挖掘机液压系统故障诊断方法研究
|
摘要
随着挖掘机自动化水平的提高,液压系统的故障诊断已经成为现代挖掘机的关键技术之一,开展挖掘机液压系统故障诊断方法的研究,对于提高挖掘机的可靠性水平和施工效率具有重要的意义。本文以理论研究、仿真建模和实验分析为基础,对挖掘机液压系统故障检测和故障诊断方法进行了系统的研究,主要内容包括如下几个方面:
1.研究了液压系统的故障模式和故障机理,分析了挖掘机液压系统关键液压元件模块化特点,研究了关键液压元件模型参数与故障模式和故障机理之间的对应关系;分析了挖掘机液压系统故障诊断研究中的几个关键问题,提出了挖掘机液压系统故障诊断研究策略。
2.研究了动态PCA方法和动态主元模型,建立了挖掘机液压系统的基本回路建立了动态主元模型;通过研究多元统计量检验,提出了基于动态PCA的挖掘机液压系统的故障检测方法;通过分析挖掘机液压系统在实际使用中的特点,提出了在线建模方法和在线故障检测方法。
3.提出了一种模糊规则化的ARX模型,称之为FARX模型,并将之应用于挖掘机液压系统的故障诊断之中。
(1)研究了FARX模型非线性特征及其故障特征参数的提取过程,提出了基于FARX模型的挖掘机液压系统故障特征提取方法。
(2)提出了基于FARX模型与FCM的挖掘机液压系统的故障诊断方法。该方法以目标故障特征为分类参考,使用FCM分类器对故障特征进行分类,判断系统的故障状态。
(3)提出了基于FARX模型与RBF网络的挖掘机液压系统故障诊断方法。该方法使用目标故障特征训练RBF网络,建立RBF网络故障分类器;故障特征代入故障分类器所得到的输出即为故障诊断结果。
4.将动态GRNN模型和多模型故障诊断相结合应用于挖掘机液压系统的故障诊断。
(1)在GRNN模型中引入全局递归的反馈机制,提出了动态GRNN模型;研究了动态GRNN模型的基本结构和动态GRNN模型的多步预测方法。
(2)结合动态GRNN模型与残差平方和检验,提出了基于动态GRNN模型的挖掘机液压系统故障检测方法;研究了多模型故障诊断,在故障检测方法的基础上,提出了基于多网络模型的挖掘机液压系统故障诊断方法。
5.在AMESim系统仿真环境下,建立了SWE50型挖掘机工作装置及其液压系统的实体参数模型,设置了多种模拟故障;在SWE50型挖掘机实验平台上,设置了活塞磨损、阀芯运动不到位、阀芯磨损、球头松动、配流盘磨损等5类单一故障以及阀芯磨损+阀芯运动不到位、阀芯磨损+活塞磨损、球头松动+配流盘磨损等3类复合故障。采集了故障数据样本;仿真和实验验证结果表明:上述故障检测和故障诊断方法均能有效地应用于挖掘机液压系统。
With the automation progress of the excavator, fault diagnosis of hydraulic system has become one of the key technologies for the modern excavator. The development of such techniques has great significance in improving reliability and working efficiency of the excavator. Based on theory research, simulation modeling and experiment, fault detection and diagnosis approaches for the excavator's hydraulic system are systematically studied in this paper. The main contents are as follows:
1. On the basis of study on the fault mode and the fault mechanism of the hydraulic system, model parameters of the modular hydraulic components of the excavator are properly corresponded to the specific fault mode and the fault mechanism. As a result, a fault diagnosis research strategy of the excavator's hydraulic system is proposed, which is taken as the guidance of fault diagnosis research, simulation testing and experiment testing.
2. With the study of the dynamic PCA, a dynamic principal component model of the hydraulic subsystem loop of the excavator is established. Combined with multivariate statistical testing, a fault detection approach of the excavator's hydraulic system based on dynamic PCA is proposed. In addition, taking account into the practical application, an online modeling method and an online fault detection method are proposed.
3. A fuzzified ARX model, called FARX model, is proposed for the fault diagnosis of the excavator's hydraulic system.
(1) With the study of the FARX model structure and corresponding fault feature extraction, a fault feature extraction approach of the excavator's hydraulic system based on FARX model is proposed.
(2) A diagnosis approach based on FARX model and FCM is proposed. On the basis of target fault features, FCM is served as fault classifier and the output of the FCM is the result of diagnosis.
(3) A fault diagnosis approach of the excavator's hydraulic system based on FARX model and RBF network is proposed. Before diagnosis, the RBF network is firstly trained with target fault features and a fault classifier is obtained. The output of this fault classifier is the result of diagnosis.
4. The dynamic GRNN model with multi-model diagnosis is applied to the excavator's hydraulic system.
(1) A dynamic GRNN model is proposed by introducing the global feedback to the GRNN. The structure of dynamic GRNN model and a corresponding multi-step prediction method are studied.
(2) A fault detection approach of the excavator's hydraulic based on dynamic GRNN model is proposed, in which the sum of residuals' square is developed to test model's residual. Incorporating with multi-model diagnosis, a diagnosis approach based on dynamic GRNN model is proposed for the excavator's hydraulic system.
5. For testing the diagnosis approaches, a simulation model of the SWE50 manipulator and corresponding hydraulic system are established in AMESim simulation environment. Various fault cases are simulated. In addition, with the SWE50 experimental excavator, sample data is generated from five single fault cases including piston wear, spool strake, spool wear, loose slipper and port plate wear, and three multiple fault cases including spool wear plus spool stroke, spool wear plus piston wear and loose slipper plus port plate wear. The detection and diagnosis approaches are tested with sample data from simulation and experiment. The results show that the fault detection and diagnosis approaches could effectively applied to the excavator's hydraulic system.
