双余度永磁无刷直流电机匝间短路故障诊断

发布时间：2018-03-14 14:50

本文选题：双余度　切入点：永磁无刷直流电机　出处：《电工技术学报》2014年01期 　论文类型：期刊论文

【摘要】：为了提高系统可靠性,将余度技术引入到电机设计中来,设计了双余度永磁无刷直流电机。针对双余度永磁无刷直流电机绕组细微的匝间短路故障,选择了相电流作为故障分析信号,通过拆分定子槽,改变控制电路的方式,建立了电机的匝间短路故障有限元仿真模型。根据故障信号和小波函数的特点,分别利用Daubechies3和coif5小波函数对故障信号进行了特征提取,结果表明在小波分解高频部分的第2层,信号有明显突变,并由此确定了coif5小波函数进行故障特征检测。采用coif5小波函数对相电流d2分解系数进行了能量特征提取,得到了各相短路时的故障特征向量。采用了小波神经网络进行故障诊断,建立了基于PNN神经网络的故障诊断模型,对故障样本进行了诊断,诊断结果准确可靠,验证了所用方法的有效可行。
[Abstract]:In order to improve the reliability of the system, the redundancy technology is introduced into the design of the motor, and a double redundancy permanent magnet brushless DC motor is designed. The phase current is chosen as the fault analysis signal. By splitting the stator slot and changing the control circuit, the finite element simulation model of the inter-turn short-circuit fault of the motor is established. According to the characteristics of the fault signal and the wavelet function, the finite element simulation model of the inter-turn short-circuit fault of the motor is established. Daubechies3 and coif5 wavelet functions are used to extract the feature of the fault signal respectively. The results show that the signal has obvious mutation in the second layer of the high frequency part of the wavelet decomposition. The coif5 wavelet function is used to detect the fault feature, and the coif5 wavelet function is used to extract the energy feature of the phase current D2 decomposition coefficient. The fault eigenvector of each phase is obtained and the fault diagnosis model based on PNN neural network is established by using wavelet neural network. The fault sample is diagnosed accurately and reliably. The effectiveness of the proposed method is verified.
【作者单位】：西北工业大学自动化学院;
【基金】：陕西省自然科学基金(2013JQ7035) 西安市科技计划(CX12185-2) 航空基金(2013ZC53045)资助项目
【分类号】：TM33

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