电机转子检测方法及故障诊断技术研究
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摘要
电机运行的正常与否直接影响着工矿企业以及日常生活的正常运转。如果电机质量不高,频繁出现故障,不仅会影响生产的运行,还会影响经济的快速发展,造成严重的经济损失和恶劣的社会影响。电机转子是电机中的运动部件,在运行过程中受到轴向电磁力、离心力和热弯曲扰度力等的作用,因此在电机的运行过程中转子故障是主要的故障。本文从几个方面入手,研究了电机转子在制造以及使用过程中的检测方法及故障诊断技术,本文的研究成果一方面可以为提高电机出厂质量,延长电机的使用寿命,另一方面可以分析电机的运行状态,在故障的前期进行预警,防止因突发故障给生产生活带来巨大损失。
转子制作过程受到诸多因素的影响,其中线圈抽头至换向片的虚焊问题尤为严重且较为普遍,由此引发的质量隐患通常在电机的生产过程中难以发现,更多的表现在装机出厂交付使用后随时可能发生的局部发热、断线等故障导致的电机烧毁。本文在建立电机转子电路模型及数学模型的基础上,研究了电机转子片间电阻、跨间电阻、焊接电阻的测量及计算方法,设计了电机转子直流电阻测量电路,设计了电机转子诊断智能测试系统。电机出厂之前对转子进行全面的电气性能检测与分析,可以防止不良产品流入市场,提高电机出厂质量,延长电机的使用寿命。
电机转子绕组匝间短路故障是电机常见故障之一,本文在分析电机转子绕组匝间短路故障机理的基础上,设计了基于DSP的高速数据处理系统,采集了气隙电动势信号。利用小波变换,去除该信号中的主磁通干扰信号,分别将正常信号和故障信号进行4层小波包变换,提取重构后的16个能量特征信息并进行归一化处理,作为神经网络的输入向量,为建立人工智能的故障检测提供数据支持,应用小波包分解提取后的能量信息值作为神经网络的输入向量,故障类型作为网络的输出向量,建立了电机故障诊断系统的诊断过程,通过分别利用不同的数据进行网络训练和测试,实现了电机转子绕组匝间短路的故障诊断。
转子断条故障即转子导条断裂是电动机常见故障之一。本文在分析电动机转子断条故障的诊断机理基础上,使用小波包分析电机转子断条仿真信号,确定断条的故障特征信号,结合FFT变换对重构的结点系数进行频谱分析,得到故障信号的特征频率,然后运用小波分析确定电机转子断条故障发生的具体时刻,最后经过试验证明了该方法的可行性。
The quality of motor directly influence the competition position of motor manufacturing enterprise in market, but armature is the main component of motor, and its faults are the main faults in the process of motor running. If the motor is often damaged and destructed, the system will not work in order and get in trouble. It will also affect the rapid economic development, causing serious economic losses and adverse social impact. Rotor is the moving parts of the motor, motor often suffered axial electromagnetic force, centrifugal force and hot bending flexibility in the running process, so the rotor fault is the major fault. The detection method and fault diagnosis technology of rotor is researched by several aspects in this article; the research results can improve the quality of the motor and extend the life of the motor. The motor can be analyzed operating state in the pre-fault early warning.
Rotors are subjected many factors in manufacturing process, including the coil tap to the conversion to film a particularly serious problem, which is uually caused quality problems in the production process and it is difficult to find. This testing system could experimentize and determinate to the basic electrical performance of motor armature, for instance, welding resistance, bar to bar resistance, leap resistance, industrial frequency withstand voltage, inter-turn withstand voltage, dc voltage withstand and so on. The paper designs the rotor DC resistance measuring circuit and diagnostic intelligence test rotor system. Rotor will have a full electrical performance test before the motor factory and prevent defective products from entering the market; it will improve the quality of the motor factory and extend the motor life.
The rotor winding inter turn short circuit fault is one of the common faults, which is of destruction to the machines. The paper analyze the rotor winding inter turn short circuit fault mechanism and design high speed data processing system based on DSP. The system can collect the air gap EMF signal. Wavelet transform can remove the main magnetic flux interference signals from the air gap EMF signal, the normal and fault signals have 4 layers wavelet packet transform to extract the reconstructed energy feature information. After the wavelet packet transform, we get 16 energy feature information as the neural network the input vector for the establishment of artificial intelligence to provide data to support fault detection. Wavelet packet decomposition of the energy information extracted value as neural network input vector, fault type as the network output vector, and then establish a motor fault diagnosis system. In the diagnostic process, different data were used to train and test the network to achieve the rotor winding inter turn short circuit fault diagnosis.
Rotor fault analysis, that is the motor rotor bar fault, is one of the common faults. This paper analyzes broken bar of the motor fault diagnosis mechanism, which is based on wavelet packet analysis. Use the wavelet packet to simulate of the signal rotor broken bars and determine the fault feature information.Use the nodes of FFT transform coefficients to reconstruct the spectrum analysis, then we can get the characteristic frequency of fault signal. Use wavelet analysis to determine fault specific time with broken bar of induction motor. The simulated experimental results show that the diagnosis method, a quicker diagnosis and a higher accuracy, is feasible.
