基于样本延迟的现有噪声消除方法在异步电动机轴承故障诊断中的发展

Behnam Azizi, M. Shahkarami, Fardin Dalvand, Satar Dalvand, A. Khorsandi
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引用次数: 2

摘要

典型异步电动机定子电流中的主导分量包含了大量与轴承故障无关的信息,对于轴承故障检测来说,这些信息可以被认为是“噪声”。由于信噪比较差,利用电流信号检测轴承早期故障仍然具有挑战性。为了提高这一比率,本文提出了一种基于非高斯残差的电流噪声消除方法,用于感应电机轴承故障诊断。内/外滚道轴承缺陷的实验结果验证了该方法的优点和有效性。此外,实验表明,该方法优于经典的电机电流特征分析方法和最近提出的基于Teager-Kaiser能量算子的电机电流信号分析方法。为了在整个实验结果中验证外/内滚道缺陷的存在,还对振动信号进行了包络分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of current noise cancellation method using sample delaying for bearing fault diagnosis in induction motors
The dominant components in the stator current of a typical induction motor contain a substantial amount of information that is not related to bearing faults and can be considered as "noise" to bearing fault detection. Due largely to poor signal to noise ratio, the detection of incipient faults in bearings by current signal is still challenging. To improve this ratio, this paper proposes a current noise cancellation method based on non-Gaussian residues for bearing fault diagnosis in induction motors. Experimental results for inner/outer raceway bearing defects verify the merits and effectiveness of the proposed method. In addition, it is experimentally shown that the proposed method is superior to the classic motor current signature analysis and the recent proposed method based on Teager-Kaiser energy Operator on motor current signal. To verify the existence of outer/inner raceway defects throughout experimental results, envelope analysis of vibration signal is used as well.
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