摘要
为实现永磁同步电机的故障类别的诊断,采用小波函数根据不同频段进行故障特征提取,进行归一化数据样本处理,以剔除奇异样本。利用小波函数构成SOM(Self Organizing Map)的领域函数,形成次兴奋神经元进行权值更新,以避免SOM的局部最优。采用实验提取的故障数据作为SOM神经网络的输入样本进行网络训练,从而得出产生特定故障时所激发的相应神经元索引。实验结果验证了该方法的可行性和实用性。
In order to achieve the diagnosis of fault categories of permanent magnet synchronous motor,fault feature is extracted by using wavelet function according to the different frequency band,then normalized date sample as the SOM（Self Organizing Map） network input,the SOM field function is constructed with the wavelet function and the second excited neurons are formed to update weights,so the local optimization of SOM is avoided.The fault data extracted is regarded as the input samples of SOM neural networks in order to train the network,and appropriate neuron index stimulated when a specific fault occurs is obtained.The feasibility has been demonstrated by simulation results.
出处
《吉林大学学报（信息科学版）》
CAS
2012年第6期555-560,共6页
Journal of Jilin University（Information Science Edition）
基金
吉林省重大科技攻关基金资助项目(10ZDGG002)
关键词
神经网络
故障诊断
神经元索引
永磁同步电机
neural network
fault diagnosis
neuron index
permanent magnet synchronous motor（PMSM）