1.研究了液压系统的故障模式和故障机理,分析了挖掘机液压系统关键液压元件模块化特点,研究了关键液压元件模型参数与故障模式和故障机理之间的对应关系;分析了挖掘机液压系统故障诊断研究中的几个关键问题,提出了挖掘机液压系统故障诊断研究策略。
2.研究了动态PCA方法和动态主元模型,建立了挖掘机液压系统的基本回路建立了动态主元模型;通过研究多元统计量检验,提出了基于动态PCA的挖掘机液压系统的故障检测方法;通过分析挖掘机液压系统在实际使用中的特点,提出了在线建模方法和在线故障检测方法。
3.提出了一种模糊规则化的ARX模型,称之为FARX模型,并将之应用于挖掘机液压系统的故障诊断之中。
(1)研究了FARX模型非线性特征及其故障特征参数的提取过程,提出了基于FARX模型的挖掘机液压系统故障特征提取方法。
(2)提出了基于FARX模型与FCM的挖掘机液压系统的故障诊断方法。该方法以目标故障特征为分类参考,使用FCM分类器对故障特征进行分类,判断系统的故障状态。
(3)提出了基于FARX模型与RBF网络的挖掘机液压系统故障诊断方法。该方法使用目标故障特征训练RBF网络,建立RBF网络故障分类器;故障特征代入故障分类器所得到的输出即为故障诊断结果。
4.将动态GRNN模型和多模型故障诊断相结合应用于挖掘机液压系统的故障诊断。
(1)在GRNN模型中引入全局递归的反馈机制,提出了动态GRNN模型;研究了动态GRNN模型的基本结构和动态GRNN模型的多步预测方法。
(2)结合动态GRNN模型与残差平方和检验,提出了基于动态GRNN模型的挖掘机液压系统故障检测方法;研究了多模型故障诊断,在故障检测方法的基础上,提出了基于多网络模型的挖掘机液压系统故障诊断方法。
5.在AMESim系统仿真环境下,建立了SWE50型挖掘机工作装置及其液压系统的实体参数模型,设置了多种模拟故障;在SWE50型挖掘机实验平台上,设置了活塞磨损、阀芯运动不到位、阀芯磨损、球头松动、配流盘磨损等5类单一故障以及阀芯磨损+阀芯运动不到位、阀芯磨损+活塞磨损、球头松动+配流盘磨损等3类复合故障。采集了故障数据样本;仿真和实验验证结果表明:上述故障检测和故障诊断方法均能有效地应用于挖掘机液压系统。
With the automation progress of the excavator, fault diagnosis of hydraulic system has become one of the key technologies for the modern excavator. The development of such techniques has great significance in improving reliability and working efficiency of the excavator. Based on theory research, simulation modeling and experiment, fault detection and diagnosis approaches for the excavator's hydraulic system are systematically studied in this paper. The main contents are as follows:
1. On the basis of study on the fault mode and the fault mechanism of the hydraulic system, model parameters of the modular hydraulic components of the excavator are properly corresponded to the specific fault mode and the fault mechanism. As a result, a fault diagnosis research strategy of the excavator's hydraulic system is proposed, which is taken as the guidance of fault diagnosis research, simulation testing and experiment testing.
2. With the study of the dynamic PCA, a dynamic principal component model of the hydraulic subsystem loop of the excavator is established. Combined with multivariate statistical testing, a fault detection approach of the excavator's hydraulic system based on dynamic PCA is proposed. In addition, taking account into the practical application, an online modeling method and an online fault detection method are proposed.
3. A fuzzified ARX model, called FARX model, is proposed for the fault diagnosis of the excavator's hydraulic system.
(1) With the study of the FARX model structure and corresponding fault feature extraction, a fault feature extraction approach of the excavator's hydraulic system based on FARX model is proposed.
(2) A diagnosis approach based on FARX model and FCM is proposed. On the basis of target fault features, FCM is served as fault classifier and the output of the FCM is the result of diagnosis.
(3) A fault diagnosis approach of the excavator's hydraulic system based on FARX model and RBF network is proposed. Before diagnosis, the RBF network is firstly trained with target fault features and a fault classifier is obtained. The output of this fault classifier is the result of diagnosis.
4. The dynamic GRNN model with multi-model diagnosis is applied to the excavator's hydraulic system.
(1) A dynamic GRNN model is proposed by introducing the global feedback to the GRNN. The structure of dynamic GRNN model and a corresponding multi-step prediction method are studied.
(2) A fault detection approach of the excavator's hydraulic based on dynamic GRNN model is proposed, in which the sum of residuals' square is developed to test model's residual. Incorporating with multi-model diagnosis, a diagnosis approach based on dynamic GRNN model is proposed for the excavator's hydraulic system.
5. For testing the diagnosis approaches, a simulation model of the SWE50 manipulator and corresponding hydraulic system are established in AMESim simulation environment. Various fault cases are simulated. In addition, with the SWE50 experimental excavator, sample data is generated from five single fault cases including piston wear, spool strake, spool wear, loose slipper and port plate wear, and three multiple fault cases including spool wear plus spool stroke, spool wear plus piston wear and loose slipper plus port plate wear. The detection and diagnosis approaches are tested with sample data from simulation and experiment. The results show that the fault detection and diagnosis approaches could effectively applied to the excavator's hydraulic system.
引文
[1]张铁.液压挖掘机结构、原理及使用.东营:石油大学出版社,2002.
[2]林晓磊.挖掘工操作技术要领图解.济南:山东科学技术出版社,2006.
[3]朱湘冀.机械铲式挖掘机机器人化的探讨.机器人,1995,17(5):316-320.
[4]Pyung Hun Chang,Soo-Jin Lee.A straight-line motion tracking control of hydraulic excavator system.Mechatronics,2002,12(1):119-138.
[5]Sung-Uk Lee,Pyung Hun Chang.Control of a heavy-duty robotic excavator using time delay control with integral sliding surface.Control Engineering Practice,2002,10(7):697-711.
[6]Masakazu Haga,Watanabe Hiroshi,Kazuo Fujishima.Digging Control System for Hydraulic Excavator.Mechatronics,2001,11:665-676.
[7]A.S.Hall,P.R.McAree.Robust bucket position tracking for a large hydraulic excavator.Mechanism and Machine Theory,2005,40(1):1-16.
[8]于国飞,宋文荣,许纯新.挖掘装置操作臂末端轨迹控制的试验研究.中国机械工程,2003,14(17):1448-1451.
