Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
复制标题
数据驱动的永磁同步电机早期故障诊断方法
DOI：
10.1016/j.eswa.2021.115000
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发表时间：
2021-04-14
影响因子：
8.5
通讯作者：
Liu, Yonghong
中科院分区：
文献类型：
--
作者：
Cai, Baoping;Hao, Keke;Liu, Yonghong
永磁同步电动机是现代工业系统中常用的核心动力元件之一。早期故障诊断可以避免重大事故，并提前计划维修。但早期断层特征较弱，征兆不明显。同时，故障信号往往被噪声淹没。因此，对于早期故障的故障诊断是困难的，并且诊断精度通常较低。提出了一种基于贝叶斯网络的数据驱动的永磁同步电动机早期故障诊断方法。采用小波阈值去噪和最小熵反褶积方法提高了信噪比。采用互补集成经验模态分解方法提取信号特征值，并应用贝叶斯网络识别早期、中期和永久性故障。采用Tyco ST8N80P100V22E中型永磁同步电动机进行的实验数据来训练故障诊断模型，并验证所提出的故障诊断方法。结果表明，声发射信号对早期故障的识别准确率在90%以上，高于振动信号。研究了载荷对诊断精度的影响，结果表明，在不同载荷下，声发射信号的诊断精度高于振动信号。
Permanent magnet synchronous motor (PMSM) is one of the common core power components in modern industrial systems. Early fault diagnosis can avoid major accidents and plan maintenance in advance. However, the features of early faults are weak, and the symptoms are not obvious. Meanwhile, the fault signal is often overwhelmed by noise. Accordingly, fault diagnosis for early faults is difficult, and the diagnostic accuracy is generally low. A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data. The wavelet threshold denoising and minimum entropy deconvolution methods are used to improve the signal-to-noise ratio. The complementary ensemble empirical mode decomposition method is used to extract signal eigenvalues, and Bayesian networks are applied to identify the early, middle, and permanent faults. Experimental data carried out with Tyco ST8N80P100V22E medium PMSM are used to train the fault diagnostic model and validate the proposed fault diagnostic methodology. Result shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal. The influence of load on diagnostic accuracy is also investigated, and it indicates that the accuracy with acoustic emission signal is higher than that with vibration signal under different loads.