嵌入式鼠笼断条智能在线检测系统的研究
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摘要
三相鼠笼式异步电动机由于结构简单、价格低廉、运行可靠，在工农业生产中得到了广泛应用。但由于工作环境恶劣，或者电机频繁起动等原因，转子导条经常会发生开焊和断裂等故障，因此有必要对电机进行转子断条在线检测。
本文采用的方法是电动机电流信号分析法（MCSA），即通过检测电动机定子电流信号中(1-2s)f1频率分量的大小检测转子断条故障。本文首次提出了基于连续细化傅里叶分析、小波包分解、BP神经网络和“能量-故障”诊断模式的转子断条检测法。
对于无负载波动的情况，本文提出了基于连续细化傅里叶分析检测方法，这种方法对电机定子电流信号进行连续细化傅里叶分析，首次提出了准确抵消定子电流中f1分量的算法，从根本上解决了f1频率分量的泄漏淹没(1-2s)f1频率分量这一问题。
针对负载波动时用傅里叶分析得不到信号正确频谱的问题，本文采用了小波包分解的方法。通过对运行时电机定子电流信号小波包分解系数的分析，判断有无负载波动，读取波动点，利用无负载波动时信号的能量计算基波分量，再利用特定频带中无负载波动时小波包分解信号的能量和基波分量的幅值作为特征向量，输入给神经网络，计算故障分量的幅值，与基波分量的幅值比较，得到故障程度的信息。在无负载波动时，这种方法可以和基于连续细化傅里叶分析的检测方法得到的结果相互佐证，提高检测的准确率。
本课题成功研制了基于嵌入式微机系统的智能在线断条检测仪，系统集成度高，体积小，功耗低，稳定可靠，并且程序设计与标准微机完全相同。利用该检测仪对天津某电厂的多台电机进行了检测，实验结果表明该检测仪在有无负载波动时均能够准确检测转子断条故障。
Because of their simple structure, low price and high reliability, squirrel-cage induction motors are widely used in many aspects. But mechanical faults such as breakage of rotor bars often happen owing to various stresses. Therefore online detection on induction motors is necessary.
The method adopted in this paper is called Motor Current Signature Analysis (MCSA). It detects broken rotor bars according to the amplitude of the (1-2s)f1 frequency component in stator current signal. This paper proposed a novel method, which is based on continuous and subdivided Fourier Transform (FT), wavelet packet decomposition (WPD), BP artificial neural network (ANN) and ‘energy-fault’ diagnosis mode for the first time.
Under steady load condition a method based on continuous and subdivided FT is proposed. The stator current signal under operation condition is analyzed with continuous and subdivided FT. A novel arithmetic is proposed for the first time to counteract the fundamental component. The problem that the (1-2s)f1 frequency component is submerged by the fundamental component is solved completely.
WPD is adopted because the spectrum of the stator current cannot be gained correctly by FT when the load torque is fluctuant. The WPD coefficients of stator current signal under operation condition are used to judge whether the load torque is fluctuant and to read the fluctuant points. The signal’s energy in the range of steady state is used to calculate the amplitude of the fundamental component, which is selected as the input of BP ANN together with the energy of WPD coefficients in the range of steady state of special node. The result of ANN is the amplitude of the (1-2s)f1 frequency component. The information of fault degree is gained from two amplitudes abovementioned. Under steady condition this method and the above method based on continuous and subdivided FT can testify each other to increase the detection veracity.
An intelligent online detector based on embedded PC system for broken rotor bars is manufactured successfully. This system has advantages of high integration level, small volume, low power, high stability and reliability. Moreover, it has the same program protocol with ordinary PC. Experiment results show the accuracy of the proposed method for broken rotor bar detection in squirrel-cage induction motors under both steady and fluctuant load condition.
