| 基于小波包能量分析和信号融合的异步电机转子故障诊断 |
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| 引用本文： | 张雅晖,杨凯,杨帆.基于小波包能量分析和信号融合的异步电机转子故障诊断[J].电测与仪表,2024,61(4):161-168. |
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| 作者姓名： | 张雅晖 杨凯 杨帆 |
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| 作者单位： | 华中科技大学 电气与电子工程学院 强电磁工程与新技术国家重点实验室,华中科技大学 电气与电子工程学院 强电磁工程与新技术国家重点实验室,华中科技大学 电气与电子工程学院 强电磁工程与新技术国家重点实验室 |
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| 基金项目： | 国家重点研发计划资助项目（2018YFB0904800）,国家自然科学基金资助项目（ 51677078，51477060），湖北省自然科学基金资助项目（2019CFB812） |
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| 摘 要： | 为提高异步电机转子故障诊断的可靠性，文中介绍了一种基于小波包能量分析和信号融合的异步电机转子故障诊断方法。采用定子电流信号和振动信号的频谱特征融合作为转子断条以及气隙偏心故障的诊断依据，首先对信号进行小波包分解，获得不同小波包频带节点下对应的能量分布，并与正常电机信号进行比较，进而对能量异常的信号频段进行小波包节点重构，最后通过快速傅里叶变换识别故障特征频率，诊断电机故障是否发生。通过仿真分析，验证了该方法的有效性和实用性，对于电机运行状态的准确监测具有重要意义。
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| 关 键 词： | 故障诊断 异步电机 转子断条 气隙偏心 小波包分析 信号融合 |
| 收稿时间： | 2021/2/4 0:00:00 |
| 修稿时间： | 2021/2/18 0:00:00 |
Rotor fault diagnosis of induction motor based on wavelet packet energy analysis and signal fusion |
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| Zhang Yahui,Yang Kai and Yang Fan.Rotor fault diagnosis of induction motor based on wavelet packet energy analysis and signal fusion[J].Electrical Measurement & Instrumentation,2024,61(4):161-168. |
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| Authors: | Zhang Yahui Yang Kai and Yang Fan |
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| Affiliation: | State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology,State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology,State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology |
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| Abstract: | Aiming to improve the reliability of rotor fault diagnosis for induction motor, this paper proposed a method of rotor fault diagnosis for induction motor based on wavelet packet energy analysis and signal fusion. The frequency spectrum fusion of stator current signal and vibration signal is used as the diagnosis basis of rotor broken bar fault and air gap eccentric fault. First, the signal is decomposed by wavelet packet to obtain the corresponding energy distribution under different wavelet packet frequency band nodes and compare it with the normal motor signal. Then, the wavelet packet node reconstruction is carried out for the signal frequency band with abnormal energy. Finally, the fault characteristic frequency is identified by the fast Fourier transform to diagnose whether the motor fault occurs. Through simulation analysis, the effectiveness and practicability of the method are verified, and it is of great significance for the accurate monitoring of motor running state. |
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| Keywords: | fault diagnosis induction motor rotor broken bar fault air gap eccentricity fault wavelet packet analysis signal fusion |
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