for Dynamic Systems[M]. London:Springer-Verlag,2000.
[59]Frank P M, Schrier G, Alcorta Garcia E. Nonlinear observer for fault detection and isolation[M]. London:Springer-Verlag,1999:399-422.
[60]周东华,叶银忠.现代故障诊断与容错控制[M].北京：清华大学出版,2000.
[61]薛光辉,吴淼.机电设备故障诊断方法研究现状与发展趋势[J].煤炭工程,2010(5)：103-105.
[62]贾庆贤,张迎春,管宇,陈雪芹.基于解析模型的非线性系统故障诊断方法综述[J].信息与控制,2012,41(3)：356-264
[63]Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri Surya N. A review of process fault detection and diagnosis part Ⅰ:quantitative model-based methods[J]. Computers and Chemical Engineering,2003,27(3):293-311.
[64]Venkatasubramanian V, Rengaswamy R, Kavuri Surya N. A re-view of process fault detection and diagnosis part Ⅱ:qualitative models and search strategies[J]. Computers and Chemical Engineering,2003,27(3):313-326.
[65]Venkatasubramanian V, Rengaswamy R, Kavuri Surya N, Yin K. A review of process fault detection and diagnosis part Ⅲ:process history based methods[J]. Computers and Chemical Engineering,2003,27(3):327-346.
[66]周东华,胡艳艳.动态系统的故障诊断技术[J].自动化学报,2009,35(6)：748-758.
[67]张朋友,吕明.近代信号处理理论和方法[M].北京：国防工业出版社,2005.
[68]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[D].华中科技大学,2008.
[69]彭文季,罗兴椅.基于小波包分析和支持向量机的水电机组振动故障诊断研究[J].中国电机工程学报,2006,26(24)：164-168.
[70]彭文季.水电机组振动故障的智能诊断方法研究[D].西安理工大学,2007.
[71]Mehra R K, Peschon J. An innovation approach to fault detection and diagnosis in dynamics systems. Automatica,1971,7(5):637-640.
[72]Chen J, Lopez-Toribio C L, Patton R J. Non-linear dynamic systems fault detection and isolation using fuzzy observers. Proceedings of the Institution of Mechanical Engineers, Part I:Journal of Systems and Control Engineering,1999,213(6):467-476.
[73]贾庆贤,张迎春,管宇,陈雪芹.基于解析模型的非线性系统故障诊断方法综述[J]. 信息与控制,2012,41(3)：356-364.
[74]Orchard M, Vachtsevanos G. A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis[J]. Transactions of the Institute of Measurement and Control,2009,31(3-4):221-246.
[75]Chang C C, Yu C C. On-line fault diagnosis using the signed directed graph[J]. Industrial and Engineering Chemistry Research,1990,29(7):1290-1299.
[76]吴军强,梁军.基于图论的故障诊断技术及其发展[J].机电工程,2003,20(5)：188-190.
[77]Yager R R, Filev D P. Gereration of fuzzy rules by mountain clustering[J]. Journal of Intelligent and Fuzzy Systems,1994(2):209-219.
[78]戴其兵,傅行军.旋转机械故障诊断方法概述[0L].中国科技论文在线,2007.06.04
[79]王毅.风机故障诊断的集成神经网络信息融合法[J].风机技术,2005(2)：35-37.
[80]孙卫祥,陈进,伍星,等.基于信息融合的支撑座早期松动故障诊断[J].上海交通大学学报,2006,40(2)：239-243.
[81]姜万录,李冲祥.神经网络和证据理论融合的故障诊断方法研究[J].中国机械工程,2004,15(9)：760-764.
[82]Chen C, Zhang B, Vachtsevanos G. Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms[J]. IEEE Transactions on Instrumentation and Measurement,2012,61(1):297-306.
[83]Venkatasubramanian V. Inexact reasoning in expert systems:a stochastic parallel network approach[C]. In Proceedings of the second conference on artificial intelligence applications, Miami, FL.1985, pp:191-195.
[84]Venkatasubramanian V, Chan K. A neural network methodology for process fault diagnosis[J]. American Institute of Chemical Engineers Journal,1989,35(12): 1993-2002.
[85]Watanabe K, Matsura I, Abe M, Kubota M, Himmelblau D M. Incipient fault diagnosis of chemical processes via artificial neural networks[J]. American Institute of Chemical Engineers Journal,1989,35(11):1803-1812.
[86]Ungar L H, Powell B A, Kamens S N. Adaptive networks for fault diagnosis and process control[J]. Computers and Chemical Engineering,1990,14(4-5):561-572.
[87]Hoskins J C, Kaliyur K M, Himmelblau D M. Fault diagnosis in complex chemical plants using artificial neural networks[J]. American Institute of Chemical Engineers Journal,1991,37(1):137-141.
[88]Venkatasubramanian V, Vaidyanathan R, Yamamoto Y. Process fault detection and diagnosis using neural networks I:steady state processes[J]. Computers and Chemical Engineering,1990,14(7):699-712.
[89]Vaidyanathan R, Venkatasubramanian V. Representing and diagnosing dynamic process data using neural networks[J]. Engineering Applications of Artificial Intelligence,1992, 5(1):11-21.