转子制作过程受到诸多因素的影响,其中线圈抽头至换向片的虚焊问题尤为严重且较为普遍,由此引发的质量隐患通常在电机的生产过程中难以发现,更多的表现在装机出厂交付使用后随时可能发生的局部发热、断线等故障导致的电机烧毁。本文在建立电机转子电路模型及数学模型的基础上,研究了电机转子片间电阻、跨间电阻、焊接电阻的测量及计算方法,设计了电机转子直流电阻测量电路,设计了电机转子诊断智能测试系统。电机出厂之前对转子进行全面的电气性能检测与分析,可以防止不良产品流入市场,提高电机出厂质量,延长电机的使用寿命。
电机转子绕组匝间短路故障是电机常见故障之一,本文在分析电机转子绕组匝间短路故障机理的基础上,设计了基于DSP的高速数据处理系统,采集了气隙电动势信号。利用小波变换,去除该信号中的主磁通干扰信号,分别将正常信号和故障信号进行4层小波包变换,提取重构后的16个能量特征信息并进行归一化处理,作为神经网络的输入向量,为建立人工智能的故障检测提供数据支持,应用小波包分解提取后的能量信息值作为神经网络的输入向量,故障类型作为网络的输出向量,建立了电机故障诊断系统的诊断过程,通过分别利用不同的数据进行网络训练和测试,实现了电机转子绕组匝间短路的故障诊断。
转子断条故障即转子导条断裂是电动机常见故障之一。本文在分析电动机转子断条故障的诊断机理基础上,使用小波包分析电机转子断条仿真信号,确定断条的故障特征信号,结合FFT变换对重构的结点系数进行频谱分析,得到故障信号的特征频率,然后运用小波分析确定电机转子断条故障发生的具体时刻,最后经过试验证明了该方法的可行性。
The quality of motor directly influence the competition position of motor manufacturing enterprise in market, but armature is the main component of motor, and its faults are the main faults in the process of motor running. If the motor is often damaged and destructed, the system will not work in order and get in trouble. It will also affect the rapid economic development, causing serious economic losses and adverse social impact. Rotor is the moving parts of the motor, motor often suffered axial electromagnetic force, centrifugal force and hot bending flexibility in the running process, so the rotor fault is the major fault. The detection method and fault diagnosis technology of rotor is researched by several aspects in this article; the research results can improve the quality of the motor and extend the life of the motor. The motor can be analyzed operating state in the pre-fault early warning.
Rotors are subjected many factors in manufacturing process, including the coil tap to the conversion to film a particularly serious problem, which is uually caused quality problems in the production process and it is difficult to find. This testing system could experimentize and determinate to the basic electrical performance of motor armature, for instance, welding resistance, bar to bar resistance, leap resistance, industrial frequency withstand voltage, inter-turn withstand voltage, dc voltage withstand and so on. The paper designs the rotor DC resistance measuring circuit and diagnostic intelligence test rotor system. Rotor will have a full electrical performance test before the motor factory and prevent defective products from entering the market; it will improve the quality of the motor factory and extend the motor life.
The rotor winding inter turn short circuit fault is one of the common faults, which is of destruction to the machines. The paper analyze the rotor winding inter turn short circuit fault mechanism and design high speed data processing system based on DSP. The system can collect the air gap EMF signal. Wavelet transform can remove the main magnetic flux interference signals from the air gap EMF signal, the normal and fault signals have 4 layers wavelet packet transform to extract the reconstructed energy feature information. After the wavelet packet transform, we get 16 energy feature information as the neural network the input vector for the establishment of artificial intelligence to provide data to support fault detection. Wavelet packet decomposition of the energy information extracted value as neural network input vector, fault type as the network output vector, and then establish a motor fault diagnosis system. In the diagnostic process, different data were used to train and test the network to achieve the rotor winding inter turn short circuit fault diagnosis.
Rotor fault analysis, that is the motor rotor bar fault, is one of the common faults. This paper analyzes broken bar of the motor fault diagnosis mechanism, which is based on wavelet packet analysis. Use the wavelet packet to simulate of the signal rotor broken bars and determine the fault feature information.Use the nodes of FFT transform coefficients to reconstruct the spectrum analysis, then we can get the characteristic frequency of fault signal. Use wavelet analysis to determine fault specific time with broken bar of induction motor. The simulated experimental results show that the diagnosis method, a quicker diagnosis and a higher accuracy, is feasible.
