异步电动机转子故障诊断方法的研究
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摘要
由于现代科学的进步和生产系统的不断发展,电机在生产中发挥着越来越重要的作用。异步电机以其结构简单、价格低廉、可靠性高、维护方便而在工农业中取得了广泛的应用。随着现代工业系统的飞速发展,电机的单机容量不断增加,所驱动的负载也越来越复杂。电机故障不仅会损坏电机本身,严重时还会使电机突然停机、生产线崩溃,造成巨大的经济损失和灾难性后果。历史数据表明,转子断条是异步电机最常见的故障,占其全部故障种类的10%左右。因此,研究转子故障诊断技术以在电机故障早期发现故障并及时进行维修,具有重大的理论意义和社会经济效益,这已成为国内外学者们一个研究热点课题。本文基于异步电机转子正常与故障时的定子电流信号,提出了两种转子断条故障诊断研究方法。
文章研究了小波变换理论,从原理上分析了小波变换与其他变换如傅立叶变换、短时傅立叶的不同特点。鉴于小波变换具有多分辨率的特点和比傅立叶变换更强的特征提取功能,本文利用小波变换对电动机转子故障进行诊断方法研究。深入地分析了小波变换的浮动阈值消噪法,运用改进的浮动阈值消噪法进行了信号预处理。通过对仿真信号进行两种小波消噪方法的研究,对其进行消噪图形和消噪比的比较,证明了改进阈值的消噪方法不仅可使信号不失真,又能得到良好的消噪效果。
其次本文对电机故障模拟实验台进行了故障信号诊断实验。根据电动机转子故障的机理,结合所采集的实验数据中,利用所得的定子电流信号,进行小波消噪,然后对消噪后的信号利用小波包进行六层分解、重构,结合重构后的频谱图,对特定的频率段与正常信号相应的能量进行对比,确定故障频率,从而找出转子断条故障所在。本文还进行了基于Park's矢量变换对电机转子的故障诊断方法研究。得出对于频谱复杂,特征频率成分的幅值往往较小,当与基频分量很接近时,容易被基频及其“旁瓣”淹没的特征。利用基于Park's矢量方法的异步电动机故障监测方法可以克服这一缺点的结论。
With the progress of modern science and technology as well as the development of production system, electric machines play a more and more important role in modern industrial plants. Asynchronous motors are widely used in the industrial and agricultural production because of its simple structure, low price, high reliability and convenient maintenance. With the rapid development of the modern industrial system, the capacity of a single motor is keeping increasing and the load is also becoming more complicated now. A motor failure not only can result in damage to the motor, but also can result in unscheduled machine downtime and the shutdown of a production line, which will cause heavy financial losses and catastrophic failure. Statistical studies have shown that the rotor broken bar fault, which account for nearly 10% of total asynchronous motor failures, is the most familiar fault for asynchronous motors. Consequently, researches on rotor broken bar fault diagnosis able to detect this kind of fault at an early stage and also to allow for carefully planed repair actions are of great theoretical significance and socioeconomic benefits, which has turned to be the hotspot of research for scholars in the world. This paper presents two methods to diagnose rotor broken bar fault based on normal and Breakdown Stator current signals of asynchronous motor.
The transformation theory of wavelet has mainly been studied in this thesis. The characteristics of wavelet transformation and other transformation have been analyzed in principle, for example, Fourier, the short-time Fourier transformation and so on. In view of the fact that the wavelet transformation has the multi-resolution, it is advantageous that wavelet transformation can diagnose faults accurately and withdraw characteristic compared to Fourier transformation. As described, the Rotor breakdown diagnosis to motor is done using the wavelet transformation in this article. Author emphasized on de-noise way by the fluctuation threshold. It is necessary for the procedure computation to realize de-noise only using the way of improvement fluctuation threshold with no prior-knowledge. The method is proved that it can not only make signal no-distorted but also obtain good de-noise effect after comparing this kind of way with ones of wavelet packet de-noise.
When carries on the fault signal diagnosis to the fault simulation laboratory bench, article basis electric motor rotor breakdown mechanism. After de-noised using the fluctuation threshold, Stator current signals which are obtained in the experiment are decomposed into six levels using wavelet packet. Author compares energy of specific frequency bands of fault with ones of normality and makes faults definite after unifying the spectrograph by restructuring. Therefore the faults are found accurately. Next, this article also introduced based on the Park's vector transform in rotor broken bar fault diagnosis of asynchronous machine. Because of the frequency spectrum is complex, the characteristic frequency ingredient's peak-to-peak value is often small, when and the base frequency component is very close, is easily submerged by base frequency and "side lobe". However, asynchronous motor breakdown monitoring method based on the Park's vector transform may overcome this shortcoming.