[9]吕广明,孙立宁,薛渊.神经网络在液压挖掘机工装轨迹控制中的应用.机械工程学报,2005,41(5):119-122.
[10]丁国富,吴晓,王金诺.机器人化液压挖掘机的运动学建模和仿真研究.机械科学与技术,2000,19(5):736-737.
[11]张海涛,何清华,张新海,等.机器人液压挖掘机运动系统的建模与控制.机器人,2005,27(2):113-117.
[12]何清华,张大庆,郝鹏,等.液压挖掘机工作装置模型及控制的试验研究.中南大学学报(自然科学版),2006,37(3):542-546.
[13]易孟林,曹树平,邹占江,等.电液复合控制节能原理的研究.中国机械工程,1998,9(4):13-14.
[14]郭晓方,李伟哲,黄宗益.液压挖掘机的发动机-泵的联合控制系统研究.同济大学学报(自然科学版),1999,27(3):333-336.
[15]王庆丰.大惯性负载电液进出口节流独立调节系统研究.中国机械工程,1999,10(8):853-855.
[16]彭天好,杨华勇,傅新.液压挖掘机全局功率匹配与协调控制.机械工程学报,2001,37(11):50-53.
[17]高峰,高宇,冯培恩.挖掘机载荷自适应节能控制策略.同济大学学报(自然科学版),2001,29(9):1036-1040.
[18]金立生,赵丁选,丁德胜.液压挖掘机节能参数自适应模糊PID控制器研究.农业工程学报,2003,19(6):87-90.
[19]国香恩,赵丁选,尚涛,等.模糊控制在液压挖掘机节能中的应用.吉林大学学报(工学版),2004,34(2):217-221.
[20]常绿,王国强,韩云武.液压挖掘机自动控制系统的设计和实现.农业工程学报,2007,23(6):140-144.
[21]朱建新,赵崇友,胡火焰.铲斗振动掘削岩土分析与岩土固有频率的估测方法.中南大学学报(自然科学版),2007,38(3):507-511.
[22]朱建新,赵崇友,郭鑫.液压挖掘机振动掘削减阻机理研究.岩土力学,2007,28(8):1065-1068.
[23]疏松桂.控制系统可靠性分析与综合.北京:科学出版社,1992.
[24]周东华.控制系统的故障检测与诊断技术.北京:清华大学出版社,1994.
[25]胡昌华.控制故障诊断和容错控制及在飞行可控制中的应用研究:[博士学位论文].西安:西北工业大学.1996.
[26]张育林,李东旭.动态系统故障诊断理论与应用.长沙:国防科技大学出版社,1997.
[27]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000.
[28]Sriyananda H.,Towill D.R.FAULT DIAGNOSIS USING TIME DOMAIN MEASUREMENTS.Radio and Electronic Engineer,1973,43(9):523-533.
[29]Abbott Kathy H.,Schutte Paul C.,Palmer,Michael T.et al.FAULTFINDER:A DIAGNOSTIC EXPERT SYSTEM WITH GRACEFUL DEGRADATION FOR ONBOARD AIRCRAFT APPLICATIONS.Mitteilung-Deutsche Forschungs- und Versuchsanstalt fuer Luft- und Raumfahrt,1988: 353-370.
[30]Hogan Paul,Burrows Clifford R.Development of a knowledge based system for the diagnosis of faults in hydraulic circuits.American Society of Mechanical Engineers,1990.
[31]Watton J.,Lucca-Negro O.,Stewart J.C.On-line approach to fault diagnosis of fluid power cylinder drive systems.Proceedings of the Institution of Mechanical Engineers.Part Ⅰ Journal of Systems & Control Engineering,1994,208(14):249-262.
[32]Yu D.,Shields D.N.Bilinear fault detection observer.Automatica,1996,32(11):1597-1602
[33]Crowther W.J.,Edge K.A.,Burrows C.R.et al.Fault diagnosis of a hydraulic actuator circuit using neural networks - an output vector space classification approach.Proceedings of the Institution of Mechanical Engineers.Part Ⅰ Journal of Systems & Control Engineering,1998,212(1):57-68
[34]Pollmeier K.,Burrows,C.R.,Edge K.A.Condition monitoring of an electrohydraulic position control system using artificial neural networks.American Society of Mechanical Engineers,The Fluid Power and Systems Technology Division,Proceedings of the ASME Fluid Power Systems and Technology Division,2004,11:137-146.
[35]陈章位,路甬祥.液压设备状态监测和故障诊断技术.液压与气动,1995,2:3-7.
[36]董选明,裘丽华,王占林.故障诊断神经网络系统的专家知识表达方法.北京航空航天大学学报,1996,22(1):27-32.
[37]张建华,王占林.基于模糊神经网络的故障诊断方法的研究.北京航空航天大学学报,1997,23(4):502-506.
[38]方志宏,傅周东,陈章位.基于BP神经网络的液压系统泄漏故障诊断.机械科学与技术,1998,17(4):636-638.
[39]潘宏,傅周东,陈章位.基于小波分析的液压系统泄漏检测.机械科学与技术,1998,17(1):116-118.
[40]姜万录,王益群,孔祥东,等.液压系统故障检测与诊断技术的新进展.中国机械工程,1998,9(9):58-60.
[41]许益民.基于两级BP网络伺服阀故障诊断专家系统的研究与应 用.中国机械工程,1999,10(5):545-548.
[42]高英杰.轧机AGC液压系统故障诊断技术的研究:[博士学位论文].秦皇岛:燕山大学.2000.
[43]张若青,裘丽华.基于动态神经网络的液压伺服系统故障检测.机械工程学报,2002,38(3):46-49.
[44]黄志坚,裘丽华.液压故障智能诊断逻辑方法的研究.中国机械工程,2002,13(15):1298-1301.
[45]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.中国机械工程,2004,15(9):760-763.
[46]湛从昌,李芳,付连东,等.液压故障的模糊诊断原则与方法.中国机械工程,2004,15(22):1983-1985.