引文
[1] W.T. THOMSON, M. FENGER. Case Histories of Current Signature Analysis to Detect Faults in Asynchronous motor Drives. IEEE International Conference on Electric Machines and Drives, Madison, USA, 2003. IEMDC'03 Wisconsin,2003,3:1459-1465
[2] P.J.达夫勒,J.彭曼.电机的状态监测.姜建国,史家燕译.北京:水利电力出版社,1992
[3]武建文,李德成.电动机现代测试技术.北京:机械工业出版社,2005
[4]王益全,张炳义.电动机测试技术.北京:科学出版社,2006
[5]侯志勇,李和明,许伯强,等.大型异步电动机状态监测与故障诊断系统的研制.山西电力, 2002, (1):58-60
[6]侯志勇.大型异步电动机状态监测与故障诊断系统研究.华北电力大学硕士学位论文. 2002:34-36
[7]刘小军.便携式电机故障诊断仪的研究与开发.西安建筑科技大学硕士学位论文. 2003:71-74
[8]李桂丹,王晓远,陈益广等.异步电动机转子故障在线检测方法.测试技术,2003,36(3):55-57
[9] W.S.TOMMY, S.H.CHOW. Induction Machine Fault Diagnostic Analysis with Wavelet Technique. IEEE Transactions on Industrial Electronics,2004,51(3):558-565
[10] J.CUSIDO, J.A.ROSERO, J.A.Ortega,et al. Asynchronous motor Fault Detection by using Wavelet Decomposition on dq0 Components. IEEE ISIE, Montreal, Quebec, Canada, 2006. IEEE International Symposium on Industrial Electronics,2006,3:2406-2411
[11] F.ZIDANI, M.E.H.BENBOUZID, D.DIALLO, et al. Asynchronous motor Stator Faults Diagnosis by a Current Concordia Pattern-based Fuzzy Decision System. IEEETransactions on Energy Conversion, 2003,18(4):469-475
[12] A.G.PARLOS, KYUSUNG KIM, R.BHARADWAJ. Detection of Asynchronous motor Faults-combining Signal-based and Model-based Techniques. American Control Conference, Anchorage, AK, 2002. Proceedings of the American Control Conference, 2002,6:4531-4536
[13] S.PREMRUDEEPREECHACHARN, T.UTTHIYOUNG,K.KRUEPENGKUL,et al. Asynchronous motor Fault Detection and Diagnosis using Supervised and Unsupervised Neural Networks. IEEE ICIT, Bangkok, THAILAND, 2002. IEEE International Conference on Industrial Technology, 2002,1:93-96
[14] A.BENDIABDELLAH, N.BENOUZZA,D.TOUMI.Cage Motor Faults Detection by Speed Estimation and Spectral Current Analysis. The 3rd IET International Conference on Power Electronics.Machines and Drives, 2006:47-51
[15] F. COSTA, L.A.L. DEALMEIDA,S.R.NAIDU, et al. Improving the Signal Data Acquisition in Condition Monitoring of Electrical Machines. Instrumentation and Measurement Technology Conference, Vail, CO, USA, 2003. IMTC '03.Proceedings of the 20th IEEE, 2003,1:562-566
[16]许伯强,李和明,孙丽玲等.笼型异步电动机转子断条故障检测新方法.中国电机工程学报,2004,24(5):115-119
[17]王震洲,刘教民,王丽娜.基于DSP的低压智能脱扣器.低压电器,2007,(13):51-54
[18]孙丽玲,许伯强,孙俊忠.笼型异步电动机转子断条故障新特征[J].电力系统自动化,2007,31(22):94-98.
[19] Cypress, CY7C68013A/CY7C68015A EZ-USB FX2LPTM USB Microconteroller High-Speed USB Peripheral Controller Cypress Semiconductor Corportaiont 3901 North First Street San Jose, CA 95134 408-943-2600 Revised June 8,2004
[20] T.WILLIAM THOMSON,MARK FENGER. Current Signature Analysis to Detect Asynchronous motor Faults. IEEE Industry Applications Magazine,2001:26-34
[21] J.ROBINSON, C.D.WHELAN, N.K.HAGGERTY. Trends in Advanced Motor Protection and Monitoring. IEEE-PCIC Paper, 2001:111-115
[22] B.KLIMANG,WJ.PREMERLANI, A.KOEGLR, et al. Sensitive, On-line Turn-to-turn Fault Detection in AC Motors. Electric Machines and Power Systems, 2000, 28:915-927
[23] FU-LEI CHU,WEN-XIU LU.Experimental Observation of Nonlinear Vibrationsin a Rub-impact Rotor System. Journal of Sound and Vibration. 2005,283:621~643
[24] Albright, D. R. Interturn short- circuit deteetor for turbine generator rotor windings.IEEE Trans. On Power Apparatus and System, 1971, 2: 478-483
[25] Ramirez-Nino, J, Paseaeio, A. Detecting interturn short circuit in rotor windings. IEEE Computer Applieations in Power, 2001, Vol.14No, 4:39-42
[26] B.KlimanG, WJ. Premerlani, A.KoeglR. Sensitive, On-line Turn-to-turn Fault Detection in AC Motors.Electric Machines and Power Systems, 2000, 28:915-927
[27]杨东凯,梁帆,凌桂龙.DSP嵌入式系统设计和开发指南.第1版.北京:中国电力出版社,2009.
[28]曹军军,陈小勤,吴超,等.基于USB2.0的数据采集卡的设计与实现.仪器仪表用户,2006,13(1):37-38
[29]王丽娜,王震洲,刘教民,等.数据采集卡中固件程序的开发.河北科技大学学报,2008,29 (1):40-42
[30]仝瑞普.基于Slave FIFO模式USB数据传输系统设计与实现.南京理工大学硕士学位论文,2008
[31]张华,王震洲,刘教民.数字信号处理TMS320F2812在智能脱扣器设计中的应用.河北科技大学学报,2006,27(2):165-168
[32]万章,基于DSP的多功能信息采集与智能监控系统的研究,上海交通大学硕士学位论文,2006
[33]葛哲学,沙威.小波分析理论与MATLAB R2007实现,北京:电子工业出版社,2007
[34]宋崇智,吴玉国,王璐,等.改进Elman网络在发动机齿轮箱故障诊断中的研究[J].仪器仪表学报, 2008, 29(7): 1414-1417.
[35]胡昌华,张军波,夏军,张伟.基于MATLAB的系统分析与设计—小波分析.西安电子科技大学出版社,1999
[36]张明照,刘政波,刘斌.应用MATLAB实现信号分析和处理.第1版.北京:科学出版社,2006.
[37] Jenkins, M.P. On-line monitoring of rotor shorted turns. Life Management of Power Plants, International Conference, Dec 1994:55-60.
[38] Penelope M. Sanderson, James M.Murtagh. Predicting Fault Diagnosis Performance: Why Are Some Bugs Hard to Find. IEEE Trans on Systems, MAN, and Cybernetics,1990,20(1):274-283.