文章研究了小波变换理论,从原理上分析了小波变换与其他变换如傅立叶变换、短时傅立叶的不同特点。鉴于小波变换具有多分辨率的特点和比傅立叶变换更强的特征提取功能,本文利用小波变换对电动机转子故障进行诊断方法研究。深入地分析了小波变换的浮动阈值消噪法,运用改进的浮动阈值消噪法进行了信号预处理。通过对仿真信号进行两种小波消噪方法的研究,对其进行消噪图形和消噪比的比较,证明了改进阈值的消噪方法不仅可使信号不失真,又能得到良好的消噪效果。
其次本文对电机故障模拟实验台进行了故障信号诊断实验。根据电动机转子故障的机理,结合所采集的实验数据中,利用所得的定子电流信号,进行小波消噪,然后对消噪后的信号利用小波包进行六层分解、重构,结合重构后的频谱图,对特定的频率段与正常信号相应的能量进行对比,确定故障频率,从而找出转子断条故障所在。本文还进行了基于Park's矢量变换对电机转子的故障诊断方法研究。得出对于频谱复杂,特征频率成分的幅值往往较小,当与基频分量很接近时,容易被基频及其“旁瓣”淹没的特征。利用基于Park's矢量方法的异步电动机故障监测方法可以克服这一缺点的结论。
With the progress of modern science and technology as well as the development of production system, electric machines play a more and more important role in modern industrial plants. Asynchronous motors are widely used in the industrial and agricultural production because of its simple structure, low price, high reliability and convenient maintenance. With the rapid development of the modern industrial system, the capacity of a single motor is keeping increasing and the load is also becoming more complicated now. A motor failure not only can result in damage to the motor, but also can result in unscheduled machine downtime and the shutdown of a production line, which will cause heavy financial losses and catastrophic failure. Statistical studies have shown that the rotor broken bar fault, which account for nearly 10% of total asynchronous motor failures, is the most familiar fault for asynchronous motors. Consequently, researches on rotor broken bar fault diagnosis able to detect this kind of fault at an early stage and also to allow for carefully planed repair actions are of great theoretical significance and socioeconomic benefits, which has turned to be the hotspot of research for scholars in the world. This paper presents two methods to diagnose rotor broken bar fault based on normal and Breakdown Stator current signals of asynchronous motor.
The transformation theory of wavelet has mainly been studied in this thesis. The characteristics of wavelet transformation and other transformation have been analyzed in principle, for example, Fourier, the short-time Fourier transformation and so on. In view of the fact that the wavelet transformation has the multi-resolution, it is advantageous that wavelet transformation can diagnose faults accurately and withdraw characteristic compared to Fourier transformation. As described, the Rotor breakdown diagnosis to motor is done using the wavelet transformation in this article. Author emphasized on de-noise way by the fluctuation threshold. It is necessary for the procedure computation to realize de-noise only using the way of improvement fluctuation threshold with no prior-knowledge. The method is proved that it can not only make signal no-distorted but also obtain good de-noise effect after comparing this kind of way with ones of wavelet packet de-noise.
When carries on the fault signal diagnosis to the fault simulation laboratory bench, article basis electric motor rotor breakdown mechanism. After de-noised using the fluctuation threshold, Stator current signals which are obtained in the experiment are decomposed into six levels using wavelet packet. Author compares energy of specific frequency bands of fault with ones of normality and makes faults definite after unifying the spectrograph by restructuring. Therefore the faults are found accurately. Next, this article also introduced based on the Park's vector transform in rotor broken bar fault diagnosis of asynchronous machine. Because of the frequency spectrum is complex, the characteristic frequency ingredient's peak-to-peak value is often small, when and the base frequency component is very close, is easily submerged by base frequency and "side lobe". However, asynchronous motor breakdown monitoring method based on the Park's vector transform may overcome this shortcoming.