[90]Watanabe K, Hirota S, Iloa L, Himmelblau D M. Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks[J]. American Institute of Chemical Engineers Journal,1994,40(5):839-848.
[91]Tsai C S, Chang C T. Dynamic process diagnosis via integrated neural networks[J]. Computers and Chemical Engineering,1995(19):S747-S752.
[92]Becraft W, Lee P. An integrated neural network/expert system approach for fault diagnosis[J]. Computers and Chemical Engineering,1993,17(10):1001-1014.
[93]Leonard J A, Kramer M A. Limitations of backpropagation approach to fault diagnosis and improvements with radial basis functions[C]. In AIChE annual meeting, Chicago, 1990.
[94]Bakshi B R, Stephanopoulos G. Wave-net:a multiresolution, hierarchical neural network with localized learning[J]. American Institute of Chemical Engineers Journal, 1993,39(1):57-81.
[95]张君,韩璞,董泽等.基于小波变换的振动信号分析中能量泄漏的研究[J].中国电机工程学报,2004,24(10)：238-243
[96]洪冶,李国宏,蔡维由等.基于小波包分析的水轮发电机组振动的故障诊断[J].武汉大学学报,2002,35(1)：65-68.
[97]张周锁,李凌均,何正嘉.基于支持向量机的机械故障诊断方法研究[J].西安交通大学学报,2002,36(12)：1303-130
[98]翟永杰,韩璞,王东风等.基于损失函数的SVM算法及其在轻微故障诊断中的应用[J1.中国电机工程学报,2005,23(9)：198-203
[99]何学文,赵海鸣.支持向量机及其在机械故障诊断中的应用[J].中南大学学报(自然科学版),2005,36(1)：97-101.
[100]邓慧琼,艾欣,刘昊.基于支持向量机的电力系统连锁故障评估方法研究[J].中国电机工程学报,2005,25(25)：178-183.
[101]孙斌,周云龙.基于支持向量机和小波包能量特征的气液两相流流型识别方法[J].中国电机工程学报,2005,25(17)：93-99
[102]徐启华,师军.基于支持向量机的航空发动机故障诊断[J].航空动力学报,2005,20(2)：298-302.
[103]Hsu Chihwei, Lin Chihjen. A comparison of methods for multi-classsupport vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[104]Scholkopf B, Simard P, Smola A, Vapnik V N. Prior knowledge in support vector kernels[J]. Advances in Neural Information Processing Systems,1998,10:37-49.
[105]Amari S, Wu S. Improving support vector machine classifiers by modifying kernel function[J]. Journal of Neural Networks,1999,12:783-789.
[106]邓乃扬,田英杰.数据挖掘中的新方法：支持向量机[M].北京,科学出版社,2004.307-339.
[107]赵玲玲,杨奎河,任晓鹏,单甘霖.支持向量机在机械故障诊断中的应用[J].河北工业科技,2007,24(1)：37-39.
[108]Camil Fuchs, Ron S Kenett. Multivariate quality control:Theory and application[M]. New York:Marcel Dekker,1998.
[109]Necip Doganaksoy, Frederick W Faltin, William T Tucker. Identification of out of control quality characteristics in a multivariate manufacturing environment[J]. Communication in Statistics-Theory and Methods,1991,20(9):2775-2790.
[110]Lee J M, Yoo C K, Choi S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[111]Lee JM, Yoo C K, Lee IB. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers and Chemical Engineering,2004,28(9): 1837-1840.
[112]Pearson K. On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine,1901, Series B(2):559-572.
[113]Hotelling H. Multivariate quality control illustrated by the testing of sample bombsights[M]. In O. Eisenhart (Ed.), Selected techniques of statistical analysis, New York:McGraw-Hill,1947, pp:113-184.
[114]Kresta J V, MacGregor J F, Marlin T E. Multivariate statistical monitoring of processes[J]. Canadian Journal of Chemical Engineering,1991,69(1):35-47.
[115]Nomikos P, MacGregor J. Monitoring batch processes using multiway principal component analysis[J]. American Institute of Chemical Engineers Journal,1994,40(8): 1361-1375.
[116]Qin S J, McAvoy T J. Nonlinear PLS modeling using neural networks[J]. Computers and Chemical Engineering,1992,16(4):379-391.
[117]Qin S. Recursive PLS algorithm for adaptive data monitoring[J]. Computers and Chemical Engineering,1998,22(4/5):503-514.
[118]Bakshi B. Multiscale PCA with application to multivariate statistical process monitoring[J]. American Institute of Chemical Engineers Journal,1998,44(7): 1596-1610.
[119]王俊国,闽松,赵金,万淑芸.基于模糊Petri网的故障诊断方法[J].自动检测技术,2002,21(6)：18-21.
[120]杨蓓,侯西梅,艾艳.专家系统中的模糊知识表示及推理研究[J].郑州大学学报,2004,36(2)：31-33.
[121]王奉涛,马孝江,邹岩琨.智能故障诊断技术综述[J].机床与液压,2003(4)：6-8.
