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基于极值点奇异值降噪与关联维数的电机转子不平衡故障识别
袁壮,段礼祥,王金江
中国石油大学(北京)机械与储运工程学院,北京 102249
Motor rotor imbalance fault recognition based on extreme point SVD de-noising and correlation dimension
YUAN Zhuang, DUAN Lixiang, WANG Jinjiang
College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102249, China

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摘要  提出一种基于极值点奇异值降噪与关联维数分布情况相结合的电机转子不平衡故障识别方法。首先,对不同时期采集的电机振动信号进行基于极值点的奇异值降噪,避免噪声所导致的关联维数不收敛。然后,通过复相关函数法确定延迟时间,并采用G-P算法计算关联维数。最后,对多组振动信号的关联维数进行比较,识别电机转子不平衡故障。利用西部某油田的一起电机转子不平衡故障对上述方法进行了测试,结果表明,降噪信号的关联维数能够有效识别电机转子不平衡,且同常用的自相关法相比,本文采用复相关函数法计算得到的关联维数具有更好的分辨性,更适合于电机故障识别。
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关键词 : 电机; 转子不平衡; 故障特征提取; 关联维数
Abstract

  To improve the recognition of motor rotor imbalance faults, this paper presents a method based on the extreme point singular value decomposition (SVD) and the correlation dimension distribution. First, the motor vibration signals collected in different periods are de-noised using the extreme point singular value decomposition to avoid the misconvergence of correlation dimension caused by noise. Then the multiple correlation function replaces the traditional correlation function to determine the delay time, and a genetic programming (G-P) algorithm is utilized to calculate the correlation dimension. Finally, the motor rotor imbalance can be recognized by comparing the correlation dimension of multiple sets of vibration signals. We applied the developed method to a motor rotor imbalance in an oilfield in western China, and the results demonstrate that the correlation dimensions of de-noised signals can effectively identify the motor rotor imbalance. Compared with the traditional method, the correlation dimensions obtained by the developed method are more differentiable and more suitable for motor fault identification.

Key words: motor ; rotor imbalance ; fault feature extraction ; correlation dimension
收稿日期: 2016-11-15     
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通讯作者: jwang@cpu.edu.cn
引用本文:   
袁壮,段礼祥,王金江. 基于极值点奇异值降噪与关联维数的电机转子不平衡故障识别[J]. 石油科学通报, 2016, 1(3): 425-433. YUAN Zhuang, DUAN Lixiang, WANG Jinjiang. Motor rotor imbalance fault recognition based on extreme point SVD de-noising and correlation dimension. 石油科学通报, 2016, 1(3): 425-433.
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