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作 者:张雅晖 杨凯 杨帆[1,2] ZHANG Yahui;YANG Kai;YANG Fan(State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology,Wuhan 430074,China.;Engineering Research Center of Novel Electrical Machines and Special Electromagnetic Equipments,Ministry of Education,Huazhong University of Science and Technology,Wuhan 430074,China)
机构地区:[1]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,武汉430074 [2]华中科技大学新型电机与特种电磁装备教育部工程研究中心,武汉430074
出 处:《电测与仪表》2024年第4期161-168,共8页Electrical Measurement & Instrumentation
基 金:国家重点研发计划项目(2018YFB0904800);国家自然科学基金资助项目(51677078)。
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。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.Firstly,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 proposed method are verified,and it is of great significance for the accurate monitoring of motor running state.
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