[47]周汝胜,焦宗夏,王少萍.液压系统故障诊断技术的研究现状与发展趋势.机械工程学报,2006,42(9):6-14.
[48]倪善生.挖掘机液压系统故障诊断.建筑机械,1994,7:38-42.
[49]王华,何福忠,冯培恩.液压挖掘机的故障查找策略.建筑机械,1994,8:41-43.
[50]李力.挖掘机液压系统故障诊断专家系统.工程机械与维修,1996,4:18-19.
[51]Vachkov Gancho.Recognition of different operating states in complex systems by use of growing neural models.Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems,EFS'06,2006:80-85.
[52]苏启棠.挖掘机液压系统故障信号的采集、传输与处理的研究.矿山机械,2006,12:56-58.
[53]钱锦武.液压挖掘机液压系统的故障分析与诊断.筑路机械与施工机械化,2007,6:47-49.
[54]刘永健,胡培金.液压故障诊断分析.北京:人民交通出版社,2005.
[55]中国机械工程学会.机械设计手册(第五篇).北京:机械工业出版社,1991.
[56]李永堂,雷步芳,高雨茁.液压系统建模与仿真.北京:冶金工业出版社,2003.
[57]蔡廷文.液压系统现代建模方法.北京:中国标准出版社,2002.
[58]李洪人.液压控制系统.北京:国防工业出版社,1990.
[59]刘振航.数学建模.中国人民大学出版社,2004:73-84.
[60]谢磊,张建明,王树青.基于统计信号重构的传感器故障诊断.化工学报,2006,57(10):2343-2348.
[61]何清波.多元统计分析在设备状态监测诊断中的应用研究:[博士学位论文].杭州:浙江大学.2007.
[62]郭明.基于数据驱动的流程工业性能监控与故障诊断研究:[博士学位论文].杭州:浙江大学.2004.
[63]谢磊.问歇过程统计性能监控研究:[博士学位论文].杭州:浙江大学.2005.
[64]Junghui Chen,Kun-Chih Liu.On-line batch process monitoring using dynamic PCA and dynamic PLS models.Chemical Engineering Science,2002,57(1):63-75.
[65]Shengwei Wang,Fu Xiao.AHU sensor fault diagnosis using principal component analysis method,Energy and Buildings,2004,36(2):147-160.
[66]陈国金,梁军,刘育明,等.基于多元统计投影方法的过程监控技术研究.浙江大学学报(工学版),2004,38(12):1561-1565.
[67]Shengwei Wang,Fu Xiao.Detection and diagnosis of AHU sensor faults using principal component analysis method.Energy Conversion and Management,2004,45(17):2667-2686.
[68]Leo H.Chiang,Richard D.Braatz.Process monitoring using causal map and multivariate statistics:fault detection and identification.Chemometrics and Intelligent Laboratory Systems,2003,65(2):Pages 159-178.
[69]Jin Wang,S.Joe Qin.A new subspace identification approach based on principal component analysis.Journal of Process Control,2002,12(8):841-855.
[70]冯俊婷,王桂增,徐銤.基于主元分析的核电站主冷却剂泵故障诊断.原子能科学技术,2003,37(5):395-399.
[71]Ricardo Dunia,S.Joe Qin,Thomas F.Edgar,et al.Use of principal component analysis for sensor fault identification.Computers & Chemical Engineering,1996,20(S1):S713-S718.
[72]F.Jia,E.B.Martin,A.J.Morris.Non-linear principal components analysis for process fault detection.Computers & Chemical Engineering,1998,22(S1):S851-S854.
[73]Chen J H,Liu K C.On-line batch process monitoring using dynamic PCA and dynamic PLS models.Chemical Engineering Science,2002,57(1):63-75.
[74]Wenfu Ku,Robert H.Storer,Christos Georgakis.Disturbance detection and isolation by dynamic principal component analysis.Chemometrics and Intelligent Laboratory Systems,1995,30(1):179-196.
[75]Weilu Lin,Yu Qian,Xiuxi Li.Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis.Computers and Chemical Engineering,2000,4:423-429.
[76]Weihua Li,S.Joe Qin.Consistent dynamic PCA based on errors-in-variables subspace identification.Journal of Process Control,2001,11(6):661-678.
[77]Jong-Min Lee,Chang-Kyoo Yoo,In-Beum Lee.On-line batch process monitoring using a consecutively updated multiway principal component analysis model.Computers & Chemical Engineering,2003,27(12):1903-1912.
[78]D.Wang,J.A.Romagnoli.Robust multi-scale principal components analysis with applications to process monitoring.Journal of Process Control,2005,15(8):869-882.
[79]牛征.基于多元统计分析的火电厂控制系统故障诊断研究:[博士学位论文].华北电力大学(河北).2006.
[80]Leo H.Chiang,Riccardo Leardi,Randy J.Pell,et al.Industrial experiences with multivariate statistical analysis of batch process data.Chemometrics and Intelligent Laboratory Systems,2006,81(2):109-119.
[81]Rongyu LI,Gang RONG.Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process.Chinese Journal of Chemical Engineering,2006,14(4):486-493.
[82]何清华,张大庆,郝鹏,张新海.液压挖掘机工作装置仿真研究.系统仿真学报,2006,18(3):735-738.
[83]Wilfrid Marquis-Favre,Eric Bideaux,Serge Scavarda.A planar mechanical library in the AMESim simulation software.Part Ⅰ:Formulation of dynamics equations.Simulation Modelling Practice and Theory,2006,14(1):25-46.
[84]于德介,陈淼峰,程圣军,等.一种基于经验模式分解与支持向量机的转子故障诊断方法.中国电机工程学报,2006,26(16):162-167.
[85]D.Zogg,E.Shafai,H.P.Geering.Fault diagnosis for heatpumps with parameter identification and clustering.Control Engineering Practice,2006,14(12):1435-1444.
[86]Timoyhy J.Ross(美).模糊逻辑及其工程应用.钱同惠,沈其聪,葛晓滨等译.北京:电子工业出版社,2001.
[87]赵振宇.模糊理论和神经网络的基础与应用.北京:清华大学出版社,1996.