[122]Bocanialad, Costab J S D. Application of a novel fuzzy classifier to fault detection and isolation of the DAMADICS benchmark problem Cosmin[J]. Control Engineering Practice,2006(14):653-669.
[123]禹柳飞,张友斌.基于模糊逻辑的旋转机组故障诊断方法[J].微计算机信息,2008,24(9)：225-227.
[124]苏鹏声,王欢.电力系统设备状态监测与故障诊断技术分析[J].电力系统自动化,2003,27(1)：61-65.
[125]梁武科,张彦宁,罗兴铸,水电机组故障诊断系统信号特征的提取,大电机技术,2003.No4,53-56
[126]潘罗平等,大型水电机组状态监测与诊断检修系统总体规划报告[R],中国水利水电科学研究,2008.
[127]DL/T 11972012水轮发电机组状态在线监测系统技术条件.国家能源局发布,2012.
[128]GB/T 28570-2012水轮发电机组状态在线监测系统技术导则.中华人民共和国国家质量监督检验检疫总局发布,2012.
[129]白恺,白亚民.发电机定子绕组端部机械振动模态的测量[J].华北电力技术,2001(8)：5-10.
[130]张润时,贺建华,郑松远.发电机空气间隙在线监测技术在三峡右岸电站的应用[J].水电站机电技术,2008(06)：11-16.
[131]王伯雄.测试技术基础[M].北京,清华大学出版社,2003：103-115.
[132]杨桂元,李天胜,徐军.数学模型应用实例[M].合肥,合肥工业大学出版社,2007：200-211.
[133]王承双.3σ准则与测量次数n的关系[J].长沙电力学院学报,1996(2)：73-74.
[134]齐民友.概率论与数理统计[M].北京,高等教育出版社,2004：171-183.
[135]GB/T 4091-2001常规控制图.国家质量技术监督局发布,2001.
[136]Tipping M E, Bishop C M. Probabilistic principal component analysis[J]. Journal of the Royal Statistical Society, SeriesB,1999,61(3):611-622.
[137]李晗,萧德云.基于数据驱动的故障诊断方法综述[M].控制与决策,2011,26(1)：2-9.
[138]Kaneko H, Arakawa M, Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. American Institute Chemical Engineers Journal,2009,55(1):87-98.
[139]赵旭.基于统计学方法的过程监控与质量控制研究[D].上海交通大学,2007.3.
[140]GB/T7894-2009水轮发电机基本技术条件.中华人民共和国国家质量监督检验检疫总局发布,2009.
[141]IEC60034-1 Rotating electrical machines Part1:Rating and performance. International Electrotechnical Commission(IEC),2010.
[142]SL321-2005大中型水轮发电机基本技术条件.中华人民共和国水利部发布,2005.
[143]丰田利夫,顾力刚.设备劣化倾向管理[R].中国设备管理,2000.8-2000.11.
[144]GB/T 6075.5-2002在非旋转部件上测量和评价机器的机械振动第五部分：水力发电厂和泵站机组.中华人民共和国国家质量监督检验检疫总局发布,2002.
[145]GB/T 15468-2006水轮机基本技术条件.中华人民共和国国家质量监督检验检疫总局发布,2006.
[146]GB/T 8564-2003水轮发电机组安装技术规范.中华人民共和国国家质量监督检验检疫总局发布,2003.
[147]谢宝义.工程机械状态监测与故障诊断技术[M].中国设备工程,2002：8-9
[148]吕林涛,王鹏,李军怀等.基于时间序列的趋势性分析及其预测算法研究[J].计算 机工程与应用.2004.19：173.
[149]宋仙磊,刘业政,陈思凤.基于周期项方法选择的季节性时序预测[J].计算机工程,2011,37(21)：132-135.
[150]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备,2005,25(8)：32-34.
[151]V. Tran, H. Pham, B. Yang, T. Nguyen. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing,2012,32:320-330.
[152]Pan Y, Chen J, Li X. Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means[J]. Mechanical Systems and Signal Processing,2010,24:559-566.
[153]安学利,潘罗平,张飞.基于三维曲面的抽水蓄能电站机组故障预警模型[J].水力发电,2013,39(1)：71～74.
[154]安学利,潘罗平,桂中华,周叶.抽水蓄能电站机组异常状态检测模型研究[J].水电能源科学,2013,31(1)：157-160.
[155]周淙,孙威,任海军,等.基于最小二乘支持向量机和负荷密度指标法的配电网空间负荷预测[J].电网技术,2011,35(1)：66-71.
[156]Xueli An, Dongxiang Jiang, Chao Liu, etc. Wind farm power prediction based on wavelet decomposition and chaotic time series[J]. Expert Systems with Applications, 2011,38(9):11280-11285.
[157]Cao L. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D,1997,110:43-50.
[158]潘罗平,周叶,安学利.水电站主设备状态集中监测与诊断系统的研发与应用[C].第十八次中国水电设备学术讨论会论文集,2011.10：380-385.
[159]潘家才,纪浩.智能电站建设的展望及思考[C].水电开发与经济社会可持续发展论坛论文集,2011：5-9.
© 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 |