...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Shock and vibration >Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines
【24h】

Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines

机译：振动和电流签名的融合，对感应机器的故障诊断

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

Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal-based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time-frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision-making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision-level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis.
机译：感应机器广泛用于行业，作为主要的执行器之一，如水泵，空气压缩机和风扇。有必要监控和诊断这些感应电机，以防止由过早失效引起的任何突然关闭。在过去的几十年里提出了许多用于诊断感应机器的故障检测和隔离技术。在这些技术中，电机电流特征分析（MCSA）和振动分析是最常见的基于信号的状态监测方法中的两个。它们通常是独立采用的，但每个方法都有其优势和劣势。该研究提出了一种系统方法，用于集成从振动和电流测量的信息。我们应用小波分组分解以提取振动和电流测量的时频特征，并使用支持向量机作为初始决策的分类器。鉴定了显着的特征，比较了几种分类器的性能。结果，提出了基于Sugeno模糊积分的决策级别传感器融合，以集成振动和电流信息以提高诊断的准确性。

著录项

  • 来源
    《Shock and vibration》 |2019年第9期|7176482.1-7176482.17|共17页
  • 作者

    Liu Meng-Kun; Minh-Quang Tran; Weng Peng-Yi;

  • 作者单位

    Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10607 Taiwan|Natl Taiwan Univ Sci & Technol Ctr Cyber Phys Syst Innovat Taipei 10607 Taiwan;

    Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10607 Taiwan;

    Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10607 Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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