[39] Wu Yucai, Li Yonggang, Wan Shu ting.Investigation of Turbine Generator Rotor Winding Inter-turn Short Circuit Fault Based on Harmonic Detection.The Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies[C]. Nanjing, China, 2008
[40] The MathWorks, Inc.MATLAB The Language of Technical Computing 2002
[41] Nandi S, To liyiat H A.Condition monitoring and fault diagnosis of electrical machines-A review. Conference Record IEEE-IAS. Annual Meeting, Phoenix, AZ, 1999.197-204.
[42]吴文全,叶晓慧.μ?级小电阻测量方法研究.电测与仪表, 2003, 40(455): 26-29
[43]刘志存.微小电阻测量方法及关键技术.物理测试,2005,23(1):34-36
[44]冯晓,刘仲恕.电机与电器控制.北京:机械工业出版社. 2005
[45]钱卫香.基于Windows平台电机故障诊断专家系统的研究. [东北大学硕士论文]. 2000
[46]周雅莉.基于人工智能的故障诊断方法的新发展.北京机械工业学院学报. 2003
[47]沈艳霞,纪志成,姜建国.电机故障诊断的人工智能方法综述.微特电机,国际标准刊号: ISSN 1004-7018
[48] BATUEV, V. N.. A Single-phase Commutator Motor for a New Generation of Electric Power Tools. Stroit Dorozhn Mash, 2003, (11): 31-36
[49] IEEE(1993)IEEE recommended practices and requirements for harmonic control in electrical power systems. Standarad, IEEE519.IEEE
[50] L. EMMET, P. FROOME. Requirements for a Guide on the Development of Virtual Instrument. Technique Report of National Physical Laboratory of UK, 1999
[51]易浩波.智能电机测试与控制系统的研究与开发. [湖南大学硕士论文]. 2003
[52]李维波,毛承雄,路继明,等.电力设备直流电阻测量用恒流源研究.电力自动化设备, 2003, 23(5): 63-66
[53]王宜怀. 12位A/D转换器TLC2543与51系列单片机接口技术.苏州丝绸工学院学报, 1999, 19(5): 45-50
[54]陈小玄,罗大庸,单勇腾.基于小波分析的转子绕组匝间短路故障诊断方法[J].自动化技术与应用.2007,26(2):85-89.
[55]李永刚,李和明,万书亭.发电机转子绕组匝间短路故障特征分析与识别[M].北京:中国电力出版社.2009.
[56]樊启斌.小波分析[M].武汉:武汉大学出版社,2008.
[57]葛哲学,沙威.小波分析理论与MATLABR2007实现[M].北京:电子工业出版社,2007.
[58]张毅,李夏青,范圣韬.基于VC和Matlab混合编程的牵引供电故障诊断系统[J].北京石油化工学院学报.2008, 16(3):21-25.
[59]张亮,王继阳.MATLAB与C/C++混合编程[M].北京:人民邮电出版社,2008.
[60] WOOD J W. Rotor winding short detection[C].IEE Proceedings-Pt.B, 1986,133(3):30-35.
[61]孙士慧.基于小波神经网络的设备故障诊断方法研究.[中国石油大学硕士学位论文].2008:12-15
[62]邓日青.基于神经网络专家系统的电喷发动机故障自诊断研究.[重庆交通大学硕士学位论文].2008:46-52
[63] Crinon R.J.Sinusoid parameter estimation using the fast Fourier transform[C].IEEE International Symposium on Circuits and Systems,1989
[64]蔡自兴,徐光佑.人工智能及其应用,北京:清华大学出版社,2003.9
[65] Deng X,Jokien T,Richie E.An improved knowledge based of fuzzy logic diagnosis system for an induction motor with rotor faults[C].Proc.of CICEM95,Hangzhou China 2000:901~905
[66] Cirrincione G.A Kohonen.Neural Networks for the Diagnosis of Incipient Faults in Induction Motors.ICEM.94:Paris,France,Sept.1994,Vol,2:369~373
[67] S. L. LIU, G. CHEN, M. S. YONG. EMC Characterization and Process Study for Electronics Packaging. Thin Solid Films, 2004:454-458
[68]赵红怡,武梦龙.小波分析在突变信号检测中的应用[J].北方工业大学学报,2004,6(1):21-24
[69] Ramirez-Nino,J. Pascacio,A.Detecting Interturn Short Circuits in Rotor Windings[J].IEEE Computer Applications in Power,2001,14(4):39-42
[70] Jingwen Tian,Meijuan Gao,Hao Zhou,Kai Li.The Fault Diagnosis System With Self-repair Function for Screw Oil Pump Base on Wavelet Neural Network[C].Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,Beijing,2007:699-704
[71] C.Kral, R.S.Wieser,F. Pirker,M.Sequences of field-oriented control for the detection of faulty rotor bars in induction machines-the Vienna Monitoring Method.IEEE Transactions on Industrial Electronics,2002,47(5):1042-1050
[72]陈小前.BP神经网络应用中的前后处理过程研究[J].系统工程理论与实践,2002,22(1):65-70
[73]张铃,张钹.人工神经网络理论及应用[M].杭州:浙江科学技术出版社,1997:1-10
[74] K.L.Butler,J.A.Momoh.A Neural Net Based Approach For Fault Diagnosis In Distribution Networks[J].IEEE,1998,1275–1278
[75]闻新,周露.MATLAB神经网络仿真与应用,北京:科学出版社,2003,7
[76]李显宏.MATLAB 7.x界面设计与编程技巧,北京:电子工业出版社,2006,12
[77]朱仁峰.精通MATLAB 7[M].北京:清华大学出版社,2006.5.