[88]Lennart Ljung.System Identification:Theory for the User(2nd Edition).Tsinghua University Press,Prentice Hall PTR,2002.
[89]杨叔子,吴雅.时间序列分析的工程应用.武汉:华中理工大学出版社,1991.
[90]蒋浩天,E.L.拉塞尔,R.D.布拉茨(美).工业系统的故障检测与诊断.段建明译.北京:机械工业出版社,2003.
[91]Haojun Sun,Shengrui Wang,Qingshan Jiang.FCM-Based Model Selection Algorithms for Determining the Number of Clusters.Pattern Recognition,2004,37(10):2027-2037.
[92]潘玉娜,韩捷,李志农.旋转机械诊断中的矢功率谱.模糊C均值聚类方法.汽轮机技术,2006,48(3):212-214.
[93]Z.Hilal inan,Mehmet Kuntalp.A study on fuzzy C-means clustering-based systems in automatic spike detection.Computers in Biology and Medicine,2007,37(8):1160-1166.
[94]Francisco de A.T,de Carvalho.Fuzzy c-means clustering methods for symbolic intervaldata.Pattern Recognition Letters,2007,28(4):423-437.
[95]Gleb Beliakov,Matthew King.Density based fuzzy c-means clustering of non-convex patterns.European Journal of Operational Research,2006,173(3):717-728.
[96]Wing W.Y.,Andres Dorado,Daniel S.Yeung,et al.Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error.Pattern Recognition,2007,40(1):19-32.
[97]Elisa Ricci,Renzo Perfetti.Improved pruning strategy for radial basis function networks with dynamic decay adjustment.Neurocomputing,2006,69(13):1728-1732.
[98]Haralambos Sarimveis,Philip Doganis,Alex Alexandridis.A classification technique based on radial basis function neural networks.Advances in Engineering Soflware,2006,37(4):218-221.
[99]Simon Haykin(美).神经网络原理.叶世伟,史忠植译.北京:机械工业出版社,2004.
[100]Specht D F.A general regression neural network.IEEE Transactions on Neural Networks,1991,2(6):568-576.
[101]Hikmet Kerem Cigizoglua,Murat Alpb.Generalized regression neural network in modelling river sediment yield.Advances in Engineering Software 2006,37:63-68
[102]Won-Yong Lee,John M.House,Nam-Ho Kyong.Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks.Applied Energy,2004,77(12):153-170.
[103]冯志鹏,宋希庚,薛冬新.基于广义回归神经网络的时问序列预测研究.振动、测试与诊断,2002,2:105-109.
[104]廖志伟,叶青华,王钢.基于GRNN的多故障自适应电力系统故障诊断.华南理工大学学报(自然科学版),2005,33(9):6-9.
[105]Abdullatif E.Ben-Nakhi,Mohamed A.Mahmoud.Cooling load prediction for buildings using general regression neural networks.Energy Conversion and Management,2004,45:2127-2141.
[106]James V.Hansen.Learning experiments with genetic optimization of a generalized regression neural network.Decision Support Systems,1996,18(3):317-325.
[107]Teodor Marcu,Birgit Kfppen-Seliger,Reinhard St-richer.Design of fault detection for a hydraulic looper using dynamic neural networks.Control Engineering Practice,2008,16(2):192-213.
[108]James J.Govindhasamy,Se(?)n F.McLoone,George W.Irwin,et al.Neural modelling,control and optimisation of an industrial grinding process.Control Engineering Practice,2005,13(10):1243-1258
[109]Elias N.Skoundrianos,Skyros G.Tzafestas.Finding fault.Fault diagnosis on the wheels of a mobile robot using local model neural networks.IEEE Robotics & Automation Magazine,2004,9:83-90.
[110]C.X.Wong,K.Worden.Generalised NARX shunting neural network modelling of friction.Mechanical Systems and Signal Processing,2007,21(1):553-572.
[111]Jong Min Lee,Jay H.Lee.Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes.Automatica,2005,41(7):1281-1288
[112]Skoundrianos E N,Tzafestas S G.Finding fault.Fault diagnosis on the wheels of a mobile robot using local model neural networks.IEEE Robotics &Automation Magazine,2004,9:83-90.
[113]王永骥,徐桂英.连续搅拌反应釜(CSTR)的神经网络动态建模.控制与决策,2000,15(3):375-377.
[114]翟军勇.基于多模型切换的智能控制研究:[博士学位论文].南京:东南大学,2006.
[115]Wang Xin,Li Shaoyuan,Cai Wenjian.Multi-model direct adaptive decoupling control with application to the wind tunnel system.ISA Transactions,2005,44(1):131-143.
[116]Stoica Petre,Selen Yngve,Li,Jian.Multi-model approach to model selection.Digital Signal Processing:A Review Journal,2004,14(5):399-412.
[117]Cutululis N.A.,Ceanga E.,Hansen A.D,et al.Robust multi-model control of an autonomous wind power system.Wind Energy,2006,9(5):99-419.
[118]刘小雄.飞行控制系统故障隔离与自适应重构技术研究:[博士学位论文].西安:西北工业大学,2006.
[119]Drake K J,Campbell S L,Andjelkovic I.Naval Model-based failure detection on nonlinear systems:Theory and transition.Engineers Journal,2006,119(2):93-106.
[120]Nikoukhah R,Campbell S L.Auxiliary signal design for active failure detection in uncertain linear systems with a priori information.Automatica,2006,42(2):219-228.
[121]Nikoukhah R,Campbell S L.A multi-model approach to failure detection in uncertain sampled-data systems.European Journal of Control,2005,11(3):255-265.
[2]林晓磊.挖掘工操作技术要领图解.济南:山东科学技术出版社,2006.
[3]朱湘冀.机械铲式挖掘机机器人化的探讨.机器人,1995,17(5):316-320.
[4]Pyung Hun Chang,Soo-Jin Lee.A straight-line motion tracking control of hydraulic excavator system.Mechatronics,2002,12(1):119-138.
[5]Sung-Uk Lee,Pyung Hun Chang.Control of a heavy-duty robotic excavator using time delay control with integral sliding surface.Control Engineering Practice,2002,10(7):697-711.
