船舶感应电机轴承故障诊断系统的研究
摘要
感应电动机因其可靠性高、结构简单、成本低，故而在船舶上得到了广泛的应用。一旦电机发生故障，就可能导致动力系统和电力系统的服务中止，从而威胁船舶航行的安全。其中轴承故障是电机故障中发生概率最高的，因此本文针对船舶感应电动机轴承故障的诊断展开研究。
确定轴承故障的诊断方法，研究如何提取轴承故障特征，以及组建船舶感应电机轴承故障在线诊断系统是本文研究的主要内容。
分析现有的感应电机故障诊断方法，针对船舶特殊的工作环境，以及感应电机轴承故障的特点，本文采用定子电流特征分析法来诊断船舶感应电机的轴承故障。定子电流特征分析法将轴承的故障特征与定子电流的特征频率之间建立了函数关系，只要正确提取定子电流中的轴承故障特征，就可以实现船舶感应电机轴承故障的在线诊断。
船舶感应电机轴承故障的特征信号较弱，可能会湮没在噪声中提取不出来，通过对FFT和功率谱估计方法的分析比较，本文选择采用改进的周期图法Welch法提取感应电机的轴承故障特征。为了更好的提取轴承故障特征，针对Welch周期图法的窗函数、分段数目以及采样点数的选择做了仿真研究。
在诊断系统的硬件部分，本文采用霍尔元件采集定子电流信号，选取信号处理器TMS320LF2407A进行信号处理，用PROFIBUS-DP从站构成分布式故障诊断系统。
在诊断系统的软件部分，对信号进行了A／D转换，设计了Welch周期图法的实现程序，完成了PROFIBUS-DP从站的软件设计，编写了从站的GSD文件，实现了PROFIBUS总线网络的组网。
At present, the induction motor has been applied widely on vessel, owing to its high reliability, simple structure and low cost. Once motor has faults, propelling system and power system stop possibly. Then it will threat the security of marine navigation. Bearing fault has the highest probability in all faults of motor. So this paper researches the bearing fault diagnosis system for marine induction motor.
The main content of this paper is confirming the diagnostic method of bearing fault, researching how to pick up the character of bearing fault and making up of the on-line diagnosis system of bearing fault for marine induction motor.
Through analyzing existing method of fault diagnosis, and considering the especial operation condition on vessel and the character of bearing fault for induction motor, this paper chooses Motor Current Signature Analysis (MCSA) to diagnose bearing fault for marine induction motor. Motor Current Signature Analysis establishes function between the character of bearing fault with the eigenfrequency of motor current. As long as picking up the character of bearing fault in motor current, the bearing fault for marine induction motor can be diagnosed.
The character of bearing fault for marine induction motor is extracted difficultly, because it is very weak and polluted by noise. Comparing FFT with power spectrum estimation, this paper chooses improved periodogram (Welch) to pick up the character of bearing fault for induction motor. For the better effect, it does some simulation analysis to choose window function, subsection number and sampling number.
This paper chooses hall element to measure motor current in the diagnosis system. Digital Signal Processor TMS320LF2407A is used to processing signal. PROFIBUS-DP slave station constitutes distributed fault diagnosis system.
This paper makes A/D conversion for the signal and designs the program of improved periodogram (Welch) in the diagnosis system. Also this paper completes the software design of PROFIBUS-DP slave station, compiles GSD of slave station and builds the field bus network of PROFIBUS.
确定轴承故障的诊断方法，研究如何提取轴承故障特征，以及组建船舶感应电机轴承故障在线诊断系统是本文研究的主要内容。
分析现有的感应电机故障诊断方法，针对船舶特殊的工作环境，以及感应电机轴承故障的特点，本文采用定子电流特征分析法来诊断船舶感应电机的轴承故障。定子电流特征分析法将轴承的故障特征与定子电流的特征频率之间建立了函数关系，只要正确提取定子电流中的轴承故障特征，就可以实现船舶感应电机轴承故障的在线诊断。
船舶感应电机轴承故障的特征信号较弱，可能会湮没在噪声中提取不出来，通过对FFT和功率谱估计方法的分析比较，本文选择采用改进的周期图法Welch法提取感应电机的轴承故障特征。为了更好的提取轴承故障特征，针对Welch周期图法的窗函数、分段数目以及采样点数的选择做了仿真研究。
在诊断系统的硬件部分，本文采用霍尔元件采集定子电流信号，选取信号处理器TMS320LF2407A进行信号处理，用PROFIBUS-DP从站构成分布式故障诊断系统。
在诊断系统的软件部分，对信号进行了A／D转换，设计了Welch周期图法的实现程序，完成了PROFIBUS-DP从站的软件设计，编写了从站的GSD文件，实现了PROFIBUS总线网络的组网。
At present, the induction motor has been applied widely on vessel, owing to its high reliability, simple structure and low cost. Once motor has faults, propelling system and power system stop possibly. Then it will threat the security of marine navigation. Bearing fault has the highest probability in all faults of motor. So this paper researches the bearing fault diagnosis system for marine induction motor.
The main content of this paper is confirming the diagnostic method of bearing fault, researching how to pick up the character of bearing fault and making up of the on-line diagnosis system of bearing fault for marine induction motor.
Through analyzing existing method of fault diagnosis, and considering the especial operation condition on vessel and the character of bearing fault for induction motor, this paper chooses Motor Current Signature Analysis (MCSA) to diagnose bearing fault for marine induction motor. Motor Current Signature Analysis establishes function between the character of bearing fault with the eigenfrequency of motor current. As long as picking up the character of bearing fault in motor current, the bearing fault for marine induction motor can be diagnosed.