引文
[1]陈卫文,方瑞明,异步电动机转子故障诊断方法,防爆电机,2006,41(6),39-42
[2]沈标正,电机故障诊断技术,北京,机械工业出版社,1996
[3]Tang T H,Liu Y J,li J R,et al,ANN-based fault diagnosis method with a combined BP algorithm,In:Proc.Of UKACC Int.Conf.On Control,1996(2),861-866
[4]Vas P.Parameter Estimation,Condition Monitoring,and Diagnosis of Electrical Machines.Oxford,U.K.:Clarendon,1993
[5]马宏忠,大型交流电机故障分析与诊断系统的研究,南京:东南大学博士学位论文,2001
[6]魏云冰,小波变换在电机故障诊断与测试中的应用研究,杭州:浙江大学博士学位论文,2002
[7]Briz F,Degner M W,Zamarron A,Guerrero J M,Online stator winding fault diagnosis in inverter-fed AC machines using high-frequency signal injection,IEEE Transactions on Industry Applications,2003,39(4),1109-1117
[8]Gao Zhitong,Fang Jiazhon,Chen Hongping,Support Vector Machine Used to Diagnose the fault of rotor Broken bars of induction Motors[J],IEEE Trans on Mag,2004,37(6),891-896
[9]Elkasabgy N M,Eastham A R,Dawson G E,Detection of broken bars in the cage rotor on an induction machine,IEEE Transactions on Industry Applications,1992,22(6),165-171
[10]Cruz S M A,Toliyat H A,Cardoso A J M,DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive,IEEE Transactions on Energy Conversion,2005,20(2),329-335
[11]Cruz S M A,Cardoso A J M,Toliyat H A,Diagnosis of stator,rotor and air gap eccentricity faults in three-phase induction motors based on the multiple reference frames theory,Conference Record of the 2003 IEEE Industry Applications Society Annual Meeting,Salt Lake City,USA,2003(2),1340-1346
[12]Nejjari H,Benbouzid M E H,Monitoring and Diagnosis of Induction Motors Electrical Faults Using a Current Park's Vector Pattern Learning Approach,IEEE Transactions on Industry Applications,2000,36(3),730-735
[13]Crux S M A,Cardoso A J M,Stator Winding Fault Diagnosis in Three-Phase Synchronous and Asynchronous Motors,by the Extended Park's Vector Approach,IEEE Transactions on Industry Applications,2001,37(5),1227-1233
[14]Bellini Albert,Filippetti Fiorenzo,Franceschini Giovanni et al,Quantitave Evaluation of Induction Motor Broken Bars by Means of Electrical Signature Analysis,IEEE Transactions on Industry Applications,2001,37(5),1248-1255
[15]Filippetti Fiorenzo,Franceschini Giovanni,Carla Tassoni et al,AI Techniques in Induction Machines Diagnosis Including the Speed Ripple Effect,IEEE Transactions on Industry Applications,1998,34(1),98-108
[16]Belting Alberto,On-Field Experience with Online Diagnosis of Large Induction Motors Cage Failures Using MCSA,IEEE Transactions on Industry Applications,2002,38(4),1045-1053
[17]张贤达,现代信号处理,北京,清华大学出版社,1995
[18]许佩霞,孙功宪,小波分析与应用实例(第二版),合肥:中国科学技术大学出版社,2001
[19]杨福生,小波变换的工程分析与应用,北京,科学出版社,1999
[20]陈逢时,子波变换理论及其在信号处理中的应用,北京,国防工业出版社,1998
[21]科恩L著,自居宪译,时-频分析:理论与应用,西安,西安交通大学出版社,1998
[22]胡广书,数字信号处理-理论、算法与实现(第二版),北京,清华大学出版社,2003
[23]丁康,陈键林,苏向荣,平稳和非平稳振动信号的若干处理方法及发展,振动工程学报,2003,16(1),1-10
[24]A.V.奥本海姆[美]著,刘数棠译,信号与系统,西安,西安交通大学出版社,1985
[25]任震等,小波分析及其在电力系统中的应用,北京,中国电力出版社,2003
[26]Olivier R,Duhamel Pierre,Fast Algorithms for Discrete and Continuous Wavelet Transforms,IEEE Transactions on Information Theory,1992,38(2),569-585
[27]李弼程,罗建书,小波分析及其应用,北京,电子工业出版社,2003
