首页> 外文OA文献 >Current-, force-, and vibration-based techniques for induction motor condition monitoring
【2h】

Current-, force-, and vibration-based techniques for induction motor condition monitoring

机译:基于电流,力和振动的技术用于感应电动机状态监测

摘要

The aim of this research was to discover the best indicators of induction motor faults, as well as suitable techniques for monitoring the condition of induction motors. Numerical magnetic field analysis was used with the objective of generating reliable virtual data to be analysed with modern signal processing and soft-computing techniques. In the first part of the research, a fuzzy system, based on the amplitudes of the motor current, was implemented for online detection of stator faults. Later on, from the simulation studies and using support vector machine (SVM), the electromagnetic force was shown to be the most reliable indicator of motor faults. Discrete wavelet transform (DWT) was applied to the stator current during the start-up transient, showing how the evolution of some frequency components allows the identification and discrimination of induction motor faults. Predictive filtering was applied to separate the harmonic components from the main current signal.The second part of the research was devoted to the development of a mechanical model to study the effects of electromagnetic force on the vibration pattern when the motor is working under fault conditions. The third part of this work, following the indications given by the second part, is concerned with a method that allows the prediction of the effect of the electromechanical faults in the force distribution and vibration pattern of the induction machines. The FEM computations show the existence of low-frequency and low-order force distributions acting on the stator of the electrical machine when it is working under an electrical fault. It is shown that these force components are able to produce forced vibration in the stator of the machine. This is corroborated by vibration measurements. These low-frequency components could constitute the primary indicator in a condition monitoring system.During the research, extensive measurements of current, flux and vibration were carried out in order to supply data for the research group. Various intentional faults, such as broken rotor bars, broken end ring, inter-turn short circuit, bearing and eccentricity failures, were created. A real dynamic eccentricity was also created. Moreover, different supply sources were used. The measurements supported the analytical and numerical results.
机译:这项研究的目的是发现感应电动机故障的最佳指标,以及监测感应电动机状态的合适技术。使用数字磁场分析的目的是生成可靠的虚拟数据,并通过现代信号处理和软计算技术对其进行分析。在研究的第一部分中,基于电动机电流幅度的模糊系统被实现用于定子故障的在线检测。后来,通过仿真研究并使用支持向量机(SVM),电磁力被证明是电机故障的最可靠指标。在启动瞬态过程中,将离散小波变换(DWT)应用于定子电流,这表明某些频率分量的演变如何允许识别和区分感应电动机故障。应用预测滤波从主电流信号中分离出谐波分量。研究的第二部分致力于机械模型的研究,研究电动机在故障条件下工作时电磁力对振动模式的影响。根据第二部分给出的指示,这项工作的第三部分涉及一种方法,该方法可以预测机电故障对感应电机的力分布和振动模式的影响。 FEM计算表明,当电机在电气故障下工作时,低频和低阶力分布会作用在电机的定子上。结果表明,这些力分量能够在电机的定子中产生强制振动。振动测量证实了这一点。这些低频成分可能构成状态监测系统的主要指标。在研究过程中,对电流,通量和振动进行了广泛的测量,以便为研究小组提供数据。造成了各种故意的故障,例如转子条断裂,端环断裂,匝间短路,轴承和偏心故障。还创建了真正的动态偏心率。此外,使用了不同的供应来源。测量结果支持分析和数值结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号