期刊文献+

免疫原理用于异步电动机故障诊断的研究 被引量:28

SQUIRREL-CAGE MOTORS FAULT DIAGNOSIS USING IMMUNOLOGY PRINCIPLES
在线阅读 下载PDF
导出
摘要 基于免疫系统的基本原理,提出了一种用于异步电动机故障诊断的新方法。根据免疫系统中T细胞和B细胞的机能,分别建立了T模块和B模块。T模块用来区分电动机的正常状态和故障状态,B模块用来改进T模块在故障诊断空间的覆盖。T模块具有自适应功能,可以根据探测得到的信息调整其状态位置,便于将来更好地进行故障诊断。B模块采用变异和克隆机制,具有学习、记忆等功能,有利于诊断知识的扩充。通过测量异步电动机定子电流频谱,采用T、B模块成功地诊断出电动机的转子断条故障,证明了该方法的正确性和有效性。 Based on the principles of the immune system, a new method of fault diagnosis for squirrel-cage motors ispresented. According to the functions of the T cells and B cells of the immune system, T-module and B-module are established,among which the T-module is established for discriminating normal states from fault states, and the B-module is established for improving the diagnostic space coverage of the T-module. The T-module has an adaptive function, and it can adjust the positions of the states for better diagnosis in accordance with the signals obtained. By means of mutation and cloning, the B-module is capable of learning and memory, thus it is useful for the expansion of diagnosis information. Used the statorcurrent frequency spectra of the squirrel-cage motor, thismethod has successfully diagnosed broken rotor bars,confirming the validity and efficiency of the approach.
机构地区 燕山大学
出处 《中国电机工程学报》 EI CSCD 北大核心 2003年第6期132-136,共5页 Proceedings of the CSEE
基金 国家留学基金项目 (留金出(2001)3048号)
关键词 异步电动机 故障诊断 免疫原理 人工神经网络 定子 转子 Squirrel-cage motor Immunology principles Fault diagnosis
  • 相关文献

参考文献10

  • 1刘振兴,张哲,尹项根.异步电动机的状态监测与故障诊断技术综述[J].武汉科技大学学报,2001,24(3):285-289. 被引量:83
  • 2董爱华,王福忠,艾永乐,王芳.小波变换及其在异步电动机转子断路故障诊断中的应用[J].焦作工学院学报,2001,20(2):94-97. 被引量:1
  • 3宁玉泉.鼠笼感应电机转子断条和端环开裂的故障诊断和参数计算[J].中国电机工程学报,2002,22(10):97-103. 被引量:28
  • 4Timmis J, Neal M, Hunt I. An artificial immune system for data analysis[l].Biosystems 2000,55 (2): 143-150.
  • 5Abbaszadeh K, Milimonfared J, Haji M, et al. Broken bar detection in induction motor via wavelet transformation[C]. IECON '01. The 27th Annual Conference of the IEEE Industrial Electronics Society, 2001.
  • 6McCully P J, Landy C F. Evaluation of current and vibration signals for squirrel cage induction motor condition monitoring[C]. Eight International Conference on Electrical Machines and Drives ,1997.
  • 7Filippetti F, Franceschini G, Tassoni C, et al. AI techniques in induction machine diagnosis including the speed ripple effect[J]. IEEE Trans. on Industry Applications,1998,34(1): 98-108.
  • 8Milimonfared J, Kelk H M, Nandi S, et al. A novel approach for brokenbar detection in cage induction motors[J], IEEE Trans. on Industry Applications, 1999,35 (5): 1000-1006.
  • 9Lasurt L Stronach A F , penman J. A fuzzy logic approach to the interpretation of higher order spectra applied to fault diagnosis in electrical machines[C]. International Conference of the North American Fuzzy Information Processing Society, 2000.
  • 10Dasgupta D, Forrest S. Novelty detection in time series data using ideas from immunology[C]. 5th Int. Conf. on Intelligent Systems, June 19-21, Reno, Nevada, 1996.

二级参考文献27

  • 1秦前清 杨宗凯.实用小波分析[M].西安:西安电子科技大学出版社,1995,第二章.32.
  • 2[1]Benbouzid M E H. Bibliography on induction motors faults detection and diagnosis[J]. IEEE Transactions on Energy Conversion , 1999, 14(12):1065-1074.
  • 3[2]Ning Yuquan. Effect of rotor eccentricity on the parameter and circulating current for a non-salient-pole machine[C]. CICEM'99 68-71. Xian 1999.
  • 4[3]S.Williamson et al. Steady-state annalysis of 3-phase cage motors with rotor-bar and end-ring faults[J]. IEE PROC, 1982,129(3):93-100.
  • 5[4]CRUZ S M A. Rotor cage fault diagnosis in three-phase induction motors by extended park's vector approach[J]. Electric Machines and Power Systems 2000, 289-299.
  • 6PJ达夫勒 J 彭曼.电机的状态监测[M].北京:水利电力出版社,1992.85-102.
  • 7John S Hsu,Jan Stein.Effects of Eccentricities on Shaft Signals Studied throughWindingless Rotors[J].IEEE Transactions on Energy Conversion,1994,9(3):564-571.
  • 8J R Cameron,W T Thomson,A B Dow.Vibration and Current Monitoring for Detecting AirGap Eccentricity in 3-Phase Induction Motor[J].IEEE Proceedings,1986,33(3):155-163.
  • 9G B Kliman, R A Koegl, J Stein.Noninvasive Detection of Broken Rotor Bars inOperating Induction Motors[J].IEEE Transactions on Energy Conversion,1998,(3):873-879.
  • 10Randy R Schoen, Brian K Lin,Thomas G Habetler,et al.An Unsupervised, On-line Systemfor Induction Motor Fault Detection Using Stator Current Monitoring[J].IEEE Transactions.on Industry Applications,1995,31(6):1280-1286.

共引文献108

同被引文献325

引证文献28

二级引证文献242

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部