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Induction motor fault detection using the fast orthogonal search algorithm.

机译:使用快速正交搜索算法的感应电动机故障检测。

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摘要

The condition monitoring of induction motors is very important to ensure the continuation of many industrial processes. The detection of faults at their inception is key to ensuring safe operation, maintaining maximum efficiency, and minimizing costs associated with downtimes. There are a number of different faults that can afflict induction motors, however the detection of rotor faults is essential to achieving fault tolerant drive systems. Current signature analysis (CSA) has been established as the most effective means of detecting motor faults. CSA is based on extracting known fault harmonics from the stator current of the motor. Previous systems use the fast Fourier transform (FFT) to determine the spectral content of the motor current.;A fine resolution is particularly important when the motor is operating under light-load conditions. This is because for small slip values, the fault signatures will be close to the fundamental frequency. Since the fundamental is much larger in magnitude, its spectra leakage may cover up the smaller fault harmonics. The length of the sampling time required by the FFT when the motor is under light-load conditions is often not possible to achieve.;This thesis uses the fast orthogonal search (FOS) algorithm for the application of rotor fault detection. The FOS algorithm has previously been shown capable of achieving a finer resolution than the FFT for the same record length. The results of this thesis will demonstrate that FOS is able to accurately detect rotor fault signatures using one-eighth the sampling time required by the FFT. Therefore condition monitoring is now possible for motors where lengthy periods of steady-state are unavailable, but a high degree of resolution is necessary. It will also be demonstrated that by properly selecting candidate terms, the required sampling time can be further reduced.;The FFT is inadequate under many conditions because its resolution is directly related to the length of the sampling time. To achieve a fine resolution, a long sampling time is required. For the FFT to work correctly it is necessary that the motor be in steady-state during the sampling time. Due to the non-stationary nature of motors, long periods of steady-state are often unavailable, and a compromise must be made between resolution and the length of the sampling time.
机译:感应电动机的状态监视对于确保许多工业过程的连续性非常重要。从一开始就对故障进行检测是确保安全运行,保持最高效率并最大程度减少与停机时间相关的成本的关键。可能会影响感应电动机的故障有很多,但是,检测转子故障对于实现容错驱动系统至关重要。电流特征分析(CSA)已被确定为检测电动机故障的最有效方法。 CSA基于从电动机的定子电流中提取已知的故障谐波。以前的系统使用快速傅里叶变换(FFT)来确定电动机电流的频谱含量。当电动机在轻载条件下运行时,精细的分辨率尤为重要。这是因为对于较小的滑差值,故障信号将接近基本频率。由于基频的幅度要大得多,因此其频谱泄漏可能会覆盖较小的故障谐波。电机在轻载条件下,FFT所需的采样时间通常是无法达到的。;本文采用快速正交搜索(FOS)算法在转子故障检测中的应用。对于相同的记录长度,以前已经证明FOS算法能够实现比FFT更好的分辨率。本文的结果将证明,FOS能够使用FFT所需的八分之一的采样时间来准确检测转子故障特征。因此,对于长时间无法获得稳定状态的电机,现在可以进行状态监控,但是必须有很高的分辨率。还将证明,通过适当选择候选项,可以进一步减少所需的采样时间。在许多情况下,FFT都是不充分的,因为它的分辨率与采样时间的长短直接相关。为了获得较高的分辨率,需要较长的采样时间。为了使FFT正常工作,必须在采样时间内使电机处于稳定状态。由于电动机的非平稳特性,经常无法获得长时间的稳态,因此必须在分辨率和采样时间的长度之间做出折衷。

著录项

  • 作者

    King, Gregory John.;

  • 作者单位

    Royal Military College of Canada (Canada).;

  • 授予单位 Royal Military College of Canada (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.A.Sc.
  • 年度 2010
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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