[6]Masakazu Haga,Watanabe Hiroshi,Kazuo Fujishima.Digging Control System for Hydraulic Excavator.Mechatronics,2001,11:665-676.
[7]A.S.Hall,P.R.McAree.Robust bucket position tracking for a large hydraulic excavator.Mechanism and Machine Theory,2005,40(1):1-16.
[8]于国飞,宋文荣,许纯新.挖掘装置操作臂末端轨迹控制的试验研究.中国机械工程,2003,14(17):1448-1451.
[9]吕广明,孙立宁,薛渊.神经网络在液压挖掘机工装轨迹控制中的应用.机械工程学报,2005,41(5):119-122.
[10]丁国富,吴晓,王金诺.机器人化液压挖掘机的运动学建模和仿真研究.机械科学与技术,2000,19(5):736-737.
[11]张海涛,何清华,张新海,等.机器人液压挖掘机运动系统的建模与控制.机器人,2005,27(2):113-117.
[12]何清华,张大庆,郝鹏,等.液压挖掘机工作装置模型及控制的试验研究.中南大学学报(自然科学版),2006,37(3):542-546.
[13]易孟林,曹树平,邹占江,等.电液复合控制节能原理的研究.中国机械工程,1998,9(4):13-14.
[14]郭晓方,李伟哲,黄宗益.液压挖掘机的发动机-泵的联合控制系统研究.同济大学学报(自然科学版),1999,27(3):333-336.
[15]王庆丰.大惯性负载电液进出口节流独立调节系统研究.中国机械工程,1999,10(8):853-855.
[16]彭天好,杨华勇,傅新.液压挖掘机全局功率匹配与协调控制.机械工程学报,2001,37(11):50-53.
[17]高峰,高宇,冯培恩.挖掘机载荷自适应节能控制策略.同济大学学报(自然科学版),2001,29(9):1036-1040.
[18]金立生,赵丁选,丁德胜.液压挖掘机节能参数自适应模糊PID控制器研究.农业工程学报,2003,19(6):87-90.
[19]国香恩,赵丁选,尚涛,等.模糊控制在液压挖掘机节能中的应用.吉林大学学报(工学版),2004,34(2):217-221.
[20]常绿,王国强,韩云武.液压挖掘机自动控制系统的设计和实现.农业工程学报,2007,23(6):140-144.
[21]朱建新,赵崇友,胡火焰.铲斗振动掘削岩土分析与岩土固有频率的估测方法.中南大学学报(自然科学版),2007,38(3):507-511.
[22]朱建新,赵崇友,郭鑫.液压挖掘机振动掘削减阻机理研究.岩土力学,2007,28(8):1065-1068.
[23]疏松桂.控制系统可靠性分析与综合.北京:科学出版社,1992.
[24]周东华.控制系统的故障检测与诊断技术.北京:清华大学出版社,1994.
[25]胡昌华.控制故障诊断和容错控制及在飞行可控制中的应用研究:[博士学位论文].西安:西北工业大学.1996.
[26]张育林,李东旭.动态系统故障诊断理论与应用.长沙:国防科技大学出版社,1997.
[27]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000.
[28]Sriyananda H.,Towill D.R.FAULT DIAGNOSIS USING TIME DOMAIN MEASUREMENTS.Radio and Electronic Engineer,1973,43(9):523-533.
[29]Abbott Kathy H.,Schutte Paul C.,Palmer,Michael T.et al.FAULTFINDER:A DIAGNOSTIC EXPERT SYSTEM WITH GRACEFUL DEGRADATION FOR ONBOARD AIRCRAFT APPLICATIONS.Mitteilung-Deutsche Forschungs- und Versuchsanstalt fuer Luft- und Raumfahrt,1988: 353-370.
[30]Hogan Paul,Burrows Clifford R.Development of a knowledge based system for the diagnosis of faults in hydraulic circuits.American Society of Mechanical Engineers,1990.
[31]Watton J.,Lucca-Negro O.,Stewart J.C.On-line approach to fault diagnosis of fluid power cylinder drive systems.Proceedings of the Institution of Mechanical Engineers.Part Ⅰ Journal of Systems & Control Engineering,1994,208(14):249-262.
[32]Yu D.,Shields D.N.Bilinear fault detection observer.Automatica,1996,32(11):1597-1602
[33]Crowther W.J.,Edge K.A.,Burrows C.R.et al.Fault diagnosis of a hydraulic actuator circuit using neural networks - an output vector space classification approach.Proceedings of the Institution of Mechanical Engineers.Part Ⅰ Journal of Systems & Control Engineering,1998,212(1):57-68
[34]Pollmeier K.,Burrows,C.R.,Edge K.A.Condition monitoring of an electrohydraulic position control system using artificial neural networks.American Society of Mechanical Engineers,The Fluid Power and Systems Technology Division,Proceedings of the ASME Fluid Power Systems and Technology Division,2004,11:137-146.
[35]陈章位,路甬祥.液压设备状态监测和故障诊断技术.液压与气动,1995,2:3-7.
[36]董选明,裘丽华,王占林.故障诊断神经网络系统的专家知识表达方法.北京航空航天大学学报,1996,22(1):27-32.
[37]张建华,王占林.基于模糊神经网络的故障诊断方法的研究.北京航空航天大学学报,1997,23(4):502-506.
[38]方志宏,傅周东,陈章位.基于BP神经网络的液压系统泄漏故障诊断.机械科学与技术,1998,17(4):636-638.
[39]潘宏,傅周东,陈章位.基于小波分析的液压系统泄漏检测.机械科学与技术,1998,17(1):116-118.
[40]姜万录,王益群,孔祥东,等.液压系统故障检测与诊断技术的新进展.中国机械工程,1998,9(9):58-60.
[41]许益民.基于两级BP网络伺服阀故障诊断专家系统的研究与应 用.中国机械工程,1999,10(5):545-548.
[42]高英杰.轧机AGC液压系统故障诊断技术的研究:[博士学位论文].秦皇岛:燕山大学.2000.
