...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Sensors Journal, IEEE >A New Entropy Bi-Cepstrum Based-Method for DC Motor Brush Abnormality Recognition
【24h】

A New Entropy Bi-Cepstrum Based-Method for DC Motor Brush Abnormality Recognition

机译：基于熵双倒谱的直流电动机电刷异常识别新方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abnormal arcs in dc motors are often associated with various potential failures or operational defaults. Although they may not directly led to motor breakdown, they can be causes to faults, further damages, and fire hazard. There can be arcs between brushes and rotors when motor running under normal condition, known as normal arcs. However, abnormal arcs, which are difficult to be visually distinguished from normal arcs, occur when there is loosen or contamination of brushes. Therefore, detecting the existence and identifying the type of unusual arcs can be applied as an effective method for brush condition monitoring. This paper presents a detection strategy for abnormality in brush based on the online electromagnetic field (EMF) analysis with advanced feature extraction techniques. The techniques aim at finding the unusual changes in EMF to identify abnormal arc among normal ones. Entropy bi-cepstrum applied as feature extraction method is an inverse spectrum of cumulant. Bi-cepstrum is insensitive to noise, and entropy reflects the complexity of the target signal. In the experiment, three typical types of unusual arcs occurring in brush area are successfully identified, and the result shows the accuracy as high as 91.4%. The new strategy with algorithms can serve as a very useful tool for abnormality recognition of the motor brush.
机译：直流电动机中的异常电弧通常与各种潜在故障或运行默认值相关。尽管它们可能不会直接导致电动机故障，但可能会导致故障，进一步的损坏和火灾隐患。当电动机在正常条件下运行时，电刷和转子之间可能会产生电弧，称为正常电弧。但是，当刷子松动或污染时，会发生难以从视觉上区别于正常电弧的异常电弧。因此，检测电刷的存在和识别异常电弧的类型可以作为一种有效的电刷状态监测方法。本文提出了一种基于在线电磁场（EMF）分析和先进特征提取技术的刷子异常检测策略。该技术旨在发现EMF中的异常变化，以识别正常电弧中的异常电弧。熵双倒频谱用作特征提取方法是累积量的逆谱。双倒谱对噪声不敏感，并且熵反映了目标信号的复杂性。在实验中，成功地识别了三种典型类型的在刷子区域发生的异常电弧，结果表明其准确性高达91.4％。带有算法的新策略可以作为非常有用的工具来识别电动机电刷。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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