本文采用的方法是电动机电流信号分析法（MCSA），即通过检测电动机定子电流信号中(1-2s)f1频率分量的大小检测转子断条故障。本文首次提出了基于连续细化傅里叶分析、小波包分解、BP神经网络和“能量-故障”诊断模式的转子断条检测法。
对于无负载波动的情况，本文提出了基于连续细化傅里叶分析检测方法，这种方法对电机定子电流信号进行连续细化傅里叶分析，首次提出了准确抵消定子电流中f1分量的算法，从根本上解决了f1频率分量的泄漏淹没(1-2s)f1频率分量这一问题。
针对负载波动时用傅里叶分析得不到信号正确频谱的问题，本文采用了小波包分解的方法。通过对运行时电机定子电流信号小波包分解系数的分析，判断有无负载波动，读取波动点，利用无负载波动时信号的能量计算基波分量，再利用特定频带中无负载波动时小波包分解信号的能量和基波分量的幅值作为特征向量，输入给神经网络，计算故障分量的幅值，与基波分量的幅值比较，得到故障程度的信息。在无负载波动时，这种方法可以和基于连续细化傅里叶分析的检测方法得到的结果相互佐证，提高检测的准确率。
本课题成功研制了基于嵌入式微机系统的智能在线断条检测仪，系统集成度高，体积小，功耗低，稳定可靠，并且程序设计与标准微机完全相同。利用该检测仪对天津某电厂的多台电机进行了检测，实验结果表明该检测仪在有无负载波动时均能够准确检测转子断条故障。
Because of their simple structure, low price and high reliability, squirrel-cage induction motors are widely used in many aspects. But mechanical faults such as breakage of rotor bars often happen owing to various stresses. Therefore online detection on induction motors is necessary.
The method adopted in this paper is called Motor Current Signature Analysis (MCSA). It detects broken rotor bars according to the amplitude of the (1-2s)f1 frequency component in stator current signal. This paper proposed a novel method, which is based on continuous and subdivided Fourier Transform (FT), wavelet packet decomposition (WPD), BP artificial neural network (ANN) and ‘energy-fault’ diagnosis mode for the first time.
Under steady load condition a method based on continuous and subdivided FT is proposed. The stator current signal under operation condition is analyzed with continuous and subdivided FT. A novel arithmetic is proposed for the first time to counteract the fundamental component. The problem that the (1-2s)f1 frequency component is submerged by the fundamental component is solved completely.
WPD is adopted because the spectrum of the stator current cannot be gained correctly by FT when the load torque is fluctuant. The WPD coefficients of stator current signal under operation condition are used to judge whether the load torque is fluctuant and to read the fluctuant points. The signal’s energy in the range of steady state is used to calculate the amplitude of the fundamental component, which is selected as the input of BP ANN together with the energy of WPD coefficients in the range of steady state of special node. The result of ANN is the amplitude of the (1-2s)f1 frequency component. The information of fault degree is gained from two amplitudes abovementioned. Under steady condition this method and the above method based on continuous and subdivided FT can testify each other to increase the detection veracity.
An intelligent online detector based on embedded PC system for broken rotor bars is manufactured successfully. This system has advantages of high integration level, small volume, low power, high stability and reliability. Moreover, it has the same program protocol with ordinary PC. Experiment results show the accuracy of the proposed method for broken rotor bar detection in squirrel-cage induction motors under both steady and fluctuant load condition.