[28]秦前请,杨宗凯,实用小波分析,西安,西安电子科技大学出版社,2002
[29]李建平等,小波分析与信号处理-理论、应用及软件实现,重庆,重庆出版社,1997
[30]Ingrid Daubechies I,Where Do Wavelets Come From-A Personal Point of View,Proc.IEEE,1996,84(5),510-513
[31]曹龙汉,柴油机智能化故障诊断技术研究[D],重庆,重庆大学,2001
[32]M.Misiti,Y.Misiti,G.Oppenheim,etal,MATLAB Wavelet Toolbox User's Guide(Version).The Mathworks,Inc,1997
[33]刘素美,李书光,超声检测信号处理的小波基选取,新疆石油学院,2004(4),75-78
[34]周小勇,小波分析在故障诊断中的应用[D],上海,上海海运学院,2001
[35]刘娟花,小波分析在语音去噪中的应用[D],西安,西安理工大学,2004
[36]D.L.Doroho,De-noising by soft threshold,IEEE,Trans.Inform,Theory 41(May,1995),613-627
[37]时献江,交流感应电动机故障诊断方法研究,哈尔滨电工学院学报,1994(3),80-85
[38]邱阿瑞,提取感应电动机转子故障特征的新方法,清华大学学报,1997,37(1),35-37
[39]姜建国,汪庆生,杨秉寿等,用自适应方法提取鼠笼式异步电机转子断条的特征分量,电工技术学报,1990(4),1-6
[40]曹志彤,何国光,陈宏平等,电机故障特征值的倍频小波分析,中国电机工程学报,2003,23(7),112-116
[41]付大金,王秀和等,感应电动机故障诊断技术综述,大电机技术,2004(3),27-36
[42]牛发亮,黄进,笼型异步电动机状态监测与转子故障诊断方法对比分析,中小型电机,2004,31(4),48-52
[43]Benbouzid M E H,Kliman G B,What Stator Current Processing-Based Technique to Use for Induction Motor Rotor Faults Diagnosis,IEEE Transactions on Energy Conversion,2003,18(2),238-244
[44]马宏忠,胡虔生,黄允凯等,感应电机转子绕组故障仿真与实验研究,中国电机工程学报,2003,23(4),107-112
[45]高景德,王祥珩,李发海,交流电机及其系统的分析,北京,清华大学出版社,1993
[46]Gentile G,Meo S,Ometto A,Induction motor current signature analysis to diagnostics,of stator short circuits,Proc.4th IEEE International Symposium on Diagnostics for Electric Machines,Power Electronics and Drives,SDEMPED 2003Atlanta,GA,USA,47-51
[47]Milimonfared J,Kelk H M,Nandi S,Minassians A D,Toliyat H A,A novel approach for broken rotor bar detection in cage induction motors,IEEE Transactions on Industry Applications,1999,35(5),1000-1006
[48]黄进,应用P对极n相变换分析定子绕组故障的同步发电机,中国电机工程学报,1994,14(5),10-16
[2]沈标正,电机故障诊断技术,北京,机械工业出版社,1996
[3]Tang T H,Liu Y J,li J R,et al,ANN-based fault diagnosis method with a combined BP algorithm,In:Proc.Of UKACC Int.Conf.On Control,1996(2),861-866
[4]Vas P.Parameter Estimation,Condition Monitoring,and Diagnosis of Electrical Machines.Oxford,U.K.:Clarendon,1993
[5]马宏忠,大型交流电机故障分析与诊断系统的研究,南京:东南大学博士学位论文,2001
[6]魏云冰,小波变换在电机故障诊断与测试中的应用研究,杭州:浙江大学博士学位论文,2002
[7]Briz F,Degner M W,Zamarron A,Guerrero J M,Online stator winding fault diagnosis in inverter-fed AC machines using high-frequency signal injection,IEEE Transactions on Industry Applications,2003,39(4),1109-1117
[8]Gao Zhitong,Fang Jiazhon,Chen Hongping,Support Vector Machine Used to Diagnose the fault of rotor Broken bars of induction Motors[J],IEEE Trans on Mag,2004,37(6),891-896
[9]Elkasabgy N M,Eastham A R,Dawson G E,Detection of broken bars in the cage rotor on an induction machine,IEEE Transactions on Industry Applications,1992,22(6),165-171
[10]Cruz S M A,Toliyat H A,Cardoso A J M,DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive,IEEE Transactions on Energy Conversion,2005,20(2),329-335
[11]Cruz S M A,Cardoso A J M,Toliyat H A,Diagnosis of stator,rotor and air gap eccentricity faults in three-phase induction motors based on the multiple reference frames theory,Conference Record of the 2003 IEEE Industry Applications Society Annual Meeting,Salt Lake City,USA,2003(2),1340-1346