The character of bearing fault for marine induction motor is extracted difficultly, because it is very weak and polluted by noise. Comparing FFT with power spectrum estimation, this paper chooses improved periodogram (Welch) to pick up the character of bearing fault for induction motor. For the better effect, it does some simulation analysis to choose window function, subsection number and sampling number.
This paper chooses hall element to measure motor current in the diagnosis system. Digital Signal Processor TMS320LF2407A is used to processing signal. PROFIBUS-DP slave station constitutes distributed fault diagnosis system.
This paper makes A/D conversion for the signal and designs the program of improved periodogram (Welch) in the diagnosis system. Also this paper completes the software design of PROFIBUS-DP slave station, compiles GSD of slave station and builds the field bus network of PROFIBUS.
引文
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[3]Thomson，W．T．，Fenger M．．Case histories of current signature analysis to detect faults in induction motor drives．Electric Machines and Drives Conference．IEMDC’03．IEEE International,Jun．2003．3：1459—1465．
[4]S．H．Chetwani，M．K．Shah，M．Ramamoorty．Online Condition Monitoring of Induction Motors through Signal Processing．ICEMS 2005．Proceedings of the Eighth International Conference on Electrical Machines and Systems，Sept．2005，3：2175—2179．
[5]Arfat Siddique，G．S．Yadava，Bhim Singh．A Review of Stator Fault Monitoring Techniques of Induction Motors．IEEE Transactions On Energy Conversion．Mar．2005，20(1)：106—114．
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[2]Fenger，M．，Lloyd，B．A．，Thomson，W．T．．Development of a tool to detect faults in induction motors via current signature analysis．Cement Industry Technical Conference，May 2003：37—46．
[3]Thomson，W．T．，Fenger M．．Case histories of current signature analysis to detect faults in induction motor drives．Electric Machines and Drives Conference．IEMDC’03．IEEE International,Jun．2003．3：1459—1465．
[4]S．H．Chetwani，M．K．Shah，M．Ramamoorty．Online Condition Monitoring of Induction Motors through Signal Processing．ICEMS 2005．Proceedings of the Eighth International Conference on Electrical Machines and Systems，Sept．2005，3：2175—2179．
[5]Arfat Siddique，G．S．Yadava，Bhim Singh．A Review of Stator Fault Monitoring Techniques of Induction Motors．IEEE Transactions On Energy Conversion．Mar．2005，20(1)：106—114．
[6]雷继尧，等．轴承故障诊断．西安：西安交通大学出版社．1991．
[7]王德志．滚动轴承的诊断与维护．中国铁道出版社．1994．
[8]P．J．Tavner，B．G．Gaydon，D．M．Ward．Monitoring generators and large motors．Proc．Inst．Elect．Eng．B，May 1986，133(3)：169—180．
[9]M．S．N．Said，M．E．H．Benbouzid．H-G diagram based rotor parameters identification for induction motors thermal monitoring．IEEE Trans．Energy Convers．．Mar．2002，15(1)：14—18．
[10]J．Castellon，J．Bouquqrt，J．P．Reboul et al．A new tool to study the aging of the winding insulation of electrical machines．in Annu．Rep．-IEEE Conf．Elect．Insulation Dielectric Phenomena，Minneapolis，MN，Oct．1997，2：447—450．
[11]Y．Takezawa，Y．Itoh，M．Shimodera et al．Development of a portable diagnostic apparatus for coil insulators in low—voltage induction motors．IEEE Trans．Dielect．Elect．Insulation，Apr．1998，5(2)：290—295．
[12]S．P．Verma，R．K．Singal，K．Williams．Vibration behavior of stators of electrical machines，Part Ⅰ：Theoretical study．J．Sound Vib．1987，115：1-12．
[13]R．K．Singal，K．Williams，S．P．Verma．Vibration behavior of stators of electrical machines，Part Ⅱ：Experimental study．J．Sound Vib．1987，115：13—23．
[14]S．C．Chang，R．Yacamini．Experimental study of the vibrational behavior of machine stators．Proc．Inst．Elect．Eng．，Elect．Power Appl．May 1996，143(3)：242—250．
[15]勾轶．基于小波分析的中小型电机故障诊断研究(硕士学位论文)．沈阳：沈阳工业大学．2004．
[16]Bucci．G．，Cecati．C．，Landi，C．．Special measurement system for on-line fault diagnosis of induction motors．20th International Conference on Industrial Electronics Control and Instrumentation，Sept．1994，3：1794-1798．
[17]C．Combastel，S．Lesecq，S．Petropol et al．Model-based and wavelet approaches to induction motor on-line fault detection．Control Engineering Practice．2002，(10)：493—509．
[18]D．Henry，A．Zolghadri，M．Monsion et al．Fault diagnosis in induction machines using the generalized structured singular value．Control Engineering Practice．2002 (10)：587—598．
[19]Parlos，A．G．，Kyusung Kim，Bharadwaj，R．．Detection of induction motor faults—combining signal—based and model—based techniques．Proceedings of the 2002 American Control Conference，May 2002，6：4531—4536．
[20]Sottile，J．，Trutt，F．C．，Kohler，J．L．．Experimental investigation of on—line methods for incipient fault detection．Industry Applications Conference，2000 IEEE，Oct．2000，4：2682-2687．
[21]Alfredo R．Munoz．Using an autoregressive model in the detection of abnormal characteristics of squirrel cage induction motors．Electric Power Systems Research．2000，(55)：73—77．
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