引文
吴国沛，任阵，管霖等，鼠笼异步电动机常见故障的分析与诊断，华南理工大学学报，1999，27(10)：52~57
董国艳，张春喜，时献江. 笼型异步电动机转子故障诊断技术综述. 中小型电机. 2001，28(1)：39~42
蔡泽祥，俞亮，高爱云，大型电动机的内部故障诊断与保护方法，电力自动化设备，2002，22(7)：61~66
Nagwa M. Elkasabgy, Anthony R.Eastham. Detection of Broken Bars in the Cage on an Induction Machine. IEEE Transaction On Industry Application Vol.28,No.1, January/February 1992, pp.165-170
宁玉泉，笼型感应电动机转子故障时的参数计算，电工技术学报，2002，17(4)：84~88
张龙照, 邱阿瑞，用频谱分析方法检测异步电动机转子故障，电工技术学报. 1987，(4)：46~50
G.B.Kliman. 运行中的感应电动机转子断条的非侵入性检测. 国外大电机. 1991，3(26)：26~31
P.J.McCully and C.F.Landy. Evaluation of Current and Vibration Signals for Squirrel Cage Induction Motor Condition Monitoring. EMD97 1-3 September 1997 Conference Publication No.444 of IEE, 331~335
邱阿瑞，用起动电流的时变频谱诊断鼠笼异步电动机转子故障，中国电机工程学报，1995，15(4)：267~273
许伯强，叶东，孙丽玲，笼型异步电动机转子断条在线检测新方法，华北电力大学学报, 1998，25(3)：34~39
许伯强，李和明，孙丽玲等，笼型异步电动机转子断条综合在线检测方法，华北电力大学学报， 2000，27(4)：23~28
许伯强，李和明，孙丽玲，基于小波包分析的笼型异步电动机转子断条在线
检测方法，中小型电机，2001，28(6)：47~51
许伯强，李和明，孙丽玲，小波分析应用于笼型异步电动机转子断条在线检测初探，中国电机工程学报，2001，21(11)：24~28
任震，何建军，黄雯莹等，基于小波包算法的电机故障信号的压缩和重构，中国电机工程学报，2001，21(1)：25~29
黄群古，任震，黄雯莹等，梯形小波变换及其在鼠笼电动机转子故障分析中的应用，中国电机工程学报，2001，21(6)：29~32
曹志彤，陈宏平，何国光，基于小波重构的异步电动机故障诊断，电工技术学报，2002，17(2)：80~83
K.Abbaszadeh, J.Milimonfared, M.Haji, etc. Broken Bar Detection in Induction Motor via Wavelet Transformation. IECON’01: The 27th Annual Conference of the IEEE Industrial Electronics Society. 2001, 95~99
A.J. Marques Cardoso, S.M.A. Cruz, J.F.S. Carvalho, etc. Rotor Cage Fault Diagnosis in Three-phase Induction Motors, by Park's Vector Approach. Conference Record of the 1995 IEEE, 1995, vol.1, 642~646
Hamid Nejjari, Mohamed EI Hachemi Benbouzid. Monitoring and Diagnosis of Induction Motors Electrical Faults Using a Current Park's Vector Pattern Learning Approach. IEEE Transactions on Industry Applications. Vol. 36, No. 3, 2000, 730~735
Filippetti F, Martelli M, Franceschini G, et al. Development if Expert System knowledge Base to Online Diagnosis of Rotor Electrical Faults of Induction Motors. Proc. IEEE-IAS ANNU. Meeting, Houston, TX, Oct. 1992, 92~99.
Tak Son Cheang, Linzheng Zhang. A New Prototype of Diagnosis System of Inner-faults for Three-phase Induction Motors Developed by Expert System. Proceedings of the Fifth International Conference,?Vol.1,? 2001, vol.1, 312~ 316
Zhongming Ye, Bin Wu, A.R.Sadeghian. Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition. APEC 2001. Sixteenth Annual IEEE ,?Vol. 2 , March 2001, 1022~1029
Zhongming Ye, Bin Wu, Navid Zargati, “Online Mechanical Fault Diagnosis of Induction Motor by Wavelet Artificial Network Using Stator Current ” 26th Annual Confjerence of the IEEE , Vol. 2 , 2000, 1183~1188
K.abbaszadeh, J. milimonfared, M. Haji, H.A.Toliyat “Broken Bar Detection in Induction Motor via Wavelet Transformation ” IEEE conference record 2001, 95~99
Tan, Woei Wan, Huo Hong. An On-line Neurofuzzy Approach for Detecting Faults in Induction Motors. Electric Machines and Drives Conference, 2001, 878~883
Masoud Haji, Hamid A. Toliyat. Pattern Recognition——A Technique for Induction Machines Rotor Broken Bar Detection. IEEE, IEEE Transactions on Energy Conversion, 2001, 312~317