[78]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2005.45-52
[79] Dam M, Saraf D N. Design of neural networks using genetic algorithm for on-lineproperty estimation of data mining applications[J]. Computer and Chemical Engineering, 2006,30(4):722-729
[80] Zhang Q.H.A Wavelet Networks[J].IEEE Transactions on Neural Networks,2002,3(6):889~892
[81] GENG Zhongxing,Qu Liangsheng.Frequency shift algorithms of wavelet packet and noise signal analysis[J].Journal of Vibration Engineering 1996,9(2):145-152
[82] Jenkins, M.P. On-line monitoring of rotor shorted turns[C]. Life Management of Power Plants, International Conference , 1994:55-60
[83] G. DUCHAMP, D. CASTAGNET, A. MERESSE. Near-field EMC Study to Improve Electronic Component Reliability. Microelectronics Reliability, 2007, 47: 1668-1672
[84] Grandke T.Interpolating algorithms for discrete Fourier transforms of weighted signals[J].IEEE Trans.IM,1983,13(2):351-356
[85]孙垒.基于BP神经网路的模拟电脑故障诊断系统的FPGA设计与实现.[南京理工大学硕士学位论文].2009
[86]刘天祯.基于多维数据分析的神经网络与分布式计算研究.[武汉理工大学硕士学位论文].2008
[87]徐俩俩.基于神经网络的感应电机故障诊断研究.扬州大学硕士学位论文].2009
[88]郭庆军.基于小波包络解调与人工神经网络的轴承智能诊断技术研究.[西南交通大学硕士学位论文].2008
[89]侯新国,夏立,吴正国,等.基于小波和神经网络的异步电机转子故障诊断方法研究.数据采集与处理,2004,19(1)
[90]南晋华.决策神经网络模型及应用研究.[华中科技大学硕士学位论文].2008
[91] W.T. THOMSON, M. FENGER. Case Histories of Current Signature Analysis to Detect Faults in Asynchronous motor Drives. IEEE International Conference on Electric Machines and Drives, Madison, USA, 2003. IEMDC'03 Wisconsin,2003,3:1459-1465
[92] W.S.TOMMY, S.H.CHOW. Induction Machine Fault Diagnostic Analysis with Wavelet Technique. IEEE Transactions on Industrial Electronics,2004,51(3):558-565
[93] J.CUSIDO, J.A.ROSERO, J.A.Ortega,et al. Asynchronous motor Fault Detection by using Wavelet Decomposition on dq0 Components. IEEE ISIE, Montreal, Quebec, Canada, 2006. IEEE International Symposium on Industrial Electronics,2006,3:2406-2411
[94] F.ZIDANI, M.E.H.BENBOUZID, D.DIALLO, et al. Asynchronous motor Stator Faults Diagnosis by aCurrent Concordia Pattern-based Fuzzy Decision System. IEEETransactions on Energy Conversion, 2003,18(4):469-475
[95] A.G.PARLOS, KYUSUNG KIM, R.BHARADWAJ. Detection of Asynchronous motor Faults-combining Signal-based and Model-based Techniques. American Control Conference, Anchorage, AK, 2002. Proceedings of the American Control Conference, 2002,6:4531-4536
[96] S.PREMRUDEEPREECHACHARN,T.UTTHIYOUNG,K.KRUEPENGKUL,etal. Asynchronous motor Fault Detection and Diagnosis using Supervised and Unsupervised Neural Networks. IEEE ICIT, Bangkok, THAILAND, 2002. IEEE International Conference on Industrial Technology, 2002,1:93-96
[97] A.BENDIABDELLAH, N.BENOUZZA,D.TOUMI.Cage Motor Faults Detection by Speed Estimation and Spectral Current Analysis. The 3rd IET International Conference on Power Electronics.Machines and Drives, 2006:47-51
[98] F. COSTA, L.A.L. DEALMEIDA,S.R.NAIDU, et al. Improving the Signal Data Acquisition in Condition Monitoring of Electrical Machines. Instrumentation and Measurement Technology Conference, Vail, CO, USA, 2003. IMTC '03.Proceedings of the 20th IEEE, 2003,1:562-566
[99]张植保.电机原理与应用.北京:化学工业出版社,2006
[100]刘和平,王维俊,江渝,等.TMS320LF240x DSP C语言开发应用.北京:北京航空航天大学出版社,2002
[101] Deng X , Ritchie E , Jokinen T1An improved knowledgebase of a fuzzy logic system for an induction motor with rotor faults1Proceedings of CICEM’95 , Hangzhou , PRC ,Aug11995 , 901～905
[102] Cao Z , Ritchie E1Rotor fault diagnosis of induction motorbased on a dynamic associative memory of chaotic neural network1Proceedings of ICEM’2000 , Helsinki , Finland ,Sept.2000 , 863～867
[103]杨铁生.小波变换的工程分析与应用.北京∶科学出版社, 1999.
[104]刘贵忠,邸双亮.小波分析及其应用.西安∶西安电子科技大学出版社, 1997.