[12]Nejjari H,Benbouzid M E H,Monitoring and Diagnosis of Induction Motors Electrical Faults Using a Current Park's Vector Pattern Learning Approach,IEEE Transactions on Industry Applications,2000,36(3),730-735
[13]Crux S M A,Cardoso A J M,Stator Winding Fault Diagnosis in Three-Phase Synchronous and Asynchronous Motors,by the Extended Park's Vector Approach,IEEE Transactions on Industry Applications,2001,37(5),1227-1233
[14]Bellini Albert,Filippetti Fiorenzo,Franceschini Giovanni et al,Quantitave Evaluation of Induction Motor Broken Bars by Means of Electrical Signature Analysis,IEEE Transactions on Industry Applications,2001,37(5),1248-1255
[15]Filippetti Fiorenzo,Franceschini Giovanni,Carla Tassoni et al,AI Techniques in Induction Machines Diagnosis Including the Speed Ripple Effect,IEEE Transactions on Industry Applications,1998,34(1),98-108
[16]Belting Alberto,On-Field Experience with Online Diagnosis of Large Induction Motors Cage Failures Using MCSA,IEEE Transactions on Industry Applications,2002,38(4),1045-1053
[17]张贤达,现代信号处理,北京,清华大学出版社,1995
[18]许佩霞,孙功宪,小波分析与应用实例(第二版),合肥:中国科学技术大学出版社,2001
[19]杨福生,小波变换的工程分析与应用,北京,科学出版社,1999
[20]陈逢时,子波变换理论及其在信号处理中的应用,北京,国防工业出版社,1998
[21]科恩L著,自居宪译,时-频分析:理论与应用,西安,西安交通大学出版社,1998
[22]胡广书,数字信号处理-理论、算法与实现(第二版),北京,清华大学出版社,2003
[23]丁康,陈键林,苏向荣,平稳和非平稳振动信号的若干处理方法及发展,振动工程学报,2003,16(1),1-10
[24]A.V.奥本海姆[美]著,刘数棠译,信号与系统,西安,西安交通大学出版社,1985
[25]任震等,小波分析及其在电力系统中的应用,北京,中国电力出版社,2003
[26]Olivier R,Duhamel Pierre,Fast Algorithms for Discrete and Continuous Wavelet Transforms,IEEE Transactions on Information Theory,1992,38(2),569-585
[27]李弼程,罗建书,小波分析及其应用,北京,电子工业出版社,2003
[28]秦前请,杨宗凯,实用小波分析,西安,西安电子科技大学出版社,2002
[29]李建平等,小波分析与信号处理-理论、应用及软件实现,重庆,重庆出版社,1997
[30]Ingrid Daubechies I,Where Do Wavelets Come From-A Personal Point of View,Proc.IEEE,1996,84(5),510-513
[31]曹龙汉,柴油机智能化故障诊断技术研究[D],重庆,重庆大学,2001
[32]M.Misiti,Y.Misiti,G.Oppenheim,etal,MATLAB Wavelet Toolbox User's Guide(Version).The Mathworks,Inc,1997
[33]刘素美,李书光,超声检测信号处理的小波基选取,新疆石油学院,2004(4),75-78
[34]周小勇,小波分析在故障诊断中的应用[D],上海,上海海运学院,2001
[35]刘娟花,小波分析在语音去噪中的应用[D],西安,西安理工大学,2004
[36]D.L.Doroho,De-noising by soft threshold,IEEE,Trans.Inform,Theory 41(May,1995),613-627
[37]时献江,交流感应电动机故障诊断方法研究,哈尔滨电工学院学报,1994(3),80-85
[38]邱阿瑞,提取感应电动机转子故障特征的新方法,清华大学学报,1997,37(1),35-37
[39]姜建国,汪庆生,杨秉寿等,用自适应方法提取鼠笼式异步电机转子断条的特征分量,电工技术学报,1990(4),1-6
[40]曹志彤,何国光,陈宏平等,电机故障特征值的倍频小波分析,中国电机工程学报,2003,23(7),112-116
[41]付大金,王秀和等,感应电动机故障诊断技术综述,大电机技术,2004(3),27-36
[42]牛发亮,黄进,笼型异步电动机状态监测与转子故障诊断方法对比分析,中小型电机,2004,31(4),48-52
[43]Benbouzid M E H,Kliman G B,What Stator Current Processing-Based Technique to Use for Induction Motor Rotor Faults Diagnosis,IEEE Transactions on Energy Conversion,2003,18(2),238-244
[44]马宏忠,胡虔生,黄允凯等,感应电机转子绕组故障仿真与实验研究,中国电机工程学报,2003,23(4),107-112
[45]高景德,王祥珩,李发海,交流电机及其系统的分析,北京,清华大学出版社,1993
[46]Gentile G,Meo S,Ometto A,Induction motor current signature analysis to diagnostics,of stator short circuits,Proc.4th IEEE International Symposium on Diagnostics for Electric Machines,Power Electronics and Drives,SDEMPED 2003Atlanta,GA,USA,47-51
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