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首页> 外文期刊>Shock and vibration >Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm
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Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

机译：基于互信息算法的多尺度熵和支持向量机的异步电动机故障诊断系统

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摘要

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.
机译：提出了一种有效的异步电动机故障诊断方法，结合熵特征提取，互信息和支持向量机，提高了异步电动机的可靠性。样本熵和多尺度熵用于从电机振动信号中提取所需的熵特征。样本熵用于估计原始时间序列的复杂性，而多尺度熵用于测量不同尺度下的时间序列的复杂性。熵特征是直接从非线性，非平稳感应电动机的振动信号中提取的，然后使用互信息进行分类，从而根据特征的重要性和与故障的相关性对特征向量中的元素进行排序。从特征向量中选择前五个最重要的特征，并使用支持向量机进行分类。然后，将所提出的方法用于分析从电机故障模拟器测试台获得的振动数据。分类结果表明，该方法能够以合理的良好精度有效诊断各种电机故障。还表明，所提出的方法可以仅使用振动数据就可以为各种感应电动机故障提供有效且准确的故障诊断。

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  • 来源
    《Shock and vibration》 |2016年第3期|5836717.1-5836717.12|共12页
  • 作者

    Pan Shuang; Han Tian; Tan Andy C. C.; Lin Tian Ran;

  • 作者单位

    Univ Sci & Technol Beijing, Sch Mech Engn, 30 Xueyuan Rd, Beijing 100083, Peoples R China;

    Univ Sci & Technol Beijing, Sch Mech Engn, 30 Xueyuan Rd, Beijing 100083, Peoples R China;

    Univ Tunku Abdul Rahman, Fac Engn, Sungai Long Campus, Kajang, Malaysia;

    Qingdao Technol Univ, Sch Mech Engn, 777 Jialingjian Rd, Qingdao 266520, Peoples R China;

  • 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
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