[2] P.J.达夫勒,J.彭曼.电机的状态监测.姜建国,史家燕译.北京:水利电力出版社,1992
[3]武建文,李德成.电动机现代测试技术.北京:机械工业出版社,2005
[4]王益全,张炳义.电动机测试技术.北京:科学出版社,2006
[5]侯志勇,李和明,许伯强,等.大型异步电动机状态监测与故障诊断系统的研制.山西电力, 2002, (1):58-60
[6]侯志勇.大型异步电动机状态监测与故障诊断系统研究.华北电力大学硕士学位论文. 2002:34-36
[7]刘小军.便携式电机故障诊断仪的研究与开发.西安建筑科技大学硕士学位论文. 2003:71-74
[8]李桂丹,王晓远,陈益广等.异步电动机转子故障在线检测方法.测试技术,2003,36(3):55-57
[9] W.S.TOMMY, S.H.CHOW. Induction Machine Fault Diagnostic Analysis with Wavelet Technique. IEEE Transactions on Industrial Electronics,2004,51(3):558-565
[10] J.CUSIDO, J.A.ROSERO, J.A.Ortega,et al. Asynchronous motor Fault Detection by using Wavelet Decomposition on dq0 Components. IEEE ISIE, Montreal, Quebec, Canada, 2006. IEEE International Symposium on Industrial Electronics,2006,3:2406-2411
[11] F.ZIDANI, M.E.H.BENBOUZID, D.DIALLO, et al. Asynchronous motor Stator Faults Diagnosis by a Current Concordia Pattern-based Fuzzy Decision System. IEEETransactions on Energy Conversion, 2003,18(4):469-475
[12] A.G.PARLOS, KYUSUNG KIM, R.BHARADWAJ. Detection of Asynchronous motor Faults-combining Signal-based and Model-based Techniques. American Control Conference, Anchorage, AK, 2002. Proceedings of the American Control Conference, 2002,6:4531-4536
[13] S.PREMRUDEEPREECHACHARN, T.UTTHIYOUNG,K.KRUEPENGKUL,et al. Asynchronous motor Fault Detection and Diagnosis using Supervised and Unsupervised Neural Networks. IEEE ICIT, Bangkok, THAILAND, 2002. IEEE International Conference on Industrial Technology, 2002,1:93-96
[14] A.BENDIABDELLAH, N.BENOUZZA,D.TOUMI.Cage Motor Faults Detection by Speed Estimation and Spectral Current Analysis. The 3rd IET International Conference on Power Electronics.Machines and Drives, 2006:47-51
[15] F. COSTA, L.A.L. DEALMEIDA,S.R.NAIDU, et al. Improving the Signal Data Acquisition in Condition Monitoring of Electrical Machines. Instrumentation and Measurement Technology Conference, Vail, CO, USA, 2003. IMTC '03.Proceedings of the 20th IEEE, 2003,1:562-566
[16]许伯强,李和明,孙丽玲等.笼型异步电动机转子断条故障检测新方法.中国电机工程学报,2004,24(5):115-119
[17]王震洲,刘教民,王丽娜.基于DSP的低压智能脱扣器.低压电器,2007,(13):51-54
[18]孙丽玲,许伯强,孙俊忠.笼型异步电动机转子断条故障新特征[J].电力系统自动化,2007,31(22):94-98.
[19] Cypress, CY7C68013A/CY7C68015A EZ-USB FX2LPTM USB Microconteroller High-Speed USB Peripheral Controller Cypress Semiconductor Corportaiont 3901 North First Street San Jose, CA 95134 408-943-2600 Revised June 8,2004
[20] T.WILLIAM THOMSON,MARK FENGER. Current Signature Analysis to Detect Asynchronous motor Faults. IEEE Industry Applications Magazine,2001:26-34
[21] J.ROBINSON, C.D.WHELAN, N.K.HAGGERTY. Trends in Advanced Motor Protection and Monitoring. IEEE-PCIC Paper, 2001:111-115
[22] B.KLIMANG,WJ.PREMERLANI, A.KOEGLR, et al. Sensitive, On-line Turn-to-turn Fault Detection in AC Motors. Electric Machines and Power Systems, 2000, 28:915-927
[23] FU-LEI CHU,WEN-XIU LU.Experimental Observation of Nonlinear Vibrationsin a Rub-impact Rotor System. Journal of Sound and Vibration. 2005,283:621~643
[24] Albright, D. R. Interturn short- circuit deteetor for turbine generator rotor windings.IEEE Trans. On Power Apparatus and System, 1971, 2: 478-483
[25] Ramirez-Nino, J, Paseaeio, A. Detecting interturn short circuit in rotor windings. IEEE Computer Applieations in Power, 2001, Vol.14No, 4:39-42
[26] B.KlimanG, WJ. Premerlani, A.KoeglR. Sensitive, On-line Turn-to-turn Fault Detection in AC Motors.Electric Machines and Power Systems, 2000, 28:915-927
[27]杨东凯,梁帆,凌桂龙.DSP嵌入式系统设计和开发指南.第1版.北京:中国电力出版社,2009.
[28]曹军军,陈小勤,吴超,等.基于USB2.0的数据采集卡的设计与实现.仪器仪表用户,2006,13(1):37-38
[29]王丽娜,王震洲,刘教民,等.数据采集卡中固件程序的开发.河北科技大学学报,2008,29 (1):40-42
[30]仝瑞普.基于Slave FIFO模式USB数据传输系统设计与实现.南京理工大学硕士学位论文,2008
[31]张华,王震洲,刘教民.数字信号处理TMS320F2812在智能脱扣器设计中的应用.河北科技大学学报,2006,27(2):165-168
[32]万章,基于DSP的多功能信息采集与智能监控系统的研究,上海交通大学硕士学位论文,2006
[33]葛哲学,沙威.小波分析理论与MATLAB R2007实现,北京:电子工业出版社,2007
[34]宋崇智,吴玉国,王璐,等.改进Elman网络在发动机齿轮箱故障诊断中的研究[J].仪器仪表学报, 2008, 29(7): 1414-1417.
[35]胡昌华,张军波,夏军,张伟.基于MATLAB的系统分析与设计—小波分析.西安电子科技大学出版社,1999
[36]张明照,刘政波,刘斌.应用MATLAB实现信号分析和处理.第1版.北京:科学出版社,2006.