H.J.威佛, 离散和连续傅里叶分析理论，北京邮电学院出版社，1991
郑治真，沈萍，杨选辉等，小波变换及其Matlat工具的应用，地震出版社，2001
彭玉华，小波变换与工程应用，科学出版社，1999
杨福生，小波变换的工程分析与应用，科学出版社，1999
焦李成，神经网络的应用与实现，西安电子科技大学出版社, 1993
赵振宇, 徐用懋，模糊理论和神经网络的基础与应用，清华大学出版社, 1996
袁曾任，人工神经网络及其应用，清华大学出版社, 1999
卢献国，赵勇，鼠笼式异步电动机断条状况和断条检测仪的研制，河南电力，1995, 2, 28~31
邱阿瑞，张龙照，鼠笼式异步电动机转子导条及瑞环故障时的稳态运行分析， 电工技术学报，1987(3)
邱阿瑞，张龙照，异步电动机转子故障点分布对故障检测的影响，电工技术杂志，1989(6)，14~16
马宏忠，胡虞生，黄允凯等，感应电机转子断条根数影响分析，河海大学学报（自然科学版），2003，31(5)，573-576
J. F. Bangura, N. A. Demerdash, Diagnosis and Characterization of Effects of Broken Bars and Connectors in Squirrel-Cage Induction Motors by a Time-Stepping coupled Finite Element-State Space Modeling Approach, IEEE Trans. on Energy Conversion, 1999, vol.14(4), 1167-1176
A.Bellini, F. Filippetti, G. Fracesschini, etc. ENEL’s Experience with On-line Diagnosis of Large Induction Motors Cage Failures. Conference Record of the 2000 IEEE ,?vol.1, 492~498
刘进明，应怀樵，FFT谱连续细化分析的富里叶变换法，振动工程学报.1995，8(2)：162~166
何正嘉，机械设备非平稳信号的故障诊断原理及应用，高等教育出版社, 2001
董国艳，张春喜，时献江. 笼型异步电动机转子故障诊断技术综述. 中小型电机. 2001，28(1)：39~42
蔡泽祥，俞亮，高爱云，大型电动机的内部故障诊断与保护方法，电力自动化设备，2002，22(7)：61~66
Nagwa M. Elkasabgy, Anthony R.Eastham. Detection of Broken Bars in the Cage on an Induction Machine. IEEE Transaction On Industry Application Vol.28,No.1, January/February 1992, pp.165-170
宁玉泉，笼型感应电动机转子故障时的参数计算，电工技术学报，2002，17(4)：84~88
张龙照, 邱阿瑞，用频谱分析方法检测异步电动机转子故障，电工技术学报. 1987，(4)：46~50
G.B.Kliman. 运行中的感应电动机转子断条的非侵入性检测. 国外大电机. 1991，3(26)：26~31
P.J.McCully and C.F.Landy. Evaluation of Current and Vibration Signals for Squirrel Cage Induction Motor Condition Monitoring. EMD97 1-3 September 1997 Conference Publication No.444 of IEE, 331~335
邱阿瑞，用起动电流的时变频谱诊断鼠笼异步电动机转子故障，中国电机工程学报，1995，15(4)：267~273
许伯强，叶东，孙丽玲，笼型异步电动机转子断条在线检测新方法，华北电力大学学报, 1998，25(3)：34~39
许伯强，李和明，孙丽玲等，笼型异步电动机转子断条综合在线检测方法，华北电力大学学报， 2000，27(4)：23~28
许伯强，李和明，孙丽玲，基于小波包分析的笼型异步电动机转子断条在线
检测方法，中小型电机，2001，28(6)：47~51
许伯强，李和明，孙丽玲，小波分析应用于笼型异步电动机转子断条在线检测初探，中国电机工程学报，2001，21(11)：24~28
任震，何建军，黄雯莹等，基于小波包算法的电机故障信号的压缩和重构，中国电机工程学报，2001，21(1)：25~29
黄群古，任震，黄雯莹等，梯形小波变换及其在鼠笼电动机转子故障分析中的应用，中国电机工程学报，2001，21(6)：29~32
曹志彤，陈宏平，何国光，基于小波重构的异步电动机故障诊断，电工技术学报，2002，17(2)：80~83
K.Abbaszadeh, J.Milimonfared, M.Haji, etc. Broken Bar Detection in Induction Motor via Wavelet Transformation. IECON’01: The 27th Annual Conference of the IEEE Industrial Electronics Society. 2001, 95~99
A.J. Marques Cardoso, S.M.A. Cruz, J.F.S. Carvalho, etc. Rotor Cage Fault Diagnosis in Three-phase Induction Motors, by Park's Vector Approach. Conference Record of the 1995 IEEE, 1995, vol.1, 642~646
Hamid Nejjari, Mohamed EI Hachemi Benbouzid. Monitoring and Diagnosis of Induction Motors Electrical Faults Using a Current Park's Vector Pattern Learning Approach. IEEE Transactions on Industry Applications. Vol. 36, No. 3, 2000, 730~735
Filippetti F, Martelli M, Franceschini G, et al. Development if Expert System knowledge Base to Online Diagnosis of Rotor Electrical Faults of Induction Motors. Proc. IEEE-IAS ANNU. Meeting, Houston, TX, Oct. 1992, 92~99.
