基于 NA-MEMD 互信息和 SVM 的三相异步电机转子故障诊断技术研究

Hui Ali, Yu Jie, Weiqiang Lu
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引用次数: 0

摘要

针对三相异步电动机故障产生的非线性、非稳态电流信号在自适应分解过程中存在模态混叠问题,以及当发生早期转子线棒断裂和气隙偏心故障时,单一传感器采集的信号所包含的故障特征无法准确、全面地提取和表征等问题,提出了一种基于噪声辅助多变量经验模态分解(NA-MEMD)和互信息的三相异步电动机故障诊断方法。首先,利用 NA-MEMD 算法对异步电机的三相定子电流信号进行分解,得到多尺度本征模态函数(IMF)。然后,使用相关算法筛选出包含有用信息的 IMF。最后,利用支持向量机(SVM)识别三相异步电机的转子断条和气隙偏心故障。实验结果表明,与传统的经验模式分解(EMD)和集合经验模式分解(EEMD)方法相比,NA-MEMD 方法具有更高的识别率。
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Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM
Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.
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