[37] Jenkins, M.P. On-line monitoring of rotor shorted turns. Life Management of Power Plants, International Conference, Dec 1994:55-60.
[38] Penelope M. Sanderson, James M.Murtagh. Predicting Fault Diagnosis Performance: Why Are Some Bugs Hard to Find. IEEE Trans on Systems, MAN, and Cybernetics,1990,20(1):274-283.
[39] Wu Yucai, Li Yonggang, Wan Shu ting.Investigation of Turbine Generator Rotor Winding Inter-turn Short Circuit Fault Based on Harmonic Detection.The Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies[C]. Nanjing, China, 2008
[40] The MathWorks, Inc.MATLAB The Language of Technical Computing 2002
[41] Nandi S, To liyiat H A.Condition monitoring and fault diagnosis of electrical machines-A review. Conference Record IEEE-IAS. Annual Meeting, Phoenix, AZ, 1999.197-204.
[42]吴文全,叶晓慧.μ?级小电阻测量方法研究.电测与仪表, 2003, 40(455): 26-29
[43]刘志存.微小电阻测量方法及关键技术.物理测试,2005,23(1):34-36
[44]冯晓,刘仲恕.电机与电器控制.北京:机械工业出版社. 2005
[45]钱卫香.基于Windows平台电机故障诊断专家系统的研究. [东北大学硕士论文]. 2000
[46]周雅莉.基于人工智能的故障诊断方法的新发展.北京机械工业学院学报. 2003
[47]沈艳霞,纪志成,姜建国.电机故障诊断的人工智能方法综述.微特电机,国际标准刊号: ISSN 1004-7018
[48] BATUEV, V. N.. A Single-phase Commutator Motor for a New Generation of Electric Power Tools. Stroit Dorozhn Mash, 2003, (11): 31-36
[49] IEEE(1993)IEEE recommended practices and requirements for harmonic control in electrical power systems. Standarad, IEEE519.IEEE
[50] L. EMMET, P. FROOME. Requirements for a Guide on the Development of Virtual Instrument. Technique Report of National Physical Laboratory of UK, 1999
[51]易浩波.智能电机测试与控制系统的研究与开发. [湖南大学硕士论文]. 2003
[52]李维波,毛承雄,路继明,等.电力设备直流电阻测量用恒流源研究.电力自动化设备, 2003, 23(5): 63-66
[53]王宜怀. 12位A/D转换器TLC2543与51系列单片机接口技术.苏州丝绸工学院学报, 1999, 19(5): 45-50
[54]陈小玄,罗大庸,单勇腾.基于小波分析的转子绕组匝间短路故障诊断方法[J].自动化技术与应用.2007,26(2):85-89.
[55]李永刚,李和明,万书亭.发电机转子绕组匝间短路故障特征分析与识别[M].北京:中国电力出版社.2009.
[56]樊启斌.小波分析[M].武汉:武汉大学出版社,2008.
[57]葛哲学,沙威.小波分析理论与MATLABR2007实现[M].北京:电子工业出版社,2007.
[58]张毅,李夏青,范圣韬.基于VC和Matlab混合编程的牵引供电故障诊断系统[J].北京石油化工学院学报.2008, 16(3):21-25.
[59]张亮,王继阳.MATLAB与C/C++混合编程[M].北京:人民邮电出版社,2008.
[60] WOOD J W. Rotor winding short detection[C].IEE Proceedings-Pt.B, 1986,133(3):30-35.
[61]孙士慧.基于小波神经网络的设备故障诊断方法研究.[中国石油大学硕士学位论文].2008:12-15
[62]邓日青.基于神经网络专家系统的电喷发动机故障自诊断研究.[重庆交通大学硕士学位论文].2008:46-52
[63] Crinon R.J.Sinusoid parameter estimation using the fast Fourier transform[C].IEEE International Symposium on Circuits and Systems,1989
[64]蔡自兴,徐光佑.人工智能及其应用,北京:清华大学出版社,2003.9
[65] Deng X,Jokien T,Richie E.An improved knowledge based of fuzzy logic diagnosis system for an induction motor with rotor faults[C].Proc.of CICEM95,Hangzhou China 2000:901~905
[66] Cirrincione G.A Kohonen.Neural Networks for the Diagnosis of Incipient Faults in Induction Motors.ICEM.94:Paris,France,Sept.1994,Vol,2:369~373
[67] S. L. LIU, G. CHEN, M. S. YONG. EMC Characterization and Process Study for Electronics Packaging. Thin Solid Films, 2004:454-458
[68]赵红怡,武梦龙.小波分析在突变信号检测中的应用[J].北方工业大学学报,2004,6(1):21-24
[69] Ramirez-Nino,J. Pascacio,A.Detecting Interturn Short Circuits in Rotor Windings[J].IEEE Computer Applications in Power,2001,14(4):39-42
[70] Jingwen Tian,Meijuan Gao,Hao Zhou,Kai Li.The Fault Diagnosis System With Self-repair Function for Screw Oil Pump Base on Wavelet Neural Network[C].Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,Beijing,2007:699-704
[71] C.Kral, R.S.Wieser,F. Pirker,M.Sequences of field-oriented control for the detection of faulty rotor bars in induction machines-the Vienna Monitoring Method.IEEE Transactions on Industrial Electronics,2002,47(5):1042-1050
[72]陈小前.BP神经网络应用中的前后处理过程研究[J].系统工程理论与实践,2002,22(1):65-70
[73]张铃,张钹.人工神经网络理论及应用[M].杭州:浙江科学技术出版社,1997:1-10
[74] K.L.Butler,J.A.Momoh.A Neural Net Based Approach For Fault Diagnosis In Distribution Networks[J].IEEE,1998,1275–1278
[75]闻新,周露.MATLAB神经网络仿真与应用,北京:科学出版社,2003,7
[76]李显宏.MATLAB 7.x界面设计与编程技巧,北京:电子工业出版社,2006,12
[77]朱仁峰.精通MATLAB 7[M].北京:清华大学出版社,2006.5.
