【摘要】 提出了一种基于小波包分析(WPA)和Elman神经网络的异步电机转子断条故障诊断方法.针对异步电机转子断条故障时定子电流出现的边频分量进行小波包分析,提取动态条件下各频带能量作为故障特征向量,削弱了负载变化及噪声对诊断准确性的影响.采用Elman神经网络对故障进行识别,并对Elman网络进行改进,在关联层增加了自反馈增益因子,提高了网络性能.以频带能量作为Elman神经网络识别故障的特征向量,建立从特征向量到电机转子断条故障之间的映射.试验结果表明:基于小波包分析提取的故障特征明显,由WPA和Elman神经网络构成的诊断系统,能有效地识别出转子断条故障,故障诊断准确率高.更多还原

【Abstract】 A fault diagnosis method was presented for motor rotor broken bar fault based on wavelet package analysis(WPS) and Elman neural network.The sideband frequency current,which reflects the broken bar fault,was analyzed with the technology of wavelet package decomposition.The frequency segment power under operating states was abstracted as fault characteristic vectors,which weaken the influences of variable load and noise.Elman neural network was adopted to identify the broken bar fault.To improve its performance,Elman neural network was modified by adding a self-feedback gain factor in the context nodes.Energy of various frequency bands acting as the fault characteristic vector was input into the modified Elman neural network to realize the mapping between the feature vector and the fault mode.Experiment results show that the fault characteristic vectors abstracted by WPA are evident.The diagnosis system based on wavelet package and Elman neural network could identify motor rotor broken bar fault efficiently and accurately.更多还原