[43]张若青,裘丽华.基于动态神经网络的液压伺服系统故障检测.机械工程学报,2002,38(3):46-49.
[44]黄志坚,裘丽华.液压故障智能诊断逻辑方法的研究.中国机械工程,2002,13(15):1298-1301.
[45]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.中国机械工程,2004,15(9):760-763.
[46]湛从昌,李芳,付连东,等.液压故障的模糊诊断原则与方法.中国机械工程,2004,15(22):1983-1985.
[47]周汝胜,焦宗夏,王少萍.液压系统故障诊断技术的研究现状与发展趋势.机械工程学报,2006,42(9):6-14.
[48]倪善生.挖掘机液压系统故障诊断.建筑机械,1994,7:38-42.
[49]王华,何福忠,冯培恩.液压挖掘机的故障查找策略.建筑机械,1994,8:41-43.
[50]李力.挖掘机液压系统故障诊断专家系统.工程机械与维修,1996,4:18-19.
[51]Vachkov Gancho.Recognition of different operating states in complex systems by use of growing neural models.Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems,EFS'06,2006:80-85.
[52]苏启棠.挖掘机液压系统故障信号的采集、传输与处理的研究.矿山机械,2006,12:56-58.
[53]钱锦武.液压挖掘机液压系统的故障分析与诊断.筑路机械与施工机械化,2007,6:47-49.
[54]刘永健,胡培金.液压故障诊断分析.北京:人民交通出版社,2005.
[55]中国机械工程学会.机械设计手册(第五篇).北京:机械工业出版社,1991.
[56]李永堂,雷步芳,高雨茁.液压系统建模与仿真.北京:冶金工业出版社,2003.
[57]蔡廷文.液压系统现代建模方法.北京:中国标准出版社,2002.
[58]李洪人.液压控制系统.北京:国防工业出版社,1990.
[59]刘振航.数学建模.中国人民大学出版社,2004:73-84.
[60]谢磊,张建明,王树青.基于统计信号重构的传感器故障诊断.化工学报,2006,57(10):2343-2348.
[61]何清波.多元统计分析在设备状态监测诊断中的应用研究:[博士学位论文].杭州:浙江大学.2007.
[62]郭明.基于数据驱动的流程工业性能监控与故障诊断研究:[博士学位论文].杭州:浙江大学.2004.
[63]谢磊.问歇过程统计性能监控研究:[博士学位论文].杭州:浙江大学.2005.
[64]Junghui Chen,Kun-Chih Liu.On-line batch process monitoring using dynamic PCA and dynamic PLS models.Chemical Engineering Science,2002,57(1):63-75.
[65]Shengwei Wang,Fu Xiao.AHU sensor fault diagnosis using principal component analysis method,Energy and Buildings,2004,36(2):147-160.
[66]陈国金,梁军,刘育明,等.基于多元统计投影方法的过程监控技术研究.浙江大学学报(工学版),2004,38(12):1561-1565.
[67]Shengwei Wang,Fu Xiao.Detection and diagnosis of AHU sensor faults using principal component analysis method.Energy Conversion and Management,2004,45(17):2667-2686.
[68]Leo H.Chiang,Richard D.Braatz.Process monitoring using causal map and multivariate statistics:fault detection and identification.Chemometrics and Intelligent Laboratory Systems,2003,65(2):Pages 159-178.
[69]Jin Wang,S.Joe Qin.A new subspace identification approach based on principal component analysis.Journal of Process Control,2002,12(8):841-855.
[70]冯俊婷,王桂增,徐銤.基于主元分析的核电站主冷却剂泵故障诊断.原子能科学技术,2003,37(5):395-399.
[71]Ricardo Dunia,S.Joe Qin,Thomas F.Edgar,et al.Use of principal component analysis for sensor fault identification.Computers & Chemical Engineering,1996,20(S1):S713-S718.
[72]F.Jia,E.B.Martin,A.J.Morris.Non-linear principal components analysis for process fault detection.Computers & Chemical Engineering,1998,22(S1):S851-S854.
[73]Chen J H,Liu K C.On-line batch process monitoring using dynamic PCA and dynamic PLS models.Chemical Engineering Science,2002,57(1):63-75.
[74]Wenfu Ku,Robert H.Storer,Christos Georgakis.Disturbance detection and isolation by dynamic principal component analysis.Chemometrics and Intelligent Laboratory Systems,1995,30(1):179-196.
[75]Weilu Lin,Yu Qian,Xiuxi Li.Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis.Computers and Chemical Engineering,2000,4:423-429.
[76]Weihua Li,S.Joe Qin.Consistent dynamic PCA based on errors-in-variables subspace identification.Journal of Process Control,2001,11(6):661-678.
[77]Jong-Min Lee,Chang-Kyoo Yoo,In-Beum Lee.On-line batch process monitoring using a consecutively updated multiway principal component analysis model.Computers & Chemical Engineering,2003,27(12):1903-1912.
[78]D.Wang,J.A.Romagnoli.Robust multi-scale principal components analysis with applications to process monitoring.Journal of Process Control,2005,15(8):869-882.
[79]牛征.基于多元统计分析的火电厂控制系统故障诊断研究:[博士学位论文].华北电力大学(河北).2006.
[80]Leo H.Chiang,Riccardo Leardi,Randy J.Pell,et al.Industrial experiences with multivariate statistical analysis of batch process data.Chemometrics and Intelligent Laboratory Systems,2006,81(2):109-119.
[81]Rongyu LI,Gang RONG.Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process.Chinese Journal of Chemical Engineering,2006,14(4):486-493.
[82]何清华,张大庆,郝鹏,张新海.液压挖掘机工作装置仿真研究.系统仿真学报,2006,18(3):735-738.
[83]Wilfrid Marquis-Favre,Eric Bideaux,Serge Scavarda.A planar mechanical library in the AMESim simulation software.Part Ⅰ:Formulation of dynamics equations.Simulation Modelling Practice and Theory,2006,14(1):25-46.
[84]于德介,陈淼峰,程圣军,等.一种基于经验模式分解与支持向量机的转子故障诊断方法.中国电机工程学报,2006,26(16):162-167.
