基于能量频谱分析法的电机故障诊断
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摘要
基于定子电流信号进行异步电机故障诊断时,转子断条和匝间短路故障特征频率分量常常被电流的基频分量淹没,利用小波变换的能量谱对其进行诊断十分必要。考虑到小波分析在时域、频域都具有表征信号局部特征的能力,能通过时频窗的灵活变换来突出信号的不同频率成分;同时还有用小波处理非平稳信号的优越性,计算出各频带所占能量,进而获取能量分布的故障特征。实践表明,通过上述分析并从能量分布的角度出发,可以快速、准确地诊断出电机故障,诊断效果好于傅里叶分析;同时也提供了一种思路,为电机故障在线实时诊断提供了理论依据。
When troubleshooting asynchronous motor based on stator current signal,the broken rotor bar and turn to turn short-circuit fault characteristics frequency components was often overwhelmed by current fundamental frequency component.By analyzing of rotor bar breaking fault and turn to turn short-circuit fault principle,diagnosis using Wavelet energy spectrum proves to be very necessary.Taking into account the ability of wavelet analysis to show the local characteristics of signals in time and frequency domain,the ability to highlight different frequency components of the signals through the flexible transform time-frequency window,and the useful wavelet processing advantages of non-stationary signals,the energy of every band was calculated and the fault features of energy distribution were obtained.Practice shows that by the above analysis and from the perspective of energy distribution,the motor fault can be quickly and accurately diagnosed,and the diagnostic quality is better than Fourier analysis,thus,providing a theoretical basis for online real-time fault diagnosis of motors.
When troubleshooting asynchronous motor based on stator current signal,the broken rotor bar and turn to turn short-circuit fault characteristics frequency components was often overwhelmed by current fundamental frequency component.By analyzing of rotor bar breaking fault and turn to turn short-circuit fault principle,diagnosis using Wavelet energy spectrum proves to be very necessary.Taking into account the ability of wavelet analysis to show the local characteristics of signals in time and frequency domain,the ability to highlight different frequency components of the signals through the flexible transform time-frequency window,and the useful wavelet processing advantages of non-stationary signals,the energy of every band was calculated and the fault features of energy distribution were obtained.Practice shows that by the above analysis and from the perspective of energy distribution,the motor fault can be quickly and accurately diagnosed,and the diagnostic quality is better than Fourier analysis,thus,providing a theoretical basis for online real-time fault diagnosis of motors.
引文
[1]沈标正.电机故障诊断技术[M].北京:机械工业出版社,1996.
[2]刘华,蔡正敏,王跃社,等.小波包算法在滚动轴承的在线故障诊断中的应用[J].机械科学与技术,1999,15(2):301-303.
[3]于德介,程军圣,杨宇.Hilbert能量谱及其在齿轮故障诊断中的应用[J].湖南大学学报,2003,30(4):47-50.
[4]郑治真,沈萍,杨选辉,等.小波变换及其Matlab工具的应用[M].北京:地震出版社,2001.
[5]虞和济,韩庆大,李沈.设备故障诊断工程[M].北京:冶金工业出版社,2001.
[6]张建文.电气设备故障诊断技术[M].北京:中国水利水电出版社,2008.
[7]许伯强.大型异步电机状态监测与故障诊断方法评述[J].华北电力大学学报,2002,29(3):33-37.
[8]李永刚,李和明,万书亭.发电机转子绕组匝间短路故障特性分析与识别[M].北京:中国电力出版社,2009.
[9]王学伟,何永远.基于时-频能量群的非稳态谐波检测方法的研究[J].电测与仪表,2010,47(531):6-11.
[10]秦金婧,张士兵,包志华.基于小波包变换的能量检测技术研究[J].通信技术,2010,43:20-23.
[11]李建明.基于小波包能量谱的天气雷达伺服电机故障预警系统研究[J].河北科技大学学报,2010,31(3):233-236.
[12]黄建红.基于小波包频带能量特征的包络分析在滚动轴承故障诊断中的应用[J].南昌大学学报,2006,30(4):402-405.
[13]朱高中,刘宝菊.新型小波包频带能量法在电机断条故障诊断中的研究[J].信息化纵横,2009(15):64-67.
[14]李辉.Hilbert-Huang变换能量谱在轴承故障诊断中的应用[J].军械工程学院学报,2005,17(4):37-40.
[15]孙洁娣,靳世久.基于小波包能量及高阶谱的特征提取方法[J].天津大学学报,20104,3(6):562-566.
[16]张敬春,谷爱昱,莫惠芳.基于小波能量谱分析的电机故障诊断[J].电力系统及其自动化学报,2006,18(3):55-58.
[2]刘华,蔡正敏,王跃社,等.小波包算法在滚动轴承的在线故障诊断中的应用[J].机械科学与技术,1999,15(2):301-303.
[3]于德介,程军圣,杨宇.Hilbert能量谱及其在齿轮故障诊断中的应用[J].湖南大学学报,2003,30(4):47-50.
[4]郑治真,沈萍,杨选辉,等.小波变换及其Matlab工具的应用[M].北京:地震出版社,2001.
[5]虞和济,韩庆大,李沈.设备故障诊断工程[M].北京:冶金工业出版社,2001.
[6]张建文.电气设备故障诊断技术[M].北京:中国水利水电出版社,2008.
[7]许伯强.大型异步电机状态监测与故障诊断方法评述[J].华北电力大学学报,2002,29(3):33-37.
[8]李永刚,李和明,万书亭.发电机转子绕组匝间短路故障特性分析与识别[M].北京:中国电力出版社,2009.
[9]王学伟,何永远.基于时-频能量群的非稳态谐波检测方法的研究[J].电测与仪表,2010,47(531):6-11.
[10]秦金婧,张士兵,包志华.基于小波包变换的能量检测技术研究[J].通信技术,2010,43:20-23.
[11]李建明.基于小波包能量谱的天气雷达伺服电机故障预警系统研究[J].河北科技大学学报,2010,31(3):233-236.
[12]黄建红.基于小波包频带能量特征的包络分析在滚动轴承故障诊断中的应用[J].南昌大学学报,2006,30(4):402-405.
[13]朱高中,刘宝菊.新型小波包频带能量法在电机断条故障诊断中的研究[J].信息化纵横,2009(15):64-67.
[14]李辉.Hilbert-Huang变换能量谱在轴承故障诊断中的应用[J].军械工程学院学报,2005,17(4):37-40.
[15]孙洁娣,靳世久.基于小波包能量及高阶谱的特征提取方法[J].天津大学学报,20104,3(6):562-566.
[16]张敬春,谷爱昱,莫惠芳.基于小波能量谱分析的电机故障诊断[J].电力系统及其自动化学报,2006,18(3):55-58.
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