[78]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2005.45-52
[79] Dam M, Saraf D N. Design of neural networks using genetic algorithm for on-lineproperty estimation of data mining applications[J]. Computer and Chemical Engineering, 2006,30(4):722-729
[80] Zhang Q.H.A Wavelet Networks[J].IEEE Transactions on Neural Networks,2002,3(6):889~892
[81] GENG Zhongxing,Qu Liangsheng.Frequency shift algorithms of wavelet packet and noise signal analysis[J].Journal of Vibration Engineering 1996,9(2):145-152
[82] Jenkins, M.P. On-line monitoring of rotor shorted turns[C]. Life Management of Power Plants, International Conference , 1994:55-60
[83] G. DUCHAMP, D. CASTAGNET, A. MERESSE. Near-field EMC Study to Improve Electronic Component Reliability. Microelectronics Reliability, 2007, 47: 1668-1672
[84] Grandke T.Interpolating algorithms for discrete Fourier transforms of weighted signals[J].IEEE Trans.IM,1983,13(2):351-356
[85]孙垒.基于BP神经网路的模拟电脑故障诊断系统的FPGA设计与实现.[南京理工大学硕士学位论文].2009
[86]刘天祯.基于多维数据分析的神经网络与分布式计算研究.[武汉理工大学硕士学位论文].2008
[87]徐俩俩.基于神经网络的感应电机故障诊断研究.扬州大学硕士学位论文].2009
[88]郭庆军.基于小波包络解调与人工神经网络的轴承智能诊断技术研究.[西南交通大学硕士学位论文].2008
[89]侯新国,夏立,吴正国,等.基于小波和神经网络的异步电机转子故障诊断方法研究.数据采集与处理,2004,19(1)
[90]南晋华.决策神经网络模型及应用研究.[华中科技大学硕士学位论文].2008
[91] W.T. THOMSON, M. FENGER. Case Histories of Current Signature Analysis to Detect Faults in Asynchronous motor Drives. IEEE International Conference on Electric Machines and Drives, Madison, USA, 2003. IEMDC'03 Wisconsin,2003,3:1459-1465
[92] W.S.TOMMY, S.H.CHOW. Induction Machine Fault Diagnostic Analysis with Wavelet Technique. IEEE Transactions on Industrial Electronics,2004,51(3):558-565
[93] J.CUSIDO, J.A.ROSERO, J.A.Ortega,et al. Asynchronous motor Fault Detection by using Wavelet Decomposition on dq0 Components. IEEE ISIE, Montreal, Quebec, Canada, 2006. IEEE International Symposium on Industrial Electronics,2006,3:2406-2411
[94] F.ZIDANI, M.E.H.BENBOUZID, D.DIALLO, et al. Asynchronous motor Stator Faults Diagnosis by aCurrent Concordia Pattern-based Fuzzy Decision System. IEEETransactions on Energy Conversion, 2003,18(4):469-475
[95] A.G.PARLOS, KYUSUNG KIM, R.BHARADWAJ. Detection of Asynchronous motor Faults-combining Signal-based and Model-based Techniques. American Control Conference, Anchorage, AK, 2002. Proceedings of the American Control Conference, 2002,6:4531-4536
[96] S.PREMRUDEEPREECHACHARN,T.UTTHIYOUNG,K.KRUEPENGKUL,etal. Asynchronous motor Fault Detection and Diagnosis using Supervised and Unsupervised Neural Networks. IEEE ICIT, Bangkok, THAILAND, 2002. IEEE International Conference on Industrial Technology, 2002,1:93-96
[97] A.BENDIABDELLAH, N.BENOUZZA,D.TOUMI.Cage Motor Faults Detection by Speed Estimation and Spectral Current Analysis. The 3rd IET International Conference on Power Electronics.Machines and Drives, 2006:47-51
[98] F. COSTA, L.A.L. DEALMEIDA,S.R.NAIDU, et al. Improving the Signal Data Acquisition in Condition Monitoring of Electrical Machines. Instrumentation and Measurement Technology Conference, Vail, CO, USA, 2003. IMTC '03.Proceedings of the 20th IEEE, 2003,1:562-566
[99]张植保.电机原理与应用.北京:化学工业出版社,2006
[100]刘和平,王维俊,江渝,等.TMS320LF240x DSP C语言开发应用.北京:北京航空航天大学出版社,2002
[101] Deng X , Ritchie E , Jokinen T1An improved knowledgebase of a fuzzy logic system for an induction motor with rotor faults1Proceedings of CICEM’95 , Hangzhou , PRC ,Aug11995 , 901～905
[102] Cao Z , Ritchie E1Rotor fault diagnosis of induction motorbased on a dynamic associative memory of chaotic neural network1Proceedings of ICEM’2000 , Helsinki , Finland ,Sept.2000 , 863～867
[103]杨铁生.小波变换的工程分析与应用.北京∶科学出版社, 1999.
[104]刘贵忠,邸双亮.小波分析及其应用.西安∶西安电子科技大学出版社, 1997.