Tak Son Cheang, Linzheng Zhang. A New Prototype of Diagnosis System of Inner-faults for Three-phase Induction Motors Developed by Expert System. Proceedings of the Fifth International Conference,?Vol.1,? 2001, vol.1, 312~ 316
Zhongming Ye, Bin Wu, A.R.Sadeghian. Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition. APEC 2001. Sixteenth Annual IEEE ,?Vol. 2 , March 2001, 1022~1029
Zhongming Ye, Bin Wu, Navid Zargati, “Online Mechanical Fault Diagnosis of Induction Motor by Wavelet Artificial Network Using Stator Current ” 26th Annual Confjerence of the IEEE , Vol. 2 , 2000, 1183~1188
K.abbaszadeh, J. milimonfared, M. Haji, H.A.Toliyat “Broken Bar Detection in Induction Motor via Wavelet Transformation ” IEEE conference record 2001, 95~99
Tan, Woei Wan, Huo Hong. An On-line Neurofuzzy Approach for Detecting Faults in Induction Motors. Electric Machines and Drives Conference, 2001, 878~883
Masoud Haji, Hamid A. Toliyat. Pattern Recognition——A Technique for Induction Machines Rotor Broken Bar Detection. IEEE, IEEE Transactions on Energy Conversion, 2001, 312~317
H.J.威佛, 离散和连续傅里叶分析理论，北京邮电学院出版社，1991
郑治真，沈萍，杨选辉等，小波变换及其Matlat工具的应用，地震出版社，2001
彭玉华，小波变换与工程应用，科学出版社，1999
杨福生，小波变换的工程分析与应用，科学出版社，1999
焦李成，神经网络的应用与实现，西安电子科技大学出版社, 1993
赵振宇, 徐用懋，模糊理论和神经网络的基础与应用，清华大学出版社, 1996
袁曾任，人工神经网络及其应用，清华大学出版社, 1999
卢献国，赵勇，鼠笼式异步电动机断条状况和断条检测仪的研制，河南电力，1995, 2, 28~31
邱阿瑞，张龙照，鼠笼式异步电动机转子导条及瑞环故障时的稳态运行分析， 电工技术学报，1987(3)
邱阿瑞，张龙照，异步电动机转子故障点分布对故障检测的影响，电工技术杂志，1989(6)，14~16
马宏忠，胡虞生，黄允凯等，感应电机转子断条根数影响分析，河海大学学报（自然科学版），2003，31(5)，573-576
J. F. Bangura, N. A. Demerdash, Diagnosis and Characterization of Effects of Broken Bars and Connectors in Squirrel-Cage Induction Motors by a Time-Stepping coupled Finite Element-State Space Modeling Approach, IEEE Trans. on Energy Conversion, 1999, vol.14(4), 1167-1176
A.Bellini, F. Filippetti, G. Fracesschini, etc. ENEL’s Experience with On-line Diagnosis of Large Induction Motors Cage Failures. Conference Record of the 2000 IEEE ,?vol.1, 492~498
刘进明，应怀樵，FFT谱连续细化分析的富里叶变换法，振动工程学报.1995，8(2)：162~166
何正嘉，机械设备非平稳信号的故障诊断原理及应用，高等教育出版社, 2001