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中文摘要：
研究异步电机安全控制问题,为解决故障诊断和速度问题,提高电机运行效率,减小早期故障损失,提出了一种基于集成神经网络的电机故障诊断方法。方法采用定子电流和转子振动信号作为电机故障诊断的输入信号,应用改进的BP神经网络进行故障识别,分别用两个诊断子网络进行局部故障诊断,再运用神经网络融合算法进行全局决策的融合,从而提高诊断的准确率。仿真研究结果表明,故障诊断模型具有诊断准确率高、诊断速度快等优点,是一种比较实用的故障诊断方法,对电机进行故障监测、预报具有重要的实际意义。
英文摘要：
For the aim of improving motor running efficiency and reducing early fault loss,a fault diagnosis model based on integrated neural network is proposed.The signal of stator current and rotor oscillation are as input signal of diagnose distinction.It identifies fault with improved BP neural network,and applies two diagnosis sub-networks to diagnose local faults.The final conclusion is obtained through fusing the diagnose result of different sub-network.It increases the fault diagnosis accuracy.The simulation results show that the diagnosis method,as a higher accuracy and quicker diagnosis,is a more practical fault diagnosis method.It has important practical significance for motor fault monitoring and forecasting.
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