[85]D.Zogg,E.Shafai,H.P.Geering.Fault diagnosis for heatpumps with parameter identification and clustering.Control Engineering Practice,2006,14(12):1435-1444.
[86]Timoyhy J.Ross(美).模糊逻辑及其工程应用.钱同惠,沈其聪,葛晓滨等译.北京:电子工业出版社,2001.
[87]赵振宇.模糊理论和神经网络的基础与应用.北京:清华大学出版社,1996.
[88]Lennart Ljung.System Identification:Theory for the User(2nd Edition).Tsinghua University Press,Prentice Hall PTR,2002.
[89]杨叔子,吴雅.时间序列分析的工程应用.武汉:华中理工大学出版社,1991.
[90]蒋浩天,E.L.拉塞尔,R.D.布拉茨(美).工业系统的故障检测与诊断.段建明译.北京:机械工业出版社,2003.
[91]Haojun Sun,Shengrui Wang,Qingshan Jiang.FCM-Based Model Selection Algorithms for Determining the Number of Clusters.Pattern Recognition,2004,37(10):2027-2037.
[92]潘玉娜,韩捷,李志农.旋转机械诊断中的矢功率谱.模糊C均值聚类方法.汽轮机技术,2006,48(3):212-214.
[93]Z.Hilal inan,Mehmet Kuntalp.A study on fuzzy C-means clustering-based systems in automatic spike detection.Computers in Biology and Medicine,2007,37(8):1160-1166.
[94]Francisco de A.T,de Carvalho.Fuzzy c-means clustering methods for symbolic intervaldata.Pattern Recognition Letters,2007,28(4):423-437.
[95]Gleb Beliakov,Matthew King.Density based fuzzy c-means clustering of non-convex patterns.European Journal of Operational Research,2006,173(3):717-728.
[96]Wing W.Y.,Andres Dorado,Daniel S.Yeung,et al.Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error.Pattern Recognition,2007,40(1):19-32.
[97]Elisa Ricci,Renzo Perfetti.Improved pruning strategy for radial basis function networks with dynamic decay adjustment.Neurocomputing,2006,69(13):1728-1732.
[98]Haralambos Sarimveis,Philip Doganis,Alex Alexandridis.A classification technique based on radial basis function neural networks.Advances in Engineering Soflware,2006,37(4):218-221.
[99]Simon Haykin(美).神经网络原理.叶世伟,史忠植译.北京:机械工业出版社,2004.
[100]Specht D F.A general regression neural network.IEEE Transactions on Neural Networks,1991,2(6):568-576.
[101]Hikmet Kerem Cigizoglua,Murat Alpb.Generalized regression neural network in modelling river sediment yield.Advances in Engineering Software 2006,37:63-68
[102]Won-Yong Lee,John M.House,Nam-Ho Kyong.Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks.Applied Energy,2004,77(12):153-170.
[103]冯志鹏,宋希庚,薛冬新.基于广义回归神经网络的时问序列预测研究.振动、测试与诊断,2002,2:105-109.
[104]廖志伟,叶青华,王钢.基于GRNN的多故障自适应电力系统故障诊断.华南理工大学学报(自然科学版),2005,33(9):6-9.
[105]Abdullatif E.Ben-Nakhi,Mohamed A.Mahmoud.Cooling load prediction for buildings using general regression neural networks.Energy Conversion and Management,2004,45:2127-2141.
[106]James V.Hansen.Learning experiments with genetic optimization of a generalized regression neural network.Decision Support Systems,1996,18(3):317-325.
[107]Teodor Marcu,Birgit Kfppen-Seliger,Reinhard St-richer.Design of fault detection for a hydraulic looper using dynamic neural networks.Control Engineering Practice,2008,16(2):192-213.
[108]James J.Govindhasamy,Se(?)n F.McLoone,George W.Irwin,et al.Neural modelling,control and optimisation of an industrial grinding process.Control Engineering Practice,2005,13(10):1243-1258
[109]Elias N.Skoundrianos,Skyros G.Tzafestas.Finding fault.Fault diagnosis on the wheels of a mobile robot using local model neural networks.IEEE Robotics & Automation Magazine,2004,9:83-90.
[110]C.X.Wong,K.Worden.Generalised NARX shunting neural network modelling of friction.Mechanical Systems and Signal Processing,2007,21(1):553-572.
[111]Jong Min Lee,Jay H.Lee.Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes.Automatica,2005,41(7):1281-1288
[112]Skoundrianos E N,Tzafestas S G.Finding fault.Fault diagnosis on the wheels of a mobile robot using local model neural networks.IEEE Robotics &Automation Magazine,2004,9:83-90.
[113]王永骥,徐桂英.连续搅拌反应釜(CSTR)的神经网络动态建模.控制与决策,2000,15(3):375-377.
[114]翟军勇.基于多模型切换的智能控制研究:[博士学位论文].南京:东南大学,2006.
[115]Wang Xin,Li Shaoyuan,Cai Wenjian.Multi-model direct adaptive decoupling control with application to the wind tunnel system.ISA Transactions,2005,44(1):131-143.
[116]Stoica Petre,Selen Yngve,Li,Jian.Multi-model approach to model selection.Digital Signal Processing:A Review Journal,2004,14(5):399-412.
[117]Cutululis N.A.,Ceanga E.,Hansen A.D,et al.Robust multi-model control of an autonomous wind power system.Wind Energy,2006,9(5):99-419.
[118]刘小雄.飞行控制系统故障隔离与自适应重构技术研究:[博士学位论文].西安:西北工业大学,2006.
[119]Drake K J,Campbell S L,Andjelkovic I.Naval Model-based failure detection on nonlinear systems:Theory and transition.Engineers Journal,2006,119(2):93-106.
[120]Nikoukhah R,Campbell S L.Auxiliary signal design for active failure detection in uncertain linear systems with a priori information.Automatica,2006,42(2):219-228.
[121]Nikoukhah R,Campbell S L.A multi-model approach to failure detection in uncertain sampled-data systems.European Journal of Control,